Mercurial > hg > map
changeset 38:c2204b18f4a2 tip
End nov big change
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Binary file Documentation Manuals Presentations etc/multiThreshold quickStart instructions.doc has changed
--- a/MAP/MAP1_14.m Thu Oct 06 15:43:20 2011 +0100 +++ b/MAP/MAP1_14.m Mon Nov 28 13:34:28 2011 +0000 @@ -23,35 +23,27 @@ restorePath=path; addpath (['..' filesep 'parameterStore']) -% clear global -regexp OMEParams DRNLParams IHC_cilia_RPParams IHCpreSynapseParams -% clear global -regexp AN_IHCsynapseParams MacGregorParams MacGregorMultiParams -% clear global -regexp dt ANdt savedBFlist saveAN_spikesOrProbability saveMAPparamsName... -% savedInputSignal OMEextEarPressure TMoutput OMEoutput ARattenuation ... -% DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... -% IHCoutput ANprobRateOutput ANoutput savePavailable ANtauCas ... -% CNtauGk CNoutput ICoutput ICmembraneOutput ICfiberTypeRates ... -% MOCattenuation - global OMEParams DRNLParams IHC_cilia_RPParams IHCpreSynapseParams global AN_IHCsynapseParams MacGregorParams MacGregorMultiParams % All of the results of this function are stored as global -global dt ANdt savedBFlist saveAN_spikesOrProbability saveMAPparamsName... - savedInputSignal OMEextEarPressure TMoutput OMEoutput ARattenuation ... - DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... - IHCoutput ANprobRateOutput ANoutput savePavailable ANtauCas ... - CNtauGk CNoutput ICoutput ICmembraneOutput ICfiberTypeRates ... - MOCattenuation +global savedParamChanges savedBFlist saveAN_spikesOrProbability ... + saveMAPparamsName savedInputSignal dt dtSpikes ... + OMEextEarPressure TMoutput OMEoutput DRNLoutput... + IHC_cilia_output IHCrestingCiliaCond IHCrestingV... + IHCoutput ANprobRateOutput ANoutput savePavailable saveNavailable ... + ANtauCas CNtauGk CNoutput ICoutput ICmembraneOutput ICfiberTypeRates... + MOCattenuation ARattenuation % Normally only ICoutput(logical spike matrix) or ANprobRateOutput will be % needed by the user; so the following will suffice -% global ANdt ICoutput ANprobRateOutput +% global dtSpikes ICoutput ANprobRateOutput % Note that sampleRate has not changed from the original function call and % ANprobRateOutput is sampled at this rate % However ANoutput, CNoutput and IC output are stored as logical -% 'spike' matrices using a lower sample rate (see ANdt). +% 'spike' matrices using a lower sample rate (see dtSpikes). % When AN_spikesOrProbability is set to probability, % no spike matrices are computed. @@ -60,7 +52,7 @@ % Efferent control variables are ARattenuation and MOCattenuation % These are scalars between 1 (no attenuation) and 0. -% They are represented with dt=1/sampleRate (not ANdt) +% They are represented with dt=1/sampleRate (not dtSpikes) % They are computed using either AN probability rate output % or IC (spikes) output as approrpriate. % AR is computed using across channel activity @@ -85,6 +77,7 @@ savedBFlist=BFlist; saveAN_spikesOrProbability=AN_spikesOrProbability; saveMAPparamsName=MAPparamsName; +savedParamChanges=paramChanges; dt=1/sampleRate; duration=length(inputSignal)/sampleRate; @@ -94,7 +87,8 @@ segmentTime=dt*(1:segmentLength); % used in debugging plots % all spiking activity is computed using longer epochs -ANspeedUpFactor=AN_IHCsynapseParams.ANspeedUpFactor; % e.g.5 times +ANspeedUpFactor=ceil(sampleRate/AN_IHCsynapseParams.spikesTargetSampleRate); +% ANspeedUpFactor=AN_IHCsynapseParams.ANspeedUpFactor; % e.g.5 times % inputSignal must be row vector [r c]=size(inputSignal); @@ -115,10 +109,10 @@ inputSignal=[inputSignal pad]; [ignore signalLength]=size(inputSignal); -% AN (spikes) is computed at a lower sample rate when spikes required -% so it has a reduced segment length (see 'ANspeeUpFactor' above) +% spiking activity is computed at longer sampling intervals (dtSpikes) +% so it has a smaller number of epochs per segment(see 'ANspeeUpFactor' above) % AN CN and IC all use this sample interval -ANdt=dt*ANspeedUpFactor; +dtSpikes=dt*ANspeedUpFactor; reducedSegmentLength=round(segmentLength/ANspeedUpFactor); reducedSignalLength= round(signalLength/ANspeedUpFactor); @@ -163,7 +157,7 @@ OME_TMdisplacementBndry=[]; % OME high pass (simulates poor low frequency stapes response) -OMEhighPassHighCutOff=OMEParams.OMEstapesLPcutoff; +OMEhighPassHighCutOff=OMEParams.OMEstapesHPcutoff; Nyquist=sampleRate/2; [stapesDisp_b,stapesDisp_a] = butter(1, OMEhighPassHighCutOff/Nyquist, 'high'); % figure(10), freqz(stapesDisp_b, stapesDisp_a) @@ -236,8 +230,8 @@ % DRNLb=DRNLParams.b; DRNLc=DRNLParams.c; linGAIN=DRNLParams.g; -CtBM=10e-9*10^(DRNLParams.CtBMdB/20); -CtS=CtBM/DRNLa; +ctBM=10e-9*10^(DRNLParams.ctBMdB/20); +CtS=ctBM/DRNLa; % % gammatone filter coefficients for linear pathway bw=DRNLParams.linBWs'; @@ -299,6 +293,7 @@ b0=1+ a1; % high pass (i.e. low pass reversed) IHCciliaFilter_b=[a0 a1]; IHCciliaFilter_a=b0; +% i.e. b= [1 dt/tc-1] and a= dt/IHC_cilia_RPParams.tc % figure(9), freqz(IHCciliaFilter_b, IHCciliaFilter_a) IHCciliaBndry=cell(nBFs,1); @@ -405,14 +400,17 @@ % special variables for monitoring synaptic cleft (specialists only) savePavailableSeg=zeros(nANchannels,segmentLength); savePavailable=zeros(nANchannels,signalLength); +% only one stream of available transmitter will be saved +saveNavailableSeg=zeros(1,reducedSegmentLength); +saveNavailable=zeros(1,reducedSignalLength); % spikes % ! ! ! ! ! ! ! ! -lengthAbsRefractory= round(AN_refractory_period/ANdt); +lengthAbsRefractory= round(AN_refractory_period/dtSpikes); -AN_ydt= repmat(AN_IHCsynapseParams.y*ANdt, nANfibers,1); -AN_ldt= repmat(AN_IHCsynapseParams.l*ANdt, nANfibers,1); -AN_xdt= repmat(AN_IHCsynapseParams.x*ANdt, nANfibers,1); -AN_rdt= repmat(AN_IHCsynapseParams.r*ANdt, nANfibers,1); +AN_ydt= repmat(AN_IHCsynapseParams.y*dtSpikes, nANfibers,1); +AN_ldt= repmat(AN_IHCsynapseParams.l*dtSpikes, nANfibers,1); +AN_xdt= repmat(AN_IHCsynapseParams.x*dtSpikes, nANfibers,1); +AN_rdt= repmat(AN_IHCsynapseParams.r*dtSpikes, nANfibers,1); AN_rdt_plus_ldt= AN_rdt + AN_ldt; AN_M= round(AN_IHCsynapseParams.M); @@ -480,12 +478,12 @@ CNdendriteLPfreq= MacGregorMultiParams.dendriteLPfreq; CNcurrentPerSpike=MacGregorMultiParams.currentPerSpike; CNspikeToCurrentTau=1/(2*pi*CNdendriteLPfreq); -t=ANdt:ANdt:5*CNspikeToCurrentTau; +t=dtSpikes:dtSpikes:5*CNspikeToCurrentTau; CNalphaFunction= (1 / ... CNspikeToCurrentTau)*t.*exp(-t /CNspikeToCurrentTau); CNalphaFunction=CNalphaFunction*CNcurrentPerSpike; -% figure(98), plot(t,CNalphaFunction) +% figure(98), plot(t,CNalphaFunction), xlim([0 .020]), xlabel('time (s)'), ylabel('I') % working memory for implementing convolution CNcurrentTemp=... @@ -520,7 +518,7 @@ CN_PSTH=zeros(nICcells ,reducedSegmentLength); % ICspikeWidth=0.00015; % this may need revisiting -% epochsPerSpike=round(ICspikeWidth/ANdt); +% epochsPerSpike=round(ICspikeWidth/dtSpikes); % if epochsPerSpike<1 % error(['MacGregorMulti: sample rate too low to support ' ... % num2str(ICspikeWidth*1e6) ' microsec spikes']); @@ -546,7 +544,7 @@ ICdendriteLPfreq= MacGregorParams.dendriteLPfreq; ICcurrentPerSpike=MacGregorParams.currentPerSpike; ICspikeToCurrentTau=1/(2*pi*ICdendriteLPfreq); -t=ANdt:ANdt:3*ICspikeToCurrentTau; +t=dtSpikes:dtSpikes:3*ICspikeToCurrentTau; IC_CNalphaFunction= (ICcurrentPerSpike / ... ICspikeToCurrentTau)*t.*exp(-t / ICspikeToCurrentTau); % figure(98), plot(t,IC_CNalphaFunction) @@ -661,12 +659,16 @@ nonlinOutput, GTnonlinBdry1{BFno,order}); end + % Nick's compression algorithm - abs_x = abs(nonlinOutput); - y(abs_x<CtS) = DRNLa * nonlinOutput(abs_x<CtS); - y(abs_x>=CtS) = sign(nonlinOutput(abs_x>=CtS)) * DRNLa * CtS .* ... - exp( DRNLc * log( abs_x(abs_x>=CtS)/CtS ) ); - nonlinOutput=y; + abs_x= abs(nonlinOutput); + signs= sign(nonlinOutput); + belowThreshold= abs_x<CtS; + nonlinOutput(belowThreshold)= DRNLa *nonlinOutput(belowThreshold); + aboveThreshold=~belowThreshold; + nonlinOutput(aboveThreshold)= signs(aboveThreshold) *ctBM .* ... + exp(DRNLc *log( DRNLa*abs_x(aboveThreshold)/ctBM )); + % % original broken stick instantaneous compression % holdY=nonlinOutput; @@ -688,24 +690,16 @@ % end % nonlinOutput=y; - % % Boltzmann compression function - % y=(nonlinOutput * DRNLa*100000); - % holdY=y; - % y=abs(y); - % s=10; u=0.0; - % x=1./(1+exp(-(y-u)/s))-0.5; - % nonlinOutput=sign(nonlinOutput).*x/10000; +% if segmentStartPTR==10*segmentLength+1 +% figure(90) +% plot(holdY,'b'), hold on +% plot(nonlinOutput, 'r'), hold off +% ylim([-1e-5 1e-5]) +% pause(1) +% end - % if segmentStartPTR==10*segmentLength+1 - % figure(90) - % plot(holdY,'b'), hold on - % plot(nonlinOutput, 'r'), hold off - % ylim([-1e-5 1e-5]) - % pause(1) - % end - - % second filter removes distortion products +% second filter removes distortion products for order = 1 : GTnonlinOrder [ nonlinOutput GTnonlinBdry2{BFno,order}] = ... filter(GTnonlin_b(BFno,:), GTnonlin_a(BFno,:), ... @@ -839,7 +833,8 @@ % the probability of a spike's occurring in the preceding % refractory window: t= (tnow-refractory period) :dt: tnow % pFired= 1 - II(1-p(t)), - % we need a running account of cumProb=II(1-p(t)) + % we need a running account of cumProb=II(1-p(t)) in order + % not to have to recompute this for each value of t % cumProb(t)= cumProb(t-1)*(1-p(t))/(1-p(t-refracPeriod)) % cumProb(0)=0 % pFired(t)= 1-cumProb(t) @@ -869,8 +864,7 @@ % figure(88), plot(cumANnotFireProb'), title('cumNotFire') % figure(89), plot(ANprobRateOutput'), title('ANprobRateOutput') - %% Estimate efferent effect: 0 < ARattenuation > 1 - % acoustic reflex + %% Estimate acoustic reflex efferent effect: 0 < ARattenuation > 1 [r c]=size(ANrate); if r>nBFs % Only if LSR fibers are computed ARAttSeg=mean(ANrate(1:nBFs,:),1); %LSR channels are 1:nBF @@ -893,31 +887,6 @@ ones(size(rates))* -rateToAttenuationFactorProb; else - % Nick's new code -% for idx=1:nBFs -% [smoothedRates, MOCprobBoundary{idx}] = ... -% filter(MOCfilt_b, MOCfilt_a, rates(idx,:), ... -% MOCprobBoundary{idx}); -% % smoothedRates=smoothedRates-MOCrateThresholdProb; -% % smoothedRates(smoothedRates<0)=0; -% % x = (1- smoothedRates* rateToAttenuationFactorProb); %ORIGINAL -% -% %NEW !!! -% x = -20*log10( max(smoothedRates/MOCrateThresholdProb,1) )*rateToAttenuationFactorProb; %dB attenuation -% x = 10.^(x/20); -% x = max(x,10^(-35/20)); -% % %ALSO - filter at the end - this will stop rapid attack -% % %and slow decay -% % [x, MOCprobBoundary{idx}] = ... -% % filter(MOCfilt_b, MOCfilt_a, x, ... -% % MOCprobBoundary{idx}); -% -% -% MOCattenuation(idx,segmentStartPTR:segmentEndPTR)= ... -% x; -% end - - for idx=1:nBFs [smoothedRates, MOCprobBoundary{idx}] = ... filter(MOCfiltProb_b, MOCfiltProb_a, rates(idx,:), ... @@ -940,7 +909,7 @@ for t = ANspeedUpFactor:ANspeedUpFactor:segmentLength; ANtimeCount=ANtimeCount+1; % convert release rate to probabilities - releaseProb=vesicleReleaseRate(:,t)*ANdt; + releaseProb=vesicleReleaseRate(:,t)*dtSpikes; % releaseProb is the release probability per channel % but each channel has many synapses releaseProb=repmat(releaseProb',nFibersPerChannel,1); @@ -968,6 +937,8 @@ AN_available = AN_available + replenish - ejected ... + reprocessed; + saveNavailableSeg(:,ANtimeCount)=AN_available(end,:); % only last channel + AN_cleft = AN_cleft + ejected - reuptakeandlost; AN_reprocess = AN_reprocess + reuptake - reprocessed; @@ -1003,17 +974,20 @@ % segment debugging % plotInstructions.figureNo=98; - % plotInstructions.displaydt=ANdt; + % plotInstructions.displaydt=dtSpikes; % plotInstructions.numPlots=1; % plotInstructions.subPlotNo=1; % UTIL_plotMatrix(ANspikes, plotInstructions); % and save it. NB, AN is now on 'speedUp' time ANoutput(:, reducedSegmentPTR: shorterSegmentEndPTR)=ANspikes; + % monitor synapse contents (only sometimes used) + saveNavailable(reducedSegmentPTR:reducedSegmentPTR+reducedSegmentLength-1)=... + saveNavailableSeg; %% CN Macgregor first neucleus ------------------------------- - % input is from AN so ANdt is used throughout + % input is from AN so dtSpikes is used throughout % Each CNneuron has a unique set of input fibers selected % at random from the available AN fibers (CNinputfiberLists) @@ -1024,7 +998,7 @@ for idx=1:nCNneuronsPerChannel % determine candidate fibers for this unit fibersUsed=CNinputfiberLists(synapseNo,:); - % ANpsth has a bin width of ANdt + % ANpsth has a bin width of dtSpikes % (just a simple sum across fibers) AN_PSTH(synapseNo,:) = ... sum(ANspikes(fibersUsed,:), 1); @@ -1056,14 +1030,14 @@ if CN_c>0 % variable threshold condition (slow) for t=1:reducedSegmentLength - CNtimeSinceLastSpike=CNtimeSinceLastSpike-ANdt; + CNtimeSinceLastSpike=CNtimeSinceLastSpike-dtSpikes; s=CN_E>CN_Th & CNtimeSinceLastSpike<0 ; CNtimeSinceLastSpike(s)=0.0005; % 0.5 ms for sodium spike dE =(-CN_E/CN_tauM + ... CNcurrentInput(:,t)/CN_cap+(... - CN_Gk/CN_cap).*(CN_Ek-CN_E))*ANdt; - dGk=-CN_Gk*ANdt./tauGk + CN_b*s; - dTh=-(CN_Th-CN_Th0)*ANdt/CN_tauTh + CN_c*s; + CN_Gk/CN_cap).*(CN_Ek-CN_E))*dtSpikes; + dGk=-CN_Gk*dtSpikes./tauGk + CN_b*s; + dTh=-(CN_Th-CN_Th0)*dtSpikes/CN_tauTh + CN_c*s; CN_E=CN_E+dE; CN_Gk=CN_Gk+dGk; CN_Th=CN_Th+dTh; @@ -1076,7 +1050,7 @@ ss=zeros(1,reducedSegmentLength); for t=1:reducedSegmentLength % time of previous spike moves back in time - CNtimeSinceLastSpike=CNtimeSinceLastSpike-ANdt; + CNtimeSinceLastSpike=CNtimeSinceLastSpike-dtSpikes; % action potential if E>threshold % allow time for s to reset between events s=CN_E>CN_Th0 & CNtimeSinceLastSpike<0 ; @@ -1084,8 +1058,8 @@ CNtimeSinceLastSpike(s)=0.0005; % 0.5 ms for sodium spike dE = (-CN_E/CN_tauM + ... CNcurrentInput(:,t)/CN_cap +... - (CN_Gk/CN_cap).*(CN_Ek-CN_E))*ANdt; - dGk=-CN_Gk*ANdt./tauGk +CN_b*s; + (CN_Gk/CN_cap).*(CN_Ek-CN_E))*dtSpikes; + dGk=-CN_Gk*dtSpikes./tauGk +CN_b*s; CN_E=CN_E+dE; CN_Gk=CN_Gk+dGk; E(t)=CN_E(1); @@ -1116,7 +1090,7 @@ % segment debugging % plotInstructions.figureNo=98; - % plotInstructions.displaydt=ANdt; + % plotInstructions.displaydt=dtSpikes; % plotInstructions.numPlots=1; % plotInstructions.subPlotNo=1; % UTIL_plotMatrix(CN_spikes, plotInstructions); @@ -1158,8 +1132,8 @@ for t=1:reducedSegmentLength s=IC_E>IC_Th0; dE = (-IC_E/IC_tauM + inputCurrent(:,t)/IC_cap +... - (IC_Gk/IC_cap).*(IC_Ek-IC_E))*ANdt; - dGk=-IC_Gk*ANdt/IC_tauGk +IC_b*s; + (IC_Gk/IC_cap).*(IC_Ek-IC_E))*dtSpikes; + dGk=-IC_Gk*dtSpikes/IC_tauGk +IC_b*s; IC_E=IC_E+dE; IC_Gk=IC_Gk+dGk; ICmembranePotential(:,t)=IC_E+s.*(IC_Eb-IC_E)+IC_Er; @@ -1169,16 +1143,16 @@ for t=1:reducedSegmentLength dE = (-IC_E/IC_tauM + ... inputCurrent(:,t)/IC_cap + (IC_Gk/IC_cap)... - .*(IC_Ek-IC_E))*ANdt; + .*(IC_Ek-IC_E))*dtSpikes; IC_E=IC_E+dE; s=IC_E>IC_Th; ICmembranePotential(:,t)=IC_E+s.*(IC_Eb-IC_E)+IC_Er; - dGk=-IC_Gk*ANdt/IC_tauGk +IC_b*s; + dGk=-IC_Gk*dtSpikes/IC_tauGk +IC_b*s; IC_Gk=IC_Gk+dGk; % After a spike, the threshold is raised % otherwise it settles to its baseline - dTh=-(IC_Th-Th0)*ANdt/IC_tauTh +s*IC_c; + dTh=-(IC_Th-Th0)*dtSpikes/IC_tauTh +s*IC_c; IC_Th=IC_Th+dTh; end end @@ -1202,7 +1176,7 @@ ICfiberTypeRates(tauCount, ... reducedSegmentPTR:shorterSegmentEndPTR)=... sum(ICspikes(firstCell:lastCell, :))... - /(nCellsPerTau*ANdt); + /(nCellsPerTau*dtSpikes); firstCell=firstCell+nCellsPerTau; lastCell=lastCell+nCellsPerTau; end @@ -1230,19 +1204,19 @@ % figure(4),plot(ICmembraneOutput(2,:)) % estimate efferent effects. - % ARis based on LSR units. LSR channels are 1:nBF + % AR is based on LSR units. LSR channels are 1:nBF if nANfiberTypes>1 % use only if model is multi-fiber - ARAttSeg=mean(ICspikes(1:nBFs,:),1)/ANdt; + ARAttSeg=mean(ICspikes(1:nBFs,:),1)/dtSpikes; [ARAttSeg, ARboundary] = ... filter(ARfilt_b, ARfilt_a, ARAttSeg, ARboundary); -% ARAttSeg(ARAttSeg<0)=0; % prevent negative strengths - % scale up to dt from ANdt +% ARAttSeg(ARAttSeg<0)=0; % prevent negative strengths + % scale up to dt from dtSpikes x= repmat(ARAttSeg, ANspeedUpFactor,1); x= reshape(x,1,segmentLength); ARattenuation(segmentStartPTR:segmentEndPTR)=... (1-ARrateToAttenuationFactor* x); % max 60 dB attenuation - ARattenuation(ARattenuation<0)=0.001; + ARattenuation(ARattenuation<0)=0.01; else % single fiber type; disable AR because no LSR fibers ARattenuation(segmentStartPTR:segmentEndPTR)=... @@ -1253,7 +1227,7 @@ % separate MOC effect for each BF % there is only one unit per channel HSRbegins=nBFs*(nANfiberTypes-1)+1; - rates=ICspikes(HSRbegins:end,:)/ANdt; + rates=ICspikes(HSRbegins:end,:)/dtSpikes; % figure(4),plot(rates(1,:)) for idx=1:nBFs @@ -1262,7 +1236,7 @@ MOCboundary{idx}); % spont 'rates' is zero for IC MOCattSegment(idx,:)=smoothedRates; - % expand timescale back to model dt from ANdt + % expand timescale back to model dt from dtSpikes x= repmat(MOCattSegment(idx,:), ANspeedUpFactor,1); x= reshape(x,1,segmentLength); MOCattenuation(idx,segmentStartPTR:segmentEndPTR)= ... @@ -1276,7 +1250,7 @@ % segment debugging % plotInstructions.figureNo=98; - % plotInstructions.displaydt=ANdt; + % plotInstructions.displaydt=dtSpikes; % plotInstructions.numPlots=1; % plotInstructions.subPlotNo=1; % UTIL_plotMatrix(ICspikes, plotInstructions);
--- a/MAP/MAPrunner.m Thu Oct 06 15:43:20 2011 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,71 +0,0 @@ -function MAPrunner(MAPparamsName, AN_spikesOrProbability, ... - signalCharacteristics, paramChanges, showMapOptions) - -dbstop if error -restorePath=path; -addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... - ['..' filesep 'utilities']) - - -%% #3 pure tone, harmonic sequence or speech file input -signalType= signalCharacteristics.type; -sampleRate= signalCharacteristics.sampleRate; -duration=signalCharacteristics.duration; % seconds -rampDuration=signalCharacteristics.rampDuration; % raised cosine ramp (seconds) -beginSilence=signalCharacteristics.beginSilence; -endSilence=signalCharacteristics.endSilence; -toneFrequency= signalCharacteristics.toneFrequency; % or a pure tone (Hz) -leveldBSPL=signalCharacteristics.leveldBSPL; - -BFlist=-1; - - -%% Generate stimuli - -switch signalType - case 'tones' - % Create pure tone stimulus - dt=1/sampleRate; % seconds - time=dt: dt: duration; - inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); - amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) - inputSignal=amp*inputSignal; - % apply ramps - % catch rampTime error - if rampDuration>0.5*duration, rampDuration=duration/2; end - rampTime=dt:dt:rampDuration; - ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... - ones(1,length(time)-length(rampTime))]; - inputSignal=inputSignal.*ramp; - ramp=fliplr(ramp); - inputSignal=inputSignal.*ramp; - % add silence - intialSilence= zeros(1,round(beginSilence/dt)); - finalSilence= zeros(1,round(endSilence/dt)); - inputSignal= [intialSilence inputSignal finalSilence]; - - case 'file' - %% file input simple or mixed - [inputSignal sampleRate]=wavread(fileName); - dt=1/sampleRate; - inputSignal=inputSignal(:,1); - targetRMS=20e-6*10^(leveldBSPL/20); - rms=(mean(inputSignal.^2))^0.5; - amp=targetRMS/rms; - inputSignal=inputSignal*amp; - intialSilence= zeros(1,round(0.1/dt)); - finalSilence= zeros(1,round(0.2/dt)); - inputSignal= [intialSilence inputSignal' finalSilence]; -end - - -%% run the model - -MAP1_14(inputSignal, sampleRate, BFlist, ... - MAPparamsName, AN_spikesOrProbability, paramChanges); - -%% the model run is now complete. Now display the results -UTIL_showMAP(showMapOptions, paramChanges) - -path(restorePath) -
--- a/MAP/filteredSACF.m Thu Oct 06 15:43:20 2011 +0100 +++ b/MAP/filteredSACF.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,53 +1,52 @@ -function [P, BFlist, sacf, boundaryValue] = ... - filteredSACF(inputSignalMatrix, method, params) -% UTIL_filteredSACF computes within-channel, running autocorrelations (acfs) -% and finds the sum across channels (sacf). -% The SACF is smoothed to give the 'p function' (P). +function [LP_SACF, BFlist, SACF, ACFboundaryValue] = ... + filteredSACF(inputSignalMatrix, dt, BFlist, params) +% UTIL_filteredSACF computes within-channel, running autocorrelations (ACFs) +% and finds the running sum across channels (SACF). +% The SACF is smoothed to give the 'p function' (LP_SACF). +% (Balaguer-Ballestera, E. Denham, S.L. and Meddis, R. (2008)) +% % % INPUT % inputSignalMatrix: a matrix (channel x time) of AN activity -% method.dt: the signal sampling interval in seconds -% method.segmentNo: -% method.nonlinCF -% +% dt: the signal sampling interval in seconds +% BFlist: channel BFs +% % params: a list of parmeters to guide the computation: -% filteredSACFParams.lags: an array of lags to be computed (seconds) -% filteredSACFParams.acfTau: time constant (sec) of the running acf calculations -% if acfTau>1 it is assumed that Wiegrebe'sacf method +% params.lags: an array of lags to be computed (seconds) +% params.acfTau: time constant (sec) of the running ACF +% if acfTau>1 it is assumed that Wiegrebe'SACF method % for calculating tau is to be used (see below) -% filteredSACFParams.Lambda: time constant for smoothing thsacf to make P -% filteredSACFParams.lagsProcedure identifies a strategy for omitting some lags. -% Options are: 'useAllLags', 'omitShortLags', or 'useBernsteinLagWeights' -% filteredSACFParams.usePressnitzer applies lower weights longer lags -% parafilteredSACFParamsms.plotACFs (=1) creates movie of acf matrix (optional) -% +% params.Lambda: smoothing factor for the SACF +% params.lagsProcedure: strategies for omitting some lags. +% (Options: 'useAllLags' or 'omitShortLags') +% params.usePressnitzer applies lower weights longer lags +% params.plotACFs (=1) creates movie of ACF matrix (optional) % % OUTPUT -% P: P function (time x lags), a low pass filtered version of sacf. -% method: updated version of input method (to include lags used) -% sacf: (time x lags) +% LP_SACF: LP_SACF function (time x lags), a low pass filtered version of SACF. +% method: updated version of input 'method' (to include lags used) +% SACF: (time x lags) +% +% Notes: +% ACFboundaryValue refers to segmented evaluation and is currently not +% supported. However the code may be useful later when this function +% is incorporated into MAP1_14. %% -boundaryValue=[]; +global savedInputSignal + +ACFboundaryValue=[]; + [nChannels inputLength]= size(inputSignalMatrix); -% list of BFs must be repeated is many fiber types used -BFlist=method.nonlinCF; -nfibertypes=nChannels/length(BFlist); -BFlist=repmat(BFlist',2,1)'; -dt=method.dt; -% adjust sample rate, if required -if isfield(params,'dt') - [inputSignalMatrix dt]=UTIL_adjustDT(params.dt, method.dt, inputSignalMatrix); - method.dt=dt; -end - -% create acf movie +% create ACF movie if isfield(params, 'plotACFs') && params.plotACFs==1 plotACF=1; + signalTime=dt:dt:dt*length(savedInputSignal); else plotACF=0; % default end +params.plotACFsInterval=round(params.plotACFsInterval/dt); if isfield(params,'lags') lags=params.lags; @@ -64,11 +63,11 @@ else lagsProcedure='useAllLags'; % default end -% disp(['lag procedure= ''' lagsProcedure '''']) + lagWeights=ones(nChannels,nLags); switch lagsProcedure case 'useAllLags' - % no action required lagWeights set above + % no action required lagWeights set above case 'omitShortLags' % remove lags that are short relative to CF allLags=repmat(lags,nChannels,1); @@ -76,14 +75,12 @@ criterionForOmittingLags=1./(params.criterionForOmittingLags*allCFs); idx= allLags < criterionForOmittingLags; % ignore these lags lagWeights(idx)=0; - case 'useBernsteinLagWeights' - lagWeights=BernsteinLags(BFlist, lags)'; otherwise - error ('acf: params.lagProcedure not recognised') + error ('ACF: params.lagProcedure not recognised') end -% Establish matrix of lag time constants +% Establish matrix of lag time constants % these are all the same if tau<1 % if acfTau>1, it is assumed that we are using the Wiegrebe method % and a different decay factor is applied to each lag @@ -92,7 +89,7 @@ if acfTau<1 % all taus are the same acfTaus=repmat(acfTau, 1, nLags); acfDecayFactors=ones(size(lags)).*exp(-dt/acfTau); -else % use Wiegrebe method: tau= 2*lag (for example) +else % use Wiegrebe method: tau= 2*lag (for example) WiegrebeFactor=acfTau; acfTaus=WiegrebeFactor*lags; idx= acfTaus<0.0025; acfTaus(idx)=0.0025; @@ -101,7 +98,7 @@ % make acfDecayFactors into a (channels x lags) matrix for speedy computation acfDecayFactors=repmat(acfDecayFactors,nChannels, 1); -% P function lowpass filter decay (only one value needed) +% LP_SACF function lowpass filter decay (only one value needed) pDecayFactor=exp(-dt/params.lambda); % ACF @@ -112,124 +109,93 @@ end -P=zeros(nLags,inputLength+1); % P must match segment length +1 -sacf=zeros(nLags,inputLength); +LP_SACF=zeros(nLags,inputLength+1); % LP_SACF must match segment length +1 +SACF=zeros(nLags,inputLength); +method=[]; % legacy programming if ~isfield(method,'segmentNumber') || method.segmentNumber==1 - acf=zeros(nChannels,nLags); + ACF=zeros(nChannels,nLags); % create a runup buffer of signal buffer= zeros(nChannels, max(lagPointers)); else - % boundaryValue picks up from a previous calculation - acf=params.boundaryValue{1}; - P(: , 1)=params.boundaryValue{2}; % NB first value is last value of previous segment - buffer=params.boundaryValue{3}; + % ACFboundaryValue picks up from a previous calculation + ACF=params.ACFboundaryValue{1}; + % NB first value is last value of previous segment + LP_SACF(: , 1)=params.ACFboundaryValue{2}; + buffer=params.ACFboundaryValue{3}; end inputSignalMatrix=[buffer inputSignalMatrix]; [nChannels inputLength]= size(inputSignalMatrix); timeCounter=0; biggestSACF=0; for timePointer= max(lagPointers)+1:inputLength - % acf is a continuously changing channels x lags matrix + % ACF is a continuously changing channels x lags matrix % Only the current value is stored - % sacf is the vertical summary of acf ( a vector) and all values are kept and returned - % P is the smoothed version of sacf and all values are kept and returned + % SACF is the vertical sum of ACFs; all values are kept and returned + % LP_SACF is the smoothed version of SACF:all values are kept and returned % lagWeights emphasise some BF/lag combinations and ignore others % NB time now begins at the longest lag. - % E.g. if max(lags) is .04 then this is when the ACf will begin. - % AN AN delayed weights filtering - + % E.g. if max(lags) is .04 then this is when the ACf will begin (?). + % This is the ACF calculation timeCounter=timeCounter+1; - acf= (repmat(inputSignalMatrix(:, timePointer), 1, nLags) .* inputSignalMatrix(:, timePointer-lagPointers)).*lagWeights *dt + acf.* acfDecayFactors; - x=(mean(acf,1)./acfTaus)'; -% disp(num2str(x')) - sacf(:,timeCounter)=x; - P(:,timeCounter+1)=sacf(:,timeCounter)*(1-pDecayFactor)+P(:,timeCounter)*pDecayFactor; + ACF= (repmat(inputSignalMatrix(:, timePointer), 1, nLags) .* ... + inputSignalMatrix(:, timePointer-lagPointers)).*... + lagWeights *dt + ACF.* acfDecayFactors; + x=(mean(ACF,1)./acfTaus)'; + SACF(:,timeCounter)=x; - % plot at intervals of 200 points - if plotACF && ~mod(timePointer,params.plotACFsInterval) - % mark cursor on chart to signal progress - % this assumes that the user has already plotted - % the signal in subplot(2,1,1) of figure (13) - figure(13) - hold on - subplot(4,1,1) + % smoothed version of SACF + LP_SACF(:,timeCounter+1)=SACF(:,timeCounter)* (1-pDecayFactor) + ... + LP_SACF(:,timeCounter)* pDecayFactor; + + % plot ACF at intervals if requested to do so + if plotACF && ~mod(timePointer,params.plotACFsInterval) && ... + timePointer*dt>3*max(lags) + figure(89), clf + % plot ACFs one per channel + subplot(2,1,1) + UTIL_cascadePlot(ACF, lags) + title(['running ACF at ' num2str(dt*timePointer,'%5.3f') ' s']) + ylabel('channel BF'), xlabel('period (lag, ms)') + set(gca,'ytickLabel',[]) + + % plot SACF + subplot(4,1,3), cla + plotSACF=SACF(:,timeCounter)-min(SACF(:,timeCounter)); + plot(lags*1000, plotSACF, 'k') + biggestSACF=max(biggestSACF, max(plotSACF)); + if biggestSACF>0, ylim([0 biggestSACF]), else ylim([0 1]), end + ylabel('SACF'), set(gca,'ytickLabel',[]) +% xlim([min(lags*1000) max(lags*1000)]) + + % plot signal + subplot(4,1,4) + plot(signalTime, savedInputSignal, 'k'), hold on + xlim([0 max(signalTime)]) + a= ylim; + % mark cursor on chart to indicate progress time=timePointer*dt; - a =ylim; - plot([time time], [a(1) a(1)+(a(2)-a(1))/4]) % current signal point marker - - % plot ACFs one per channel - subplot(2,1,2), cla - cascadePlot(acf, lags, BFlist) - xlim([min(lags) max(lags)]) - % set(gca,'xscale','log') - title(num2str(method.dt*timePointer)) - ylabel('BF'), xlabel('period (lag)') - - % plot SACF - subplot(4,1,2), hold off - plot(lags,sacf(:,timeCounter)-min(sacf(:,timeCounter))) - biggestSACF=max(biggestSACF, max(sacf(:,timeCounter))); - if biggestSACF>0, ylim([0 biggestSACF]), end - % set(gca,'xscale','log') - title('SACF') + plot([time time], [a(1) a(1)+(a(2)-a(1))/4], 'r', 'linewidth', 5) pause(params.plotMoviePauses) end end -P=P(:,1:end-1); % correction for speed up above +LP_SACF=LP_SACF(:,1:end-1); % correction for speed up above % Pressnitzer weights if ~isfield(params, 'usePressnitzer'), params.usePressnitzer=0; end if params.usePressnitzer - [a nTimePoints]=size(P); + [a nTimePoints]=size(LP_SACF); % higher pitches get higher weights % PressnitzerWeights=repmat(min(lags)./lags,nTimePoints,1); % weaker weighting PressnitzerWeights=repmat((min(lags)./lags).^0.5, nTimePoints,1); - P=P.*PressnitzerWeights'; - sacf=sacf.*PressnitzerWeights'; + LP_SACF=LP_SACF.*PressnitzerWeights'; + SACF=SACF.*PressnitzerWeights'; end % wrap up -method.acfLags=lags; -method.filteredSACFdt=dt; - -boundaryValue{1}=acf(:,end-nLags+1:end); -boundaryValue{2}=P(:,end); +% BoundaryValue is legacy programming used in segmented model (keep) +ACFboundaryValue{1}=ACF(:,end-nLags+1:end); +ACFboundaryValue{2}=LP_SACF(:,end); % save signal buffer for next segment -boundaryValue{3} = inputSignalMatrix(:, end-max(lagPointers)+1 : end); - -method.displaydt=method.filteredSACFdt; - -% if ~isfield(params, 'plotUnflteredSACF'), params.plotUnflteredSACF=0; end -% if method.plotGraphs -% method.plotUnflteredSACF=params.plotUnflteredSACF; -% if ~method.plotUnflteredSACF -% method=filteredSACFPlot(P,method); -% else -% method=filteredSACFPlot(SACF,method); -% end -% end - - -% ------------------------------------------------ plotting ACFs -function cascadePlot(toPlot, lags, BFs) -% % useful code -[nChannels nLags]=size(toPlot); - -% cunning code to represent channels as parallel lines -[nRows nCols]=size(toPlot); -if nChannels>1 - % max(toPlot) defines the spacing between lines - a=max(max(toPlot))*(0:nRows-1)'; - % a is the height to be added to each channel - peakGain=10; - % peakGain emphasises the peak height - x=peakGain*toPlot+repmat(a,1,nCols); - x=nRows*x/max(max(x)); -else - x=toPlot; % used when only the stimulus is returned -end -plot(lags, x','k') -ylim([0 nRows]) - +ACFboundaryValue{3} = inputSignalMatrix(:, end-max(lagPointers)+1 : end);
--- a/MAP/old MAP files/MAP1_14AP.m Thu Oct 06 15:43:20 2011 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1254 +0,0 @@ - -function MAP1_14AP(inputSignal, sampleRate, BFlist, MAPparamsName, ... - AN_spikesOrProbability, paramChanges) -% To test this function use test_MAP1_14 in this folder -% -% All arguments are mandatory. -% -% BFlist is a vector of BFs but can be '-1' to allow MAPparams to choose -% MAPparamsName='Normal'; % source of model parameters -% AN_spikesOrProbability='spikes'; % or 'probability' -% paramChanges is a cell array of strings that can be used to make last -% minute parameter changes, e.g., to simulate OHC loss -% e.g. paramChanges{1}= 'DRNLParams.a=0;'; % disable OHCs -% e.g. paramchanges={}; % no changes -% The model parameters are established in the MAPparams<***> file -% and stored as global - -restorePath=path; -addpath (['..' filesep 'parameterStore']) - - -CONVOLUTION_CHANGE_TEST = 0; %for debug - - -global OMEParams DRNLParams IHC_cilia_RPParams IHCpreSynapseParams -global AN_IHCsynapseParams MacGregorParams MacGregorMultiParams - -% All of the results of this function are stored as global -global dt ANdt savedBFlist saveAN_spikesOrProbability saveMAPparamsName... - savedInputSignal OMEextEarPressure TMoutput OMEoutput ARattenuation ... - DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... - IHCoutput ANprobRateOutput ANoutput savePavailable tauCas ... - CNoutput ICoutput ICmembraneOutput ICfiberTypeRates MOCattenuation - -% Normally only ICoutput(logical spike matrix) or ANprobRateOutput will be -% needed by the user; so the following will suffice -% global ANdt ICoutput ANprobRateOutput - -% Note that sampleRate has not changed from the original function call and -% ANprobRateOutput is sampled at this rate -% However ANoutput, CNoutput and IC output are stored as logical -% 'spike' matrices using a lower sample rate (see ANdt). - -% When AN_spikesOrProbability is set to probability, -% no spike matrices are computed. -% When AN_spikesOrProbability is set to 'spikes', -% no probability output is computed - -% Efferent control variables are ARattenuation and MOCattenuation -% These are scalars between 1 (no attenuation) and 0. -% They are represented with dt=1/sampleRate (not ANdt) -% They are computed using either AN probability rate output -% or IC (spikes) output as approrpriate. -% AR is computed using across channel activity -% MOC is computed on a within-channel basis. - -if nargin<1 - error(' MAP1_14 is not a script but a function that must be called') -end - -if nargin<6 - paramChanges=[]; -end -% Read parameters from MAPparams<***> file in 'parameterStore' folder -% Beware, 'BFlist=-1' is a legitimate argument for MAPparams<> -% It means that the calling program allows MAPparams to specify the list -cmd=['method=MAPparams' MAPparamsName ... - '(BFlist, sampleRate, 0, paramChanges);']; -eval(cmd); -BFlist=DRNLParams.nonlinCFs; - -% save as global for later plotting if required -savedBFlist=BFlist; -saveAN_spikesOrProbability=AN_spikesOrProbability; -saveMAPparamsName=MAPparamsName; - -dt=1/sampleRate; -duration=length(inputSignal)/sampleRate; -% segmentDuration is specified in parameter file (must be >efferent delay) -segmentDuration=method.segmentDuration; -segmentLength=round(segmentDuration/ dt); -segmentTime=dt*(1:segmentLength); % used in debugging plots - -% all spiking activity is computed using longer epochs -ANspeedUpFactor=AN_IHCsynapseParams.ANspeedUpFactor; % e.g.5 times - -% inputSignal must be row vector -[r c]=size(inputSignal); -if r>c, inputSignal=inputSignal'; end % transpose -% ignore stereo signals -inputSignal=inputSignal(1,:); % drop any second channel -savedInputSignal=inputSignal; - -% Segment the signal -% The sgment length is given but the signal length must be adjusted to be a -% multiple of both the segment length and the reduced segmentlength -[nSignalRows signalLength]=size(inputSignal); -segmentLength=ceil(segmentLength/ANspeedUpFactor)*ANspeedUpFactor; -% Make the signal length a whole multiple of the segment length -nSignalSegments=ceil(signalLength/segmentLength); -padSize=nSignalSegments*segmentLength-signalLength; -pad=zeros(nSignalRows,padSize); -inputSignal=[inputSignal pad]; -[ignore signalLength]=size(inputSignal); - -% AN (spikes) is computed at a lower sample rate when spikes required -% so it has a reduced segment length (see 'ANspeeUpFactor' above) -% AN CN and IC all use this sample interval -ANdt=dt*ANspeedUpFactor; -reducedSegmentLength=round(segmentLength/ANspeedUpFactor); -reducedSignalLength= round(signalLength/ANspeedUpFactor); - -%% Initialise with respect to each stage before computing -% by allocating memory, -% by computing constants -% by establishing easy to read variable names -% The computations are made in segments and boundary conditions must -% be established and stored. These are found in variables with -% 'boundary' or 'bndry' in the name - -%% OME --- -% external ear resonances -OMEexternalResonanceFilters=OMEParams.externalResonanceFilters; -[nOMEExtFilters c]=size(OMEexternalResonanceFilters); -% details of external (outer ear) resonances -OMEgaindBs=OMEexternalResonanceFilters(:,1); -OMEgainScalars=10.^(OMEgaindBs/20); -OMEfilterOrder=OMEexternalResonanceFilters(:,2); -OMElowerCutOff=OMEexternalResonanceFilters(:,3); -OMEupperCutOff=OMEexternalResonanceFilters(:,4); -% external resonance coefficients -ExtFilter_b=cell(nOMEExtFilters,1); -ExtFilter_a=cell(nOMEExtFilters,1); -for idx=1:nOMEExtFilters - Nyquist=sampleRate/2; - [b, a] = butter(OMEfilterOrder(idx), ... - [OMElowerCutOff(idx) OMEupperCutOff(idx)]... - /Nyquist); - ExtFilter_b{idx}=b; - ExtFilter_a{idx}=a; -end -OMEExtFilterBndry=cell(2,1); -OMEextEarPressure=zeros(1,signalLength); % pressure at tympanic membrane - -% pressure to velocity conversion using smoothing filter (50 Hz cutoff) -tau=1/(2*pi*50); -a1=dt/tau-1; a0=1; -b0=1+ a1; -TMdisp_b=b0; TMdisp_a=[a0 a1]; -% figure(9), freqz(TMdisp_b, TMdisp_a) -OME_TMdisplacementBndry=[]; - -% OME high pass (simulates poor low frequency stapes response) -OMEhighPassHighCutOff=OMEParams.OMEstapesLPcutoff; -Nyquist=sampleRate/2; -[stapesDisp_b,stapesDisp_a] = butter(1, OMEhighPassHighCutOff/Nyquist, 'high'); -% figure(10), freqz(stapesDisp_b, stapesDisp_a) - -OMEhighPassBndry=[]; - -% OMEampStapes might be reducdant (use OMEParams.stapesScalar) -stapesScalar= OMEParams.stapesScalar; - -% Acoustic reflex -efferentDelayPts=round(OMEParams.ARdelay/dt); -% smoothing filter -a1=dt/OMEParams.ARtau-1; a0=1; -b0=1+ a1; -ARfilt_b=b0; ARfilt_a=[a0 a1]; - -ARattenuation=ones(1,signalLength); -ARrateThreshold=OMEParams.ARrateThreshold; % may not be used -ARrateToAttenuationFactor=OMEParams.rateToAttenuationFactor; -ARrateToAttenuationFactorProb=OMEParams.rateToAttenuationFactorProb; -ARboundary=[]; -ARboundaryProb=0; - -% save complete OME record (stapes displacement) -OMEoutput=zeros(1,signalLength); -TMoutput=zeros(1,signalLength); - -%% BM --- -% BM is represented as a list of locations identified by BF -DRNL_BFs=BFlist; -nBFs= length(DRNL_BFs); - -% DRNLchannelParameters=DRNLParams.channelParameters; -DRNLresponse= zeros(nBFs, segmentLength); - -MOCrateToAttenuationFactor=DRNLParams.rateToAttenuationFactor; -rateToAttenuationFactorProb=DRNLParams.rateToAttenuationFactorProb; -MOCrateThresholdProb=DRNLParams.MOCrateThresholdProb; - -% smoothing filter for MOC -a1=dt/DRNLParams.MOCtau-1; a0=1; -b0=1+ a1; -MOCfilt_b=b0; MOCfilt_a=[a0 a1]; -% figure(9), freqz(stapesDisp_b, stapesDisp_a) -MOCboundary=cell(nBFs,1); -MOCprobBoundary=cell(nBFs,1); - -MOCattSegment=zeros(nBFs,reducedSegmentLength); -MOCattenuation=ones(nBFs,signalLength); - -if DRNLParams.a>0 - DRNLcompressionThreshold=10^((1/(1-DRNLParams.c))* ... - log10(DRNLParams.b/DRNLParams.a)); -else - DRNLcompressionThreshold=inf; -end - -DRNLlinearOrder= DRNLParams.linOrder; -DRNLnonlinearOrder= DRNLParams.nonlinOrder; - -DRNLa=DRNLParams.a; -DRNLb=DRNLParams.b; -DRNLc=DRNLParams.c; -linGAIN=DRNLParams.g; -% -% gammatone filter coefficients for linear pathway -bw=DRNLParams.linBWs'; -phi = 2 * pi * bw * dt; -cf=DRNLParams.linCFs'; -theta = 2 * pi * cf * dt; -cos_theta = cos(theta); -sin_theta = sin(theta); -alpha = -exp(-phi).* cos_theta; -b0 = ones(nBFs,1); -b1 = 2 * alpha; -b2 = exp(-2 * phi); -z1 = (1 + alpha .* cos_theta) - (alpha .* sin_theta) * i; -z2 = (1 + b1 .* cos_theta) - (b1 .* sin_theta) * i; -z3 = (b2 .* cos(2 * theta)) - (b2 .* sin(2 * theta)) * i; -tf = (z2 + z3) ./ z1; -a0 = abs(tf); -a1 = alpha .* a0; -GTlin_a = [b0, b1, b2]; -GTlin_b = [a0, a1]; -GTlinOrder=DRNLlinearOrder; -GTlinBdry=cell(nBFs,GTlinOrder); - -% nonlinear gammatone filter coefficients -bw=DRNLParams.nlBWs'; -phi = 2 * pi * bw * dt; -cf=DRNLParams.nonlinCFs'; -theta = 2 * pi * cf * dt; -cos_theta = cos(theta); -sin_theta = sin(theta); -alpha = -exp(-phi).* cos_theta; -b0 = ones(nBFs,1); -b1 = 2 * alpha; -b2 = exp(-2 * phi); -z1 = (1 + alpha .* cos_theta) - (alpha .* sin_theta) * i; -z2 = (1 + b1 .* cos_theta) - (b1 .* sin_theta) * i; -z3 = (b2 .* cos(2 * theta)) - (b2 .* sin(2 * theta)) * i; -tf = (z2 + z3) ./ z1; -a0 = abs(tf); -a1 = alpha .* a0; -GTnonlin_a = [b0, b1, b2]; -GTnonlin_b = [a0, a1]; -GTnonlinOrder=DRNLnonlinearOrder; -GTnonlinBdry1=cell(nBFs, GTnonlinOrder); -GTnonlinBdry2=cell(nBFs, GTnonlinOrder); - -% complete BM record (BM displacement) -DRNLoutput=zeros(nBFs, signalLength); - - -%% IHC --- -% IHC cilia activity and receptor potential -% viscous coupling between BM and stereocilia displacement -% Nyquist=sampleRate/2; -% IHCcutoff=1/(2*pi*IHC_cilia_RPParams.tc); -% [IHCciliaFilter_b,IHCciliaFilter_a]=... -% butter(1, IHCcutoff/Nyquist, 'high'); -a1=dt/IHC_cilia_RPParams.tc-1; a0=1; -b0=1+ a1; -% high pass (i.e. low pass reversed) -IHCciliaFilter_b=[a0 a1]; IHCciliaFilter_a=b0; -% figure(9), freqz(IHCciliaFilter_b, IHCciliaFilter_a) - -IHCciliaBndry=cell(nBFs,1); - -% IHC apical conductance (Boltzman function) -IHC_C= IHC_cilia_RPParams.C; -IHCu0= IHC_cilia_RPParams.u0; -IHCu1= IHC_cilia_RPParams.u1; -IHCs0= IHC_cilia_RPParams.s0; -IHCs1= IHC_cilia_RPParams.s1; -IHCGmax= IHC_cilia_RPParams.Gmax; -IHCGa= IHC_cilia_RPParams.Ga; % (leakage) - -IHCGu0 = IHCGa+IHCGmax./(1+exp(IHCu0/IHCs0).*(1+exp(IHCu1/IHCs1))); -IHCrestingCiliaCond=IHCGu0; - -% Receptor potential -IHC_Cab= IHC_cilia_RPParams.Cab; -IHC_Gk= IHC_cilia_RPParams.Gk; -IHC_Et= IHC_cilia_RPParams.Et; -IHC_Ek= IHC_cilia_RPParams.Ek; -IHC_Ekp= IHC_Ek+IHC_Et*IHC_cilia_RPParams.Rpc; - -IHCrestingV= (IHC_Gk*IHC_Ekp+IHCGu0*IHC_Et)/(IHCGu0+IHC_Gk); - -IHC_Vnow= IHCrestingV*ones(nBFs,1); % initial voltage -IHC_RP= zeros(nBFs,segmentLength); - -% complete record of IHC receptor potential (V) -IHCciliaDisplacement= zeros(nBFs,segmentLength); -IHCoutput= zeros(nBFs,signalLength); -IHC_cilia_output= zeros(nBFs,signalLength); - - -%% pre-synapse --- -% Each BF is replicated using a different fiber type to make a 'channel' -% The number of channels is nBFs x nANfiberTypes -% Fiber types are specified in terms of tauCa -nANfiberTypes= length(IHCpreSynapseParams.tauCa); -tauCas= IHCpreSynapseParams.tauCa; -nANchannels= nANfiberTypes*nBFs; -synapticCa= zeros(nANchannels,segmentLength); - -% Calcium control (more calcium, greater release rate) -ECa=IHCpreSynapseParams.ECa; -gamma=IHCpreSynapseParams.gamma; -beta=IHCpreSynapseParams.beta; -tauM=IHCpreSynapseParams.tauM; -mICa=zeros(nANchannels,segmentLength); -GmaxCa=IHCpreSynapseParams.GmaxCa; -synapse_z= IHCpreSynapseParams.z; -synapse_power=IHCpreSynapseParams.power; - -% tauCa vector is established across channels to allow vectorization -% (one tauCa per channel). Do not confuse with tauCas (one pre fiber type) -tauCa=repmat(tauCas, nBFs,1); -tauCa=reshape(tauCa, nANchannels, 1); - -% presynapse startup values (vectors, length:nANchannels) -% proportion (0 - 1) of Ca channels open at IHCrestingV -mICaCurrent=((1+beta^-1 * exp(-gamma*IHCrestingV))^-1)... - *ones(nBFs*nANfiberTypes,1); -% corresponding startup currents -ICaCurrent= (GmaxCa*mICaCurrent.^3) * (IHCrestingV-ECa); -CaCurrent= ICaCurrent.*tauCa; - -% vesicle release rate at startup (one per channel) -% kt0 is used only at initialisation -kt0= -synapse_z * CaCurrent.^synapse_power; - - -%% AN --- -% each row of the AN matrices represents one AN fiber -% The results computed either for probabiities *or* for spikes (not both) -% Spikes are necessary if CN and IC are to be computed -nFibersPerChannel= AN_IHCsynapseParams.numFibers; -nANfibers= nANchannels*nFibersPerChannel; -AN_refractory_period= AN_IHCsynapseParams.refractory_period; - -y=AN_IHCsynapseParams.y; -l=AN_IHCsynapseParams.l; -x=AN_IHCsynapseParams.x; -r=AN_IHCsynapseParams.r; -M=round(AN_IHCsynapseParams.M); - -% probability (NB initial 'P' on everything) -PAN_ydt = repmat(AN_IHCsynapseParams.y*dt, nANchannels,1); -PAN_ldt = repmat(AN_IHCsynapseParams.l*dt, nANchannels,1); -PAN_xdt = repmat(AN_IHCsynapseParams.x*dt, nANchannels,1); -PAN_rdt = repmat(AN_IHCsynapseParams.r*dt, nANchannels,1); -PAN_rdt_plus_ldt = PAN_rdt + PAN_ldt; -PAN_M=round(AN_IHCsynapseParams.M); - -% compute starting values -Pcleft = kt0* y* M ./ (y*(l+r)+ kt0* l); -Pavailable = Pcleft*(l+r)./kt0; -Preprocess = Pcleft*r/x; % canbe fractional - -ANprobability=zeros(nANchannels,segmentLength); -ANprobRateOutput=zeros(nANchannels,signalLength); -lengthAbsRefractoryP= round(AN_refractory_period/dt); -% special variables for monitoring synaptic cleft (specialists only) -savePavailableSeg=zeros(nANchannels,segmentLength); -savePavailable=zeros(nANchannels,signalLength); - -% spikes % ! ! ! ! ! ! ! ! -lengthAbsRefractory= round(AN_refractory_period/ANdt); - -AN_ydt= repmat(AN_IHCsynapseParams.y*ANdt, nANfibers,1); -AN_ldt= repmat(AN_IHCsynapseParams.l*ANdt, nANfibers,1); -AN_xdt= repmat(AN_IHCsynapseParams.x*ANdt, nANfibers,1); -AN_rdt= repmat(AN_IHCsynapseParams.r*ANdt, nANfibers,1); -AN_rdt_plus_ldt= AN_rdt + AN_ldt; -AN_M= round(AN_IHCsynapseParams.M); - -% kt0 is initial release rate -% Establish as a vector (length=channel x number of fibers) -kt0= repmat(kt0', nFibersPerChannel, 1); -kt0=reshape(kt0, nANfibers,1); - -% starting values for reservoirs -AN_cleft = kt0* y* M ./ (y*(l+r)+ kt0* l); -AN_available = round(AN_cleft*(l+r)./kt0); %must be integer -AN_reprocess = AN_cleft*r/x; - -% output is in a logical array spikes = 1/ 0. -ANspikes= false(nANfibers,reducedSegmentLength); -ANoutput= false(nANfibers,reducedSignalLength); - - -%% CN (first brain stem nucleus - could be any subdivision of CN) -% Input to a CN neuorn is a random selection of AN fibers within a channel -% The number of AN fibers used is ANfibersFanInToCN -% CNtauGk (Potassium time constant) determines the rate of firing of -% the unit when driven hard by a DC input (not normally >350 sp/s) -% If there is more than one value, everything is replicated accordingly - -ANavailableFibersPerChan=AN_IHCsynapseParams.numFibers; -ANfibersFanInToCN=MacGregorMultiParams.fibersPerNeuron; - -CNtauGk=MacGregorMultiParams.tauGk; % row vector of CN types (by tauGk) -nCNtauGk=length(CNtauGk); - -% the total number of 'channels' is now greater -nCNchannels=nANchannels*nCNtauGk; - -nCNneuronsPerChannel=MacGregorMultiParams.nNeuronsPerBF; -tauGk=repmat(CNtauGk, nCNneuronsPerChannel,1); -tauGk=reshape(tauGk,nCNneuronsPerChannel*nCNtauGk,1); - -% Now the number of neurons has been increased -nCNneurons=nCNneuronsPerChannel*nCNchannels; -CNmembranePotential=zeros(nCNneurons,reducedSegmentLength); - -% establish which ANfibers (by name) feed into which CN nuerons -CNinputfiberLists=zeros(nANchannels*nCNneuronsPerChannel, ANfibersFanInToCN); -unitNo=1; -for ch=1:nANchannels - % Each channel contains a number of units =length(listOfFanInValues) - for idx=1:nCNneuronsPerChannel - for idx2=1:nCNtauGk - fibersUsed=(ch-1)*ANavailableFibersPerChan + ... - ceil(rand(1,ANfibersFanInToCN)* ANavailableFibersPerChan); - CNinputfiberLists(unitNo,:)=fibersUsed; - unitNo=unitNo+1; - end - end -end - -% input to CN units -AN_PSTH=zeros(nCNneurons,reducedSegmentLength); - -% Generate CNalphaFunction function -% by which spikes are converted to post-synaptic currents -CNdendriteLPfreq= MacGregorMultiParams.dendriteLPfreq; -CNcurrentPerSpike=MacGregorMultiParams.currentPerSpike; -CNspikeToCurrentTau=1/(2*pi*CNdendriteLPfreq); -t=ANdt:ANdt:5*CNspikeToCurrentTau; -CNalphaFunction= (1 / ... - CNspikeToCurrentTau)*t.*exp(-t /CNspikeToCurrentTau); -CNalphaFunction=CNalphaFunction*CNcurrentPerSpike; - -% figure(98), plot(t,CNalphaFunction) -% working memory for implementing convolution - -CNcurrentTemp=... - zeros(nCNneurons,reducedSegmentLength+length(CNalphaFunction)-1); -% trailing alphas are parts of humps carried forward to the next segment -CNtrailingAlphas=zeros(nCNneurons,length(CNalphaFunction)); - -CN_tauM=MacGregorMultiParams.tauM; -CN_tauTh=MacGregorMultiParams.tauTh; -CN_cap=MacGregorMultiParams.Cap; -CN_c=MacGregorMultiParams.c; -CN_b=MacGregorMultiParams.dGkSpike; -CN_Ek=MacGregorMultiParams.Ek; -CN_Eb= MacGregorMultiParams.Eb; -CN_Er=MacGregorMultiParams.Er; -CN_Th0= MacGregorMultiParams.Th0; -CN_E= zeros(nCNneurons,1); -CN_Gk= zeros(nCNneurons,1); -CN_Th= MacGregorMultiParams.Th0*ones(nCNneurons,1); -CN_Eb=CN_Eb.*ones(nCNneurons,1); -CN_Er=CN_Er.*ones(nCNneurons,1); -CNtimeSinceLastSpike=zeros(nCNneurons,1); -% tauGk is the main distinction between neurons -% in fact they are all the same in the standard model -tauGk=repmat(tauGk,nANchannels,1); - -CNoutput=false(nCNneurons,reducedSignalLength); - - -%% MacGregor (IC - second nucleus) -------- -nICcells=nANchannels*nCNtauGk; % one cell per channel -CN_PSTH=zeros(nICcells ,reducedSegmentLength); - -ICspikeWidth=0.00015; % this may need revisiting -epochsPerSpike=round(ICspikeWidth/ANdt); -if epochsPerSpike<1 - error(['MacGregorMulti: sample rate too low to support ' ... - num2str(ICspikeWidth*1e6) ' microsec spikes']); -end - -% short names -IC_tauM=MacGregorParams.tauM; -IC_tauGk=MacGregorParams.tauGk; -IC_tauTh=MacGregorParams.tauTh; -IC_cap=MacGregorParams.Cap; -IC_c=MacGregorParams.c; -IC_b=MacGregorParams.dGkSpike; -IC_Th0=MacGregorParams.Th0; -IC_Ek=MacGregorParams.Ek; -IC_Eb= MacGregorParams.Eb; -IC_Er=MacGregorParams.Er; - -IC_E=zeros(nICcells,1); -IC_Gk=zeros(nICcells,1); -IC_Th=IC_Th0*ones(nICcells,1); - -% Dendritic filtering, all spikes are replaced by CNalphaFunction functions -ICdendriteLPfreq= MacGregorParams.dendriteLPfreq; -ICcurrentPerSpike=MacGregorParams.currentPerSpike; -ICspikeToCurrentTau=1/(2*pi*ICdendriteLPfreq); -t=ANdt:ANdt:3*ICspikeToCurrentTau; -IC_CNalphaFunction= (ICcurrentPerSpike / ... - ICspikeToCurrentTau)*t.*exp(-t / ICspikeToCurrentTau); -% figure(98), plot(t,IC_CNalphaFunction) - -% working space for implementing alpha function -ICcurrentTemp=... - zeros(nICcells,reducedSegmentLength+length(IC_CNalphaFunction)-1); -ICtrailingAlphas=zeros(nICcells, length(IC_CNalphaFunction)); - -ICfiberTypeRates=zeros(nANfiberTypes,reducedSignalLength); -ICoutput=false(nICcells,reducedSignalLength); - -ICmembranePotential=zeros(nICcells,reducedSegmentLength); -ICmembraneOutput=zeros(nICcells,signalLength); - - -%% Main program %% %% %% %% %% %% %% %% %% %% %% %% %% %% - -% Compute the entire model for each segment -segmentStartPTR=1; -reducedSegmentPTR=1; % when sampling rate is reduced -while segmentStartPTR<signalLength - segmentEndPTR=segmentStartPTR+segmentLength-1; - % shorter segments after speed up. - shorterSegmentEndPTR=reducedSegmentPTR+reducedSegmentLength-1; - - inputPressureSegment=inputSignal... - (:,segmentStartPTR:segmentStartPTR+segmentLength-1); - - % segment debugging plots - % figure(98) - % plot(segmentTime,inputPressureSegment), title('signalSegment') - - - % OME ---------------------- - - % OME Stage 1: external resonances. Add to inputSignal pressure wave - y=inputPressureSegment; - for n=1:nOMEExtFilters - % any number of resonances can be used - [x OMEExtFilterBndry{n}] = ... - filter(ExtFilter_b{n},ExtFilter_a{n},... - inputPressureSegment, OMEExtFilterBndry{n}); - x= x* OMEgainScalars(n); - % This is a parallel resonance so add it - y=y+x; - end - inputPressureSegment=y; - OMEextEarPressure(segmentStartPTR:segmentEndPTR)= inputPressureSegment; - - % OME stage 2: convert input pressure (velocity) to - % tympanic membrane(TM) displacement using low pass filter - [TMdisplacementSegment OME_TMdisplacementBndry] = ... - filter(TMdisp_b,TMdisp_a,inputPressureSegment, ... - OME_TMdisplacementBndry); - % and save it - TMoutput(segmentStartPTR:segmentEndPTR)= TMdisplacementSegment; - - % OME stage 3: middle ear high pass effect to simulate stapes inertia - [stapesDisplacement OMEhighPassBndry] = ... - filter(stapesDisp_b,stapesDisp_a,TMdisplacementSegment, ... - OMEhighPassBndry); - - % OME stage 4: apply stapes scalar - stapesDisplacement=stapesDisplacement*stapesScalar; - - % OME stage 5: acoustic reflex stapes attenuation - % Attenuate the TM response using feedback from LSR fiber activity - if segmentStartPTR>efferentDelayPts - stapesDisplacement= stapesDisplacement.*... - ARattenuation(segmentStartPTR-efferentDelayPts:... - segmentEndPTR-efferentDelayPts); - end - - % segment debugging plots - % figure(98) - % plot(segmentTime, stapesDisplacement), title ('stapesDisplacement') - - % and save - OMEoutput(segmentStartPTR:segmentEndPTR)= stapesDisplacement; - - - %% BM ------------------------------ - % Each location is computed separately - for BFno=1:nBFs - - % *linear* path - linOutput = stapesDisplacement * linGAIN; % linear gain - for order = 1 : GTlinOrder - [linOutput GTlinBdry{BFno,order}] = ... - filter(GTlin_b(BFno,:), GTlin_a(BFno,:), linOutput, GTlinBdry{BFno,order}); - end - - % *nonLinear* path - % efferent attenuation (0 <> 1) - if segmentStartPTR>efferentDelayPts - MOC=MOCattenuation(BFno, segmentStartPTR-efferentDelayPts:... - segmentEndPTR-efferentDelayPts); - else % no MOC available yet - MOC=ones(1, segmentLength); - end - % apply MOC to nonlinear input function - nonlinOutput=stapesDisplacement.* MOC; - - % first gammatone filter (nonlin path) - for order = 1 : GTnonlinOrder - [nonlinOutput GTnonlinBdry1{BFno,order}] = ... - filter(GTnonlin_b(BFno,:), GTnonlin_a(BFno,:), ... - nonlinOutput, GTnonlinBdry1{BFno,order}); - end - % broken stick instantaneous compression - y= nonlinOutput.* DRNLa; % linear section. - % compress parts of the signal above the compression threshold - abs_x = abs(nonlinOutput); - idx=find(abs_x>DRNLcompressionThreshold); - if ~isempty(idx)>0 - y(idx)=sign(y(idx)).* (DRNLb*abs_x(idx).^DRNLc); - end - nonlinOutput=y; - - % second filter removes distortion products - for order = 1 : GTnonlinOrder - [ nonlinOutput GTnonlinBdry2{BFno,order}] = ... - filter(GTnonlin_b(BFno,:), GTnonlin_a(BFno,:), ... - nonlinOutput, GTnonlinBdry2{BFno,order}); - end - - % combine the two paths to give the DRNL displacement - DRNLresponse(BFno,:)=linOutput+nonlinOutput; - end % BF - - % segment debugging plots - % figure(98) - % if size(DRNLresponse,1)>3 - % imagesc(DRNLresponse) % matrix display - % title('DRNLresponse'); % single or double channel response - % else - % plot(segmentTime, DRNLresponse) - % end - - % and save it - DRNLoutput(:, segmentStartPTR:segmentEndPTR)= DRNLresponse; - - - %% IHC ------------------------------------ - % BM displacement to IHCciliaDisplacement is a high-pass filter - % because of viscous coupling - for idx=1:nBFs - [IHCciliaDisplacement(idx,:) IHCciliaBndry{idx}] = ... - filter(IHCciliaFilter_b,IHCciliaFilter_a, ... - DRNLresponse(idx,:), IHCciliaBndry{idx}); - end - - % apply scalar - IHCciliaDisplacement=IHCciliaDisplacement* IHC_C; - - % and save it - IHC_cilia_output(:,segmentStartPTR:segmentStartPTR+segmentLength-1)=... - IHCciliaDisplacement; - - % compute apical conductance - G=IHCGmax./(1+exp(-(IHCciliaDisplacement-IHCu0)/IHCs0).*... - (1+exp(-(IHCciliaDisplacement-IHCu1)/IHCs1))); - Gu=G + IHCGa; - - % Compute receptor potential - for idx=1:segmentLength - IHC_Vnow=IHC_Vnow+ (-Gu(:, idx).*(IHC_Vnow-IHC_Et)-... - IHC_Gk*(IHC_Vnow-IHC_Ekp))* dt/IHC_Cab; - IHC_RP(:,idx)=IHC_Vnow; - end - - % segment debugging plots - % if size(IHC_RP,1)>3 - % surf(IHC_RP), shading interp, title('IHC_RP') - % else - % plot(segmentTime, IHC_RP) - % end - - % and save it - IHCoutput(:, segmentStartPTR:segmentStartPTR+segmentLength-1)=IHC_RP; - - - %% synapse ----------------------------- - % Compute the vesicle release rate for each fiber type at each BF - % replicate IHC_RP for each fiber type - Vsynapse=repmat(IHC_RP, nANfiberTypes,1); - - % look-up table of target fraction channels open for a given IHC_RP - mICaINF= 1./( 1 + exp(-gamma * Vsynapse) /beta); - % fraction of channel open - apply time constant - for idx=1:segmentLength - % mICaINF is the current 'target' value of mICa - mICaCurrent=mICaCurrent+(mICaINF(:,idx)-mICaCurrent)*dt./tauM; - mICa(:,idx)=mICaCurrent; - end - - ICa= (GmaxCa* mICa.^3) .* (Vsynapse- ECa); - - for idx=1:segmentLength - CaCurrent=CaCurrent + ICa(:,idx)*dt - CaCurrent*dt./tauCa; - synapticCa(:,idx)=CaCurrent; - end - synapticCa=-synapticCa; % treat IHCpreSynapseParams as positive substance - - % NB vesicleReleaseRate is /s and is independent of dt - vesicleReleaseRate = synapse_z * synapticCa.^synapse_power; % rate - - % segment debugging plots - % if size(vesicleReleaseRate,1)>3 - % surf(vesicleReleaseRate), shading interp, title('vesicleReleaseRate') - % else - % plot(segmentTime, vesicleReleaseRate) - % end - - - %% AN - switch AN_spikesOrProbability - case 'probability' - % No refractory effect is applied - for t = 1:segmentLength; - M_Pq=PAN_M-Pavailable; - M_Pq(M_Pq<0)=0; - Preplenish = M_Pq .* PAN_ydt; - Pejected = Pavailable.* vesicleReleaseRate(:,t)*dt; - Preprocessed = M_Pq.*Preprocess.* PAN_xdt; - - ANprobability(:,t)= min(Pejected,1); - reuptakeandlost= PAN_rdt_plus_ldt .* Pcleft; - reuptake= PAN_rdt.* Pcleft; - - Pavailable= Pavailable+ Preplenish- Pejected+ Preprocessed; - Pcleft= Pcleft + Pejected - reuptakeandlost; - Preprocess= Preprocess + reuptake - Preprocessed; - Pavailable(Pavailable<0)=0; - savePavailableSeg(:,t)=Pavailable; % synapse tracking - end - % and save it as *rate* - ANrate=ANprobability/dt; - ANprobRateOutput(:, segmentStartPTR:... - segmentStartPTR+segmentLength-1)= ANrate; - % monitor synapse contents (only sometimes used) - savePavailable(:, segmentStartPTR:segmentStartPTR+segmentLength-1)=... - savePavailableSeg; - - % Estimate efferent effects. ARattenuation (0 <> 1) - % acoustic reflex - [r c]=size(ANrate); - if r>nBFs % Only if LSR fibers are computed - ARAttSeg=mean(ANrate(1:nBFs,:),1); %LSR channels are 1:nBF - % smooth - [ARAttSeg, ARboundaryProb] = ... - filter(ARfilt_b, ARfilt_a, ARAttSeg, ARboundaryProb); - ARAttSeg=ARAttSeg-ARrateThreshold; - ARAttSeg(ARAttSeg<0)=0; % prevent negative strengths - ARattenuation(segmentStartPTR:segmentEndPTR)=... - (1-ARrateToAttenuationFactorProb.* ARAttSeg); - end - % plot(ARattenuation) - - % MOC attenuation - % within-channel HSR response only - HSRbegins=nBFs*(nANfiberTypes-1)+1; - rates=ANrate(HSRbegins:end,:); - if rateToAttenuationFactorProb<0 - % negative factor implies a fixed attenuation - MOCattenuation(:,segmentStartPTR:segmentEndPTR)= ... - ones(size(rates))* -rateToAttenuationFactorProb; - else - for idx=1:nBFs - [smoothedRates, MOCprobBoundary{idx}] = ... - filter(MOCfilt_b, MOCfilt_a, rates(idx,:), ... - MOCprobBoundary{idx}); - smoothedRates=smoothedRates-MOCrateThresholdProb; - smoothedRates(smoothedRates<0)=0; - MOCattenuation(idx,segmentStartPTR:segmentEndPTR)= ... - (1- smoothedRates* rateToAttenuationFactorProb); - end - end - MOCattenuation(MOCattenuation<0)=0.001; - - % plot(MOCattenuation) - - - case 'spikes' - ANtimeCount=0; - % implement speed upt - for t = ANspeedUpFactor:ANspeedUpFactor:segmentLength; - ANtimeCount=ANtimeCount+1; - % convert release rate to probabilities - releaseProb=vesicleReleaseRate(:,t)*ANdt; - % releaseProb is the release probability per channel - % but each channel has many synapses - releaseProb=repmat(releaseProb',nFibersPerChannel,1); - releaseProb=reshape(releaseProb, nFibersPerChannel*nANchannels,1); - - % AN_available=round(AN_available); % vesicles must be integer, (?needed) - M_q=AN_M- AN_available; % number of missing vesicles - M_q(M_q<0)= 0; % cannot be less than 0 - - % AN_N1 converts probability to discrete events - % it considers each event that might occur - % (how many vesicles might be released) - % and returns a count of how many were released - - % slow line -% probabilities= 1-(1-releaseProb).^AN_available; - probabilities= 1-intpow((1-releaseProb), AN_available); - ejected= probabilities> rand(length(AN_available),1); - - reuptakeandlost = AN_rdt_plus_ldt .* AN_cleft; - reuptake = AN_rdt.* AN_cleft; - - % slow line -% probabilities= 1-(1-AN_reprocess.*AN_xdt).^M_q; - probabilities= 1-intpow((1-AN_reprocess.*AN_xdt), M_q); - reprocessed= probabilities>rand(length(M_q),1); - - % slow line -% probabilities= 1-(1-AN_ydt).^M_q; - probabilities= 1-intpow((1-AN_ydt), M_q); - - replenish= probabilities>rand(length(M_q),1); - - AN_available = AN_available + replenish - ejected ... - + reprocessed; - AN_cleft = AN_cleft + ejected - reuptakeandlost; - AN_reprocess = AN_reprocess + reuptake - reprocessed; - - % ANspikes is logical record of vesicle release events>0 - ANspikes(:, ANtimeCount)= ejected; - end % t - - % zero any events that are preceded by release events ... - % within the refractory period - % The refractory period consist of two periods - % 1) the absolute period where no spikes occur - % 2) a relative period where a spike may occur. This relative - % period is realised as a variable length interval - % where the length is chosen at random - % (esentially a linear ramp up) - - % Andreas has a fix for this - for t = 1:ANtimeCount-2*lengthAbsRefractory; - % identify all spikes across fiber array at time (t) - % idx is a list of channels where spikes occurred - % ?? try sparse matrices? - idx=find(ANspikes(:,t)); - for j=idx % consider each spike - % specify variable refractory period - % between abs and 2*abs refractory period - nPointsRefractory=lengthAbsRefractory+... - round(rand*lengthAbsRefractory); - % disable spike potential for refractory period - % set all values in this range to 0 - ANspikes(j,t+1:t+nPointsRefractory)=0; - end - end %t - - % segment debugging - % plotInstructions.figureNo=98; - % plotInstructions.displaydt=ANdt; - % plotInstructions.numPlots=1; - % plotInstructions.subPlotNo=1; - % UTIL_plotMatrix(ANspikes, plotInstructions); - - % and save it. NB, AN is now on 'speedUp' time - ANoutput(:, reducedSegmentPTR: shorterSegmentEndPTR)=ANspikes; - - - %% CN Macgregor first neucleus ------------------------------- - % input is from AN so ANdt is used throughout - % Each CNneuron has a unique set of input fibers selected - % at random from the available AN fibers (CNinputfiberLists) - - % Create the dendritic current for that neuron - % First get input spikes to this neuron - synapseNo=1; - for ch=1:nCNchannels - for idx=1:nCNneuronsPerChannel - % determine candidate fibers for this unit - fibersUsed=CNinputfiberLists(synapseNo,:); - % ANpsth has a bin width of ANdt - % (just a simple sum across fibers) - AN_PSTH(synapseNo,:) = ... - sum(ANspikes(fibersUsed,:), 1); - synapseNo=synapseNo+1; - end - end - - - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - - - % One alpha function per spike - [alphaRows alphaCols]=size(CNtrailingAlphas); - - for unitNo=1:nCNneurons - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - CNcurrentTemp0(unitNo,:)= ... - conv(AN_PSTH(unitNo,:),CNalphaFunction); - - - - CNcurrentTemp(unitNo,:)= ... - conv2(AN_PSTH(unitNo,:),CNalphaFunction); - % Changed conv to conv2 because it runs faster. (Andreas) - - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - -% -% -% f = CNalphaFunction; -% g = AN_PSTH(unitNo,:); -% -% -% g = [g zeros(1,length(f)-1)]; -% -% spikePos = find(g)'; -% -% result = zeros(1,length(g)); -% -% for index = 1:length(spikePos) -% k = spikePos(index); -% result(k:(k+length(f)-1)) = result(k:(k+length(f)-1)) + g(k)*f; -% end -% -% CNcurrentTemp2(unitNo,:) = result; - - - end - - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - - - - f = CNalphaFunction; - g = AN_PSTH; - - g = [g zeros(size(g,1),length(f)-1)]; - - [r c] = find(g); - - CNcurrentTemp2 = zeros(size(g)); - - for index = 1:length(r) - - row = r(index); - col = c(index); - - CNcurrentTemp2(row,col:col+length(f)-1) = CNcurrentTemp2(row,col:col+length(f)-1) + f*g(row,col); - - end - - - -CONVOLUTION_CHANGE_TEST = CONVOLUTION_CHANGE_TEST + sum(abs(CNcurrentTemp2 - CNcurrentTemp))+ sum(abs(CNcurrentTemp0 - CNcurrentTemp)); - - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - - - -% disp(['sum(AN_PSTH)= ' num2str(sum(AN_PSTH(1,:)))]) - % add post-synaptic current left over from previous segment - CNcurrentTemp(:,1:alphaCols)=... - CNcurrentTemp(:,1:alphaCols)+ CNtrailingAlphas; - - % take post-synaptic current for this segment - CNcurrentInput= CNcurrentTemp(:, 1:reducedSegmentLength); -% disp(['mean(CNcurrentInput)= ' num2str(mean(CNcurrentInput(1,:)))]) - - % trailingalphas are the ends of the alpha functions that - % spill over into the next segment - CNtrailingAlphas= ... - CNcurrentTemp(:, reducedSegmentLength+1:end); - - if CN_c>0 - % variable threshold condition (slow) - for t=1:reducedSegmentLength - CNtimeSinceLastSpike=CNtimeSinceLastSpike-ANdt; - s=CN_E>CN_Th & CNtimeSinceLastSpike<0 ; - CNtimeSinceLastSpike(s)=0.0005; % 0.5 ms for sodium spike - dE =(-CN_E/CN_tauM + ... - CNcurrentInput(:,t)/CN_cap+(... - CN_Gk/CN_cap).*(CN_Ek-CN_E))*ANdt; - dGk=-CN_Gk*ANdt./tauGk + CN_b*s; - dTh=-(CN_Th-CN_Th0)*ANdt/CN_tauTh + CN_c*s; - CN_E=CN_E+dE; - CN_Gk=CN_Gk+dGk; - CN_Th=CN_Th+dTh; - CNmembranePotential(:,t)=CN_E+s.*(CN_Eb-CN_E)+CN_Er; - end - else - % static threshold (faster) - E=zeros(1,reducedSegmentLength); - Gk=zeros(1,reducedSegmentLength); - ss=zeros(1,reducedSegmentLength); - for t=1:reducedSegmentLength - % time of previous spike moves back in time - CNtimeSinceLastSpike=CNtimeSinceLastSpike-ANdt; - % action potential if E>threshold - % allow time for s to reset between events - s=CN_E>CN_Th0 & CNtimeSinceLastSpike<0 ; - ss(t)=s(1); - CNtimeSinceLastSpike(s)=0.0005; % 0.5 ms for sodium spike - dE = (-CN_E/CN_tauM + ... - CNcurrentInput(:,t)/CN_cap +... - (CN_Gk/CN_cap).*(CN_Ek-CN_E))*ANdt; - dGk=-CN_Gk*ANdt./tauGk +CN_b*s; - CN_E=CN_E+dE; - CN_Gk=CN_Gk+dGk; - E(t)=CN_E(1); - Gk(t)=CN_Gk(1); - % add spike to CN_E and add resting potential (-60 mV) - CNmembranePotential(:,t)=CN_E +s.*(CN_Eb-CN_E)+CN_Er; - end - end -% disp(['CN_E= ' num2str(sum(CN_E(1,:)))]) -% disp(['CN_Gk= ' num2str(sum(CN_Gk(1,:)))]) -% disp(['CNmembranePotential= ' num2str(sum(CNmembranePotential(1,:)))]) -% plot(CNmembranePotential(1,:)) - - - % extract spikes. A spike is a substantial upswing in voltage - CN_spikes=CNmembranePotential> -0.02; -% disp(['CNspikesbefore= ' num2str(sum(sum(CN_spikes)))]) - - % now remove any spike that is immediately followed by a spike - % NB 'find' works on columns (whence the transposing) - % for each spike put a zero in the next epoch - CN_spikes=CN_spikes'; - idx=find(CN_spikes); - idx=idx(1:end-1); - CN_spikes(idx+1)=0; - CN_spikes=CN_spikes'; -% disp(['CNspikes= ' num2str(sum(sum(CN_spikes)))]) - - % segment debugging - % plotInstructions.figureNo=98; - % plotInstructions.displaydt=ANdt; - % plotInstructions.numPlots=1; - % plotInstructions.subPlotNo=1; - % UTIL_plotMatrix(CN_spikes, plotInstructions); - - % and save it - CNoutput(:, reducedSegmentPTR:shorterSegmentEndPTR)=... - CN_spikes; - - - %% IC ---------------------------------------------- - % MacGregor or some other second order neurons - - % combine CN neurons in same channel, - % i.e. same BF & same tauCa - % to generate inputs to single IC unit - channelNo=0; - for idx=1:nCNneuronsPerChannel:nCNneurons-nCNneuronsPerChannel+1; - channelNo=channelNo+1; - CN_PSTH(channelNo,:)=... - sum(CN_spikes(idx:idx+nCNneuronsPerChannel-1,:)); - end - - [alphaRows alphaCols]=size(ICtrailingAlphas); - for ICneuronNo=1:nICcells - ICcurrentTemp(ICneuronNo,:)= ... - conv2(CN_PSTH(ICneuronNo,:), IC_CNalphaFunction); - % Changed conv to conv2 because it runs faster. (Andreas) - end - - % add the unused current from the previous convolution - ICcurrentTemp(:,1:alphaCols)=ICcurrentTemp(:,1:alphaCols)... - + ICtrailingAlphas; - % take what is required and keep the trailing part for next time - inputCurrent=ICcurrentTemp(:, 1:reducedSegmentLength); - ICtrailingAlphas=ICcurrentTemp(:, reducedSegmentLength+1:end); - - if IC_c==0 - % faster computation when threshold is stable (C==0) - for t=1:reducedSegmentLength - s=IC_E>IC_Th0; - dE = (-IC_E/IC_tauM + inputCurrent(:,t)/IC_cap +... - (IC_Gk/IC_cap).*(IC_Ek-IC_E))*ANdt; - dGk=-IC_Gk*ANdt/IC_tauGk +IC_b*s; - IC_E=IC_E+dE; - IC_Gk=IC_Gk+dGk; - ICmembranePotential(:,t)=IC_E+s.*(IC_Eb-IC_E)+IC_Er; - end - else - % threshold is changing (IC_c>0; e.g. bushy cell) - for t=1:reducedSegmentLength - dE = (-IC_E/IC_tauM + ... - inputCurrent(:,t)/IC_cap + (IC_Gk/IC_cap)... - .*(IC_Ek-IC_E))*ANdt; - IC_E=IC_E+dE; - s=IC_E>IC_Th; - ICmembranePotential(:,t)=IC_E+s.*(IC_Eb-IC_E)+IC_Er; - dGk=-IC_Gk*ANdt/IC_tauGk +IC_b*s; - IC_Gk=IC_Gk+dGk; - - % After a spike, the threshold is raised - % otherwise it settles to its baseline - dTh=-(IC_Th-Th0)*ANdt/IC_tauTh +s*IC_c; - IC_Th=IC_Th+dTh; - end - end - - ICspikes=ICmembranePotential> -0.01; - % now remove any spike that is immediately followed by a spike - % NB 'find' works on columns (whence the transposing) - ICspikes=ICspikes'; - idx=find(ICspikes); - idx=idx(1:end-1); - ICspikes(idx+1)=0; - ICspikes=ICspikes'; - - nCellsPerTau= nICcells/nANfiberTypes; - firstCell=1; - lastCell=nCellsPerTau; - for tauCount=1:nANfiberTypes - % separate rates according to fiber types - % currently only the last segment is saved - ICfiberTypeRates(tauCount, ... - reducedSegmentPTR:shorterSegmentEndPTR)=... - sum(ICspikes(firstCell:lastCell, :))... - /(nCellsPerTau*ANdt); - firstCell=firstCell+nCellsPerTau; - lastCell=lastCell+nCellsPerTau; - end - - ICoutput(:,reducedSegmentPTR:shorterSegmentEndPTR)=ICspikes; - - % store membrane output on original dt scale - if nBFs==1 % single channel - x= repmat(ICmembranePotential(1,:), ANspeedUpFactor,1); - x= reshape(x,1,segmentLength); - if nANfiberTypes>1 % save HSR and LSR - y=repmat(ICmembranePotential(end,:),... - ANspeedUpFactor,1); - y= reshape(y,1,segmentLength); - x=[x; y]; - end - ICmembraneOutput(:, segmentStartPTR:segmentEndPTR)= x; - end - - % estimate efferent effects. - % ARis based on LSR units. LSR channels are 1:nBF - if nANfiberTypes>1 % AR is multi-channel only - ARAttSeg=sum(ICspikes(1:nBFs,:),1)/ANdt; - [ARAttSeg, ARboundary] = ... - filter(ARfilt_b, ARfilt_a, ARAttSeg, ARboundary); - ARAttSeg=ARAttSeg-ARrateThreshold; - ARAttSeg(ARAttSeg<0)=0; % prevent negative strengths - % scale up to dt from ANdt - x= repmat(ARAttSeg, ANspeedUpFactor,1); - x=reshape(x,1,segmentLength); - ARattenuation(segmentStartPTR:segmentEndPTR)=... - (1-ARrateToAttenuationFactor* x); - ARattenuation(ARattenuation<0)=0.001; - else - % single channel model; disable AR - ARattenuation(segmentStartPTR:segmentEndPTR)=... - ones(1,segmentLength); - end - - % MOC attenuation using HSR response only - % Separate MOC effect for each BF - HSRbegins=nBFs*(nANfiberTypes-1)+1; - rates=ICspikes(HSRbegins:end,:)/ANdt; - for idx=1:nBFs - [smoothedRates, MOCboundary{idx}] = ... - filter(MOCfilt_b, MOCfilt_a, rates(idx,:), ... - MOCboundary{idx}); - % spont 'rates' is zero for IC - MOCattSegment(idx,:)=smoothedRates; - % expand timescale back to model dt from ANdt - x= repmat(MOCattSegment(idx,:), ANspeedUpFactor,1); - x= reshape(x,1,segmentLength); - MOCattenuation(idx,segmentStartPTR:segmentEndPTR)= ... - (1- MOCrateToAttenuationFactor* x); - end - MOCattenuation(MOCattenuation<0)=0.04; - % segment debugging - % plotInstructions.figureNo=98; - % plotInstructions.displaydt=ANdt; - % plotInstructions.numPlots=1; - % plotInstructions.subPlotNo=1; - % UTIL_plotMatrix(ICspikes, plotInstructions); - - end % AN_spikesOrProbability - segmentStartPTR=segmentStartPTR+segmentLength; - reducedSegmentPTR=reducedSegmentPTR+reducedSegmentLength; - - -end % segment - -disp('CONVOLUTION_CHANGE_TEST (if followed by zero all is good)') -disp(max(CONVOLUTION_CHANGE_TEST)) %% for debugging - - -%% apply refractory correction to spike probabilities - -% switch AN_spikesOrProbability -% case 'probability' -% ANprobOutput=ANprobRateOutput*dt; -% [r nEpochs]=size(ANprobOutput); -% % find probability of no spikes in refractory period -% pNoSpikesInRefrac=ones(size(ANprobOutput)); -% pSpike=zeros(size(ANprobOutput)); -% for epochNo=lengthAbsRefractoryP+2:nEpochs -% pNoSpikesInRefrac(:,epochNo)=... -% pNoSpikesInRefrac(:,epochNo-2)... -% .*(1-pSpike(:,epochNo-1))... -% ./(1-pSpike(:,epochNo-lengthAbsRefractoryP-1)); -% pSpike(:,epochNo)= ANprobOutput(:,epochNo)... -% .*pNoSpikesInRefrac(:,epochNo); -% end -% ANprobRateOutput=pSpike/dt; -% end - -path(restorePath)
--- a/MAP/old MAP files/MAP1_14parallel.m Thu Oct 06 15:43:20 2011 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1148 +0,0 @@ - -function MAP1_14parallel(inputSignal, sampleRate, BFlist, MAPparamsName, ... - AN_spikesOrProbability, paramChanges) -% To test this function use test_MAP1_14 in this folder -% -% All arguments are mandatory. -% -% BFlist is a list of BFs but can be '-1' to allow MAPparams to choose -% - -% MAPparamsName='Normal'; % source of model parameters -% AN_spikesOrProbability='spikes'; % or 'probability' -% paramChanges is a cell array of strings that can be used to make last -% minute parameter changes, e.g., to simulate OHC loss -% paramChanges{1}= 'DRNLParams.a=0;'; - -% The model parameters are established in the MAPparams<***> file -% and stored as global - -restorePath=path; -addpath (['..' filesep 'parameterStore']) - -global OMEParams DRNLParams IHC_cilia_RPParams IHCpreSynapseParams -global AN_IHCsynapseParams MacGregorParams MacGregorMultiParams - -% All of the results of this function are stored as global -global dt ANdt savedBFlist saveAN_spikesOrProbability saveMAPparamsName... - savedInputSignal OMEextEarPressure TMoutput OMEoutput ARattenuation ... - DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... - IHCoutput ANprobRateOutput ANoutput savePavailable tauCas ... - CNoutput ICoutput ICmembraneOutput ICfiberTypeRates MOCattenuation - -% Normally only ICoutput(logical spike matrix) or ANprobRateOutput will be -% needed by the user; so the following will suffice -% global ANdt ICoutput ANprobRateOutput - -% Note that sampleRate has not changed from the original function call and -% ANprobRateOutput is sampled at this rate -% However ANoutput, CNoutput and IC output are stored as logical -% 'spike' matrices using a lower sample rate (see ANdt). - -% When AN_spikesOrProbability is set to probability, -% no spike matrices are computed. -% When AN_spikesOrProbability is set to 'spikes', -% no probability output is computed - -% Efferent control variables are ARattenuation and MOCattenuation -% These are scalars between 1 (no attenuation) and 0. -% They are represented with dt=1/sampleRate (not ANdt) -% They are computed using either AN probability rate output -% or IC (spikes) output as approrpriate. -% AR is computed using across channel activity -% MOC is computed on a within-channel basis. - - -% save as global for later plotting if required -savedBFlist=BFlist; -saveAN_spikesOrProbability=AN_spikesOrProbability; -saveMAPparamsName=MAPparamsName; - -% Read parameters from MAPparams<***> file in 'parameterStore' folder -cmd=['method=MAPparams' MAPparamsName ... - '(BFlist, sampleRate, 0);']; -eval(cmd); - -% Beware, 'BFlist=-1' is a legitimate argument for MAPparams<> -% if the calling program allows MAPparams to specify the list -BFlist=DRNLParams.nonlinCFs; - -% now accept last mintue parameter changes required by the calling program -if nargin>5 && ~isempty(paramChanges) - nChanges=length(paramChanges); - for idx=1:nChanges - eval(paramChanges{idx}) - end -end - -dt=1/sampleRate; -duration=length(inputSignal)/sampleRate; -% segmentDuration is specified in parameter file (must be >efferent delay) -segmentDuration=method.segmentDuration; -segmentLength=round(segmentDuration/ dt); -segmentTime=dt*(1:segmentLength); % used in debugging plots - -% all spiking activity is computed using longer epochs -ANspeedUpFactor=5; % 5 times longer - -% inputSignal must be row vector -[r c]=size(inputSignal); -if r>c, inputSignal=inputSignal'; end % transpose -% ignore stereo signals -inputSignal=inputSignal(1,:); % drop any second channel -savedInputSignal=inputSignal; - -% Segment the signal -% The sgment length is given but the signal length must be adjusted to be a -% multiple of both the segment length and the reduced segmentlength -[nSignalRows signalLength]=size(inputSignal); -segmentLength=ceil(segmentLength/ANspeedUpFactor)*ANspeedUpFactor; -% Make the signal length a whole multiple of the segment length -nSignalSegments=ceil(signalLength/segmentLength); -padSize=nSignalSegments*segmentLength-signalLength; -pad=zeros(nSignalRows,padSize); -inputSignal=[inputSignal pad]; -[ignore signalLength]=size(inputSignal); - -% AN (spikes) is computed at a lower sample rate when spikes required -% so it has a reduced segment length (see 'ANspeeUpFactor' above) -% AN CN and IC all use this sample interval -ANdt=dt*ANspeedUpFactor; -reducedSegmentLength=round(segmentLength/ANspeedUpFactor); -reducedSignalLength= round(signalLength/ANspeedUpFactor); - -%% Initialise with respect to each stage before computing -% by allocating memory, -% by computing constants -% by establishing easy to read variable names -% The computations are made in segments and boundary conditions must -% be established and stored. These are found in variables with -% 'boundary' or 'bndry' in the name - -%% OME --- -% external ear resonances -OMEexternalResonanceFilters=OMEParams.externalResonanceFilters; -[nOMEExtFilters c]=size(OMEexternalResonanceFilters); -% details of external (outer ear) resonances -OMEgaindBs=OMEexternalResonanceFilters(:,1); -OMEgainScalars=10.^(OMEgaindBs/20); -OMEfilterOrder=OMEexternalResonanceFilters(:,2); -OMElowerCutOff=OMEexternalResonanceFilters(:,3); -OMEupperCutOff=OMEexternalResonanceFilters(:,4); -% external resonance coefficients -ExtFilter_b=cell(nOMEExtFilters,1); -ExtFilter_a=cell(nOMEExtFilters,1); -for idx=1:nOMEExtFilters - Nyquist=sampleRate/2; - [b, a] = butter(OMEfilterOrder(idx), ... - [OMElowerCutOff(idx) OMEupperCutOff(idx)]... - /Nyquist); - ExtFilter_b{idx}=b; - ExtFilter_a{idx}=a; -end -OMEExtFilterBndry=cell(2,1); -OMEextEarPressure=zeros(1,signalLength); % pressure at tympanic membrane - -% pressure to velocity conversion using smoothing filter (50 Hz cutoff) -tau=1/(2*pi*50); -a1=dt/tau-1; a0=1; -b0=1+ a1; -TMdisp_b=b0; TMdisp_a=[a0 a1]; -% figure(9), freqz(TMdisp_b, TMdisp_a) -OME_TMdisplacementBndry=[]; - -% OME high pass (simulates poor low frequency stapes response) -OMEhighPassHighCutOff=OMEParams.OMEstapesLPcutoff; -Nyquist=sampleRate/2; -[stapesDisp_b,stapesDisp_a] = butter(1, OMEhighPassHighCutOff/Nyquist, 'high'); -% figure(10), freqz(stapesDisp_b, stapesDisp_a) - -OMEhighPassBndry=[]; - -% OMEampStapes might be reducdant (use OMEParams.stapesScalar) -stapesScalar= OMEParams.stapesScalar; - -% Acoustic reflex -efferentDelayPts=round(OMEParams.ARdelay/dt); -% smoothing filter -% Nyquist=(1/ANdt)/2; -% [ARfilt_b,ARfilt_a] = butter(1, (1/(2*pi*OMEParams.ARtau))/Nyquist, 'low'); -a1=dt/OMEParams.ARtau-1; a0=1; -b0=1+ a1; -ARfilt_b=b0; ARfilt_a=[a0 a1]; - -ARattenuation=ones(1,signalLength); -ARrateThreshold=OMEParams.ARrateThreshold; % may not be used -ARrateToAttenuationFactor=OMEParams.rateToAttenuationFactor; -ARrateToAttenuationFactorProb=OMEParams.rateToAttenuationFactorProb; -ARboundary=[]; -ARboundaryProb=0; - -% save complete OME record (stapes displacement) -OMEoutput=zeros(1,signalLength); -TMoutput=zeros(1,signalLength); - -%% BM --- -% BM is represented as a list of locations identified by BF -DRNL_BFs=BFlist; -nBFs= length(DRNL_BFs); - -% DRNLchannelParameters=DRNLParams.channelParameters; -DRNLresponse= zeros(nBFs, segmentLength); - -MOCrateToAttenuationFactor=DRNLParams.rateToAttenuationFactor; -rateToAttenuationFactorProb=DRNLParams.rateToAttenuationFactorProb; -MOCrateThresholdProb=DRNLParams.MOCrateThresholdProb; - -% smoothing filter for MOC -% Nyquist=(1/ANdt)/2; -% [MOCfilt_b,MOCfilt_a] = ... -% butter(1, (1/(2*pi*DRNLParams.MOCtau))/Nyquist, 'low'); -% figure(10), freqz(stapesDisp_b, stapesDisp_a) -a1=dt/DRNLParams.MOCtau-1; a0=1; -b0=1+ a1; -MOCfilt_b=b0; MOCfilt_a=[a0 a1]; -% figure(9), freqz(stapesDisp_b, stapesDisp_a) -MOCboundary=cell(nBFs,1); -MOCprobBoundary=cell(nBFs,1); - -MOCattSegment=zeros(nBFs,reducedSegmentLength); -MOCattenuation=ones(nBFs,signalLength); - -if DRNLParams.a>0 - DRNLcompressionThreshold=10^((1/(1-DRNLParams.c))* ... - log10(DRNLParams.b/DRNLParams.a)); -else - DRNLcompressionThreshold=inf; -end - -DRNLlinearOrder= DRNLParams.linOrder; -DRNLnonlinearOrder= DRNLParams.nonlinOrder; - -DRNLa=DRNLParams.a; -DRNLb=DRNLParams.b; -DRNLc=DRNLParams.c; -linGAIN=DRNLParams.g; -% -% gammatone filter coefficients for linear pathway -bw=DRNLParams.linBWs'; -phi = 2 * pi * bw * dt; -cf=DRNLParams.linCFs'; -theta = 2 * pi * cf * dt; -cos_theta = cos(theta); -sin_theta = sin(theta); -alpha = -exp(-phi).* cos_theta; -b0 = ones(nBFs,1); -b1 = 2 * alpha; -b2 = exp(-2 * phi); -z1 = (1 + alpha .* cos_theta) - (alpha .* sin_theta) * i; -z2 = (1 + b1 .* cos_theta) - (b1 .* sin_theta) * i; -z3 = (b2 .* cos(2 * theta)) - (b2 .* sin(2 * theta)) * i; -tf = (z2 + z3) ./ z1; -a0 = abs(tf); -a1 = alpha .* a0; -GTlin_a = [b0, b1, b2]; -GTlin_b = [a0, a1]; -GTlinOrder=DRNLlinearOrder; -GTlinBdry1=cell(nBFs); -GTlinBdry2=cell(nBFs); -GTlinBdry3=cell(nBFs); -GTlinBdry1out=cell(nBFs); -GTlinBdry2out=cell(nBFs); -GTlinBdry3out=cell(nBFs); - -% nonlinear gammatone filter coefficients -bw=DRNLParams.nlBWs'; -phi = 2 * pi * bw * dt; -cf=DRNLParams.nonlinCFs'; -theta = 2 * pi * cf * dt; -cos_theta = cos(theta); -sin_theta = sin(theta); -alpha = -exp(-phi).* cos_theta; -b0 = ones(nBFs,1); -b1 = 2 * alpha; -b2 = exp(-2 * phi); -z1 = (1 + alpha .* cos_theta) - (alpha .* sin_theta) * i; -z2 = (1 + b1 .* cos_theta) - (b1 .* sin_theta) * i; -z3 = (b2 .* cos(2 * theta)) - (b2 .* sin(2 * theta)) * i; -tf = (z2 + z3) ./ z1; -a0 = abs(tf); -a1 = alpha .* a0; -GTnonlin_a = [b0, b1, b2]; -GTnonlin_b = [a0, a1]; -GTnonlinOrder=DRNLnonlinearOrder; -firstGTnonlinBdry1=cell(nBFs); -firstGTnonlinBdry2=cell(nBFs); -firstGTnonlinBdry3=cell(nBFs); -firstGTnonlinBdry1out=cell(nBFs); -firstGTnonlinBdry2out=cell(nBFs); -firstGTnonlinBdry3out=cell(nBFs); - -secondGTnonlinBdry1=cell(nBFs); -secondGTnonlinBdry2=cell(nBFs); -secondGTnonlinBdry3=cell(nBFs); -secondGTnonlinBdry1out=cell(nBFs); -secondGTnonlinBdry2out=cell(nBFs); -secondGTnonlinBdry3out=cell(nBFs); - -% complete BM record (BM displacement) -DRNLoutput=zeros(nBFs, signalLength); - - -%% IHC --- -% IHC cilia activity and receptor potential -% viscous coupling between BM and stereocilia displacement -% Nyquist=sampleRate/2; -% IHCcutoff=1/(2*pi*IHC_cilia_RPParams.tc); -% [IHCciliaFilter_b,IHCciliaFilter_a]=... -% butter(1, IHCcutoff/Nyquist, 'high'); -a1=dt/IHC_cilia_RPParams.tc-1; a0=1; -b0=1+ a1; -% high pass (i.e. low pass reversed) -IHCciliaFilter_b=[a0 a1]; IHCciliaFilter_a=b0; -% figure(9), freqz(IHCciliaFilter_b, IHCciliaFilter_a) - -IHCciliaBndry=cell(nBFs,1); - -% IHC apical conductance (Boltzman function) -IHC_C= IHC_cilia_RPParams.C; -IHCu0= IHC_cilia_RPParams.u0; -IHCu1= IHC_cilia_RPParams.u1; -IHCs0= IHC_cilia_RPParams.s0; -IHCs1= IHC_cilia_RPParams.s1; -IHCGmax= IHC_cilia_RPParams.Gmax; -IHCGa= IHC_cilia_RPParams.Ga; % (leakage) - -IHCGu0 = IHCGa+IHCGmax./(1+exp(IHCu0/IHCs0).*(1+exp(IHCu1/IHCs1))); - -% Receptor potential -IHC_Cab= IHC_cilia_RPParams.Cab; -IHC_Gk= IHC_cilia_RPParams.Gk; -IHC_Et= IHC_cilia_RPParams.Et; -IHC_Ek= IHC_cilia_RPParams.Ek; -IHC_Ekp= IHC_Ek+IHC_Et*IHC_cilia_RPParams.Rpc; - -IHCrestingV= (IHC_Gk*IHC_Ekp+IHCGu0*IHC_Et)/(IHCGu0+IHC_Gk); - -IHC_Vnow= IHCrestingV*ones(nBFs,1); % initial voltage -IHC_RP= zeros(nBFs,segmentLength); - -% complete record of IHC receptor potential (V) -IHCciliaDisplacement= zeros(nBFs,segmentLength); -IHCoutput= zeros(nBFs,signalLength); -IHC_cilia_output= zeros(nBFs,signalLength); - - -%% pre-synapse --- -% Each BF is replicated using a different fiber type to make a 'channel' -% The number of channels is nBFs x nANfiberTypes -% Fiber types are specified in terms of tauCa -nANfiberTypes= length(IHCpreSynapseParams.tauCa); -tauCas= IHCpreSynapseParams.tauCa; -nChannels= nANfiberTypes*nBFs; -synapticCa= zeros(nChannels,segmentLength); - -% Calcium control (more calcium, greater release rate) -ECa=IHCpreSynapseParams.ECa; -gamma=IHCpreSynapseParams.gamma; -beta=IHCpreSynapseParams.beta; -tauM=IHCpreSynapseParams.tauM; -mICa=zeros(nChannels,segmentLength); -GmaxCa=IHCpreSynapseParams.GmaxCa; -synapse_z= IHCpreSynapseParams.z; -synapse_power=IHCpreSynapseParams.power; - -% tauCa vector is established across channels to allow vectorization -% (one tauCa per channel). Do not confuse with tauCas (one pre fiber type) -tauCa=repmat(tauCas, nBFs,1); -tauCa=reshape(tauCa, nChannels, 1); - -% presynapse startup values (vectors, length:nChannels) -% proportion (0 - 1) of Ca channels open at IHCrestingV -mICaCurrent=((1+beta^-1 * exp(-gamma*IHCrestingV))^-1)... - *ones(nBFs*nANfiberTypes,1); -% corresponding startup currents -ICaCurrent= (GmaxCa*mICaCurrent.^3) * (IHCrestingV-ECa); -CaCurrent= ICaCurrent.*tauCa; - -% vesicle release rate at startup (one per channel) -% kt0 is used only at initialisation -kt0= -synapse_z * CaCurrent.^synapse_power; - - -%% AN --- -% each row of the AN matrices represents one AN fiber -% The results computed either for probabiities *or* for spikes (not both) -% Spikes are necessary if CN and IC are to be computed -nFibersPerChannel= AN_IHCsynapseParams.numFibers; -nANfibers= nChannels*nFibersPerChannel; - -y=AN_IHCsynapseParams.y; -l=AN_IHCsynapseParams.l; -x=AN_IHCsynapseParams.x; -r=AN_IHCsynapseParams.r; -M=round(AN_IHCsynapseParams.M); - -% probability (NB initial 'P' on everything) -PAN_ydt = repmat(AN_IHCsynapseParams.y*dt, nChannels,1); -PAN_ldt = repmat(AN_IHCsynapseParams.l*dt, nChannels,1); -PAN_xdt = repmat(AN_IHCsynapseParams.x*dt, nChannels,1); -PAN_rdt = repmat(AN_IHCsynapseParams.r*dt, nChannels,1); -PAN_rdt_plus_ldt = PAN_rdt + PAN_ldt; -PAN_M=round(AN_IHCsynapseParams.M); - -% compute starting values -Pcleft = kt0* y* M ./ (y*(l+r)+ kt0* l); -Pavailable = Pcleft*(l+r)./kt0; -Preprocess = Pcleft*r/x; % canbe fractional - -ANprobability=zeros(nChannels,segmentLength); -ANprobRateOutput=zeros(nChannels,signalLength); -% special variables for monitoring synaptic cleft (specialists only) -savePavailableSeg=zeros(nChannels,segmentLength); -savePavailable=zeros(nChannels,signalLength); - -% spikes % ! ! ! ! ! ! ! ! -AN_refractory_period= AN_IHCsynapseParams.refractory_period; -lengthAbsRefractory= round(AN_refractory_period/ANdt); - -AN_ydt= repmat(AN_IHCsynapseParams.y*ANdt, nANfibers,1); -AN_ldt= repmat(AN_IHCsynapseParams.l*ANdt, nANfibers,1); -AN_xdt= repmat(AN_IHCsynapseParams.x*ANdt, nANfibers,1); -AN_rdt= repmat(AN_IHCsynapseParams.r*ANdt, nANfibers,1); -AN_rdt_plus_ldt= AN_rdt + AN_ldt; -AN_M= round(AN_IHCsynapseParams.M); - -% kt0 is initial release rate -% Establish as a vector (length=channel x number of fibers) -kt0= repmat(kt0', nFibersPerChannel, 1); -kt0=reshape(kt0, nANfibers,1); - -% starting values for reservoirs -AN_cleft = kt0* y* M ./ (y*(l+r)+ kt0* l); -AN_available = round(AN_cleft*(l+r)./kt0); %must be integer -AN_reprocess = AN_cleft*r/x; - -% output is in a logical array spikes = 1/ 0. -ANspikes= false(nANfibers,reducedSegmentLength); -ANoutput= false(nANfibers,reducedSignalLength); - - -%% CN (first brain stem nucleus - could be any subdivision of CN) -% Input to a CN neuorn is a random selection of AN fibers within a channel -% The number of AN fibers used is ANfibersFanInToCN -ANfibersFanInToCN=MacGregorMultiParams.fibersPerNeuron; -nCNneuronsPerChannel=MacGregorMultiParams.nNeuronsPerBF; -% CNtauGk (Potassium time constant) determines the rate of firing of -% the unit when driven hard by a DC input (not normally >350 sp/s) -CNtauGk=MacGregorMultiParams.tauGk; -ANavailableFibersPerChan=AN_IHCsynapseParams.numFibers; -nCNneurons=nCNneuronsPerChannel*nChannels; -% nCNneuronsPerFiberType= nCNneurons/nANfiberTypes; - -CNmembranePotential=zeros(nCNneurons,reducedSegmentLength); - -% establish which ANfibers (by name) feed into which CN nuerons -CNinputfiberLists=zeros(nChannels*nCNneuronsPerChannel, ANfibersFanInToCN); -unitNo=1; -for ch=1:nChannels - % Each channel contains a number of units =length(listOfFanInValues) - for idx=1:nCNneuronsPerChannel - fibersUsed=(ch-1)*ANavailableFibersPerChan + ... - ceil(rand(1,ANfibersFanInToCN)* ANavailableFibersPerChan); - CNinputfiberLists(unitNo,:)=fibersUsed; - unitNo=unitNo+1; - end -end - -% input to CN units -AN_PSTH=zeros(nCNneurons,reducedSegmentLength); - -% Generate CNalphaFunction function -% by which spikes are converted to post-synaptic currents -CNdendriteLPfreq= MacGregorMultiParams.dendriteLPfreq; -CNcurrentPerSpike=MacGregorMultiParams.currentPerSpike; -CNspikeToCurrentTau=1/(2*pi*CNdendriteLPfreq); -t=ANdt:ANdt:5*CNspikeToCurrentTau; -CNalphaFunction=... - (CNcurrentPerSpike/CNspikeToCurrentTau)*t.*exp(-t/CNspikeToCurrentTau); -% figure(98), plot(t,CNalphaFunction) -% working memory for implementing convolution -CNcurrentTemp=... - zeros(nCNneurons,reducedSegmentLength+length(CNalphaFunction)-1); -% trailing alphas are parts of humps carried forward to the next segment -CNtrailingAlphas=zeros(nCNneurons,length(CNalphaFunction)); - -CN_tauM=MacGregorMultiParams.tauM; -CN_tauTh=MacGregorMultiParams.tauTh; -CN_cap=MacGregorMultiParams.Cap; -CN_c=MacGregorMultiParams.c; -CN_b=MacGregorMultiParams.dGkSpike; -CN_Ek=MacGregorMultiParams.Ek; -CN_Eb= MacGregorMultiParams.Eb; -CN_Er=MacGregorMultiParams.Er; -CN_Th0= MacGregorMultiParams.Th0; -CN_E= zeros(nCNneurons,1); -CN_Gk= zeros(nCNneurons,1); -CN_Th= MacGregorMultiParams.Th0*ones(nCNneurons,1); -CN_Eb=CN_Eb.*ones(nCNneurons,1); -CN_Er=CN_Er.*ones(nCNneurons,1); -CNtimeSinceLastSpike=zeros(nCNneurons,1); -% tauGk is the main distinction between neurons -% in fact they are all the same in the standard model -tauGk=repmat(CNtauGk,nChannels*nCNneuronsPerChannel,1); - -CN_PSTH=zeros(nChannels,reducedSegmentLength); -CNoutput=false(nCNneurons,reducedSignalLength); - - -%% MacGregor (IC - second nucleus) -------- -nICcells=nChannels; % one cell per channel - -ICspikeWidth=0.00015; % this may need revisiting -epochsPerSpike=round(ICspikeWidth/ANdt); -if epochsPerSpike<1 - error(['MacGregorMulti: sample rate too low to support ' ... - num2str(ICspikeWidth*1e6) ' microsec spikes']); -end - -% short names -IC_tauM=MacGregorParams.tauM; -IC_tauGk=MacGregorParams.tauGk; -IC_tauTh=MacGregorParams.tauTh; -IC_cap=MacGregorParams.Cap; -IC_c=MacGregorParams.c; -IC_b=MacGregorParams.dGkSpike; -IC_Th0=MacGregorParams.Th0; -IC_Ek=MacGregorParams.Ek; -IC_Eb= MacGregorParams.Eb; -IC_Er=MacGregorParams.Er; - -IC_E=zeros(nICcells,1); -IC_Gk=zeros(nICcells,1); -IC_Th=IC_Th0*ones(nICcells,1); - -% Dendritic filtering, all spikes are replaced by CNalphaFunction functions -ICdendriteLPfreq= MacGregorParams.dendriteLPfreq; -ICcurrentPerSpike=MacGregorParams.currentPerSpike; -ICspikeToCurrentTau=1/(2*pi*ICdendriteLPfreq); -t=ANdt:ANdt:3*ICspikeToCurrentTau; -IC_CNalphaFunction= (ICcurrentPerSpike / ... - ICspikeToCurrentTau)*t.*exp(-t / ICspikeToCurrentTau); -% figure(98), plot(t,IC_CNalphaFunction) - -% working space for implementing alpha function -ICcurrentTemp=... - zeros(nICcells,reducedSegmentLength+length(IC_CNalphaFunction)-1); -ICtrailingAlphas=zeros(nICcells, length(IC_CNalphaFunction)); - -ICfiberTypeRates=zeros(nANfiberTypes,reducedSignalLength); -ICoutput=false(nChannels,reducedSignalLength); - -ICmembranePotential=zeros(nICcells,reducedSegmentLength); -ICmembraneOutput=zeros(nICcells,signalLength); - - -%% Main program %% %% %% %% %% %% %% %% %% %% %% %% %% %% - -% Compute the entire model for each segment -segmentStartPTR=1; -reducedSegmentPTR=1; % when sampling rate is reduced -while segmentStartPTR<signalLength - segmentEndPTR=segmentStartPTR+segmentLength-1; - % shorter segments after speed up. - shorterSegmentEndPTR=reducedSegmentPTR+reducedSegmentLength-1; - - iputPressureSegment=inputSignal... - (:,segmentStartPTR:segmentStartPTR+segmentLength-1); - - % segment debugging plots - % figure(98) - % plot(segmentTime,iputPressureSegment), title('signalSegment') - - - % OME ---------------------- - - % OME Stage 1: external resonances. Add to inputSignal pressure wave - y=iputPressureSegment; - for n=1:nOMEExtFilters - % any number of resonances can be used - [x OMEExtFilterBndry{n}] = ... - filter(ExtFilter_b{n},ExtFilter_a{n},... - iputPressureSegment, OMEExtFilterBndry{n}); - x= x* OMEgainScalars(n); - % This is a parallel resonance so add it - y=y+x; - end - iputPressureSegment=y; - OMEextEarPressure(segmentStartPTR:segmentEndPTR)= iputPressureSegment; - - % OME stage 2: convert input pressure (velocity) to - % tympanic membrane(TM) displacement using low pass filter - [TMdisplacementSegment OME_TMdisplacementBndry] = ... - filter(TMdisp_b,TMdisp_a,iputPressureSegment, ... - OME_TMdisplacementBndry); - % and save it - TMoutput(segmentStartPTR:segmentEndPTR)= TMdisplacementSegment; - - % OME stage 3: middle ear high pass effect to simulate stapes inertia - [stapesDisplacement OMEhighPassBndry] = ... - filter(stapesDisp_b,stapesDisp_a,TMdisplacementSegment, ... - OMEhighPassBndry); - - % OME stage 4: apply stapes scalar - stapesDisplacement=stapesDisplacement*stapesScalar; - - % OME stage 5: acoustic reflex stapes attenuation - % Attenuate the TM response using feedback from LSR fiber activity - if segmentStartPTR>efferentDelayPts - stapesDisplacement= stapesDisplacement.*... - ARattenuation(segmentStartPTR-efferentDelayPts:... - segmentEndPTR-efferentDelayPts); - end - - % segment debugging plots - % figure(98) - % plot(segmentTime, stapesDisplacement), title ('stapesDisplacement') - - % and save - OMEoutput(segmentStartPTR:segmentEndPTR)= stapesDisplacement; - - % needed for parallel processing - if segmentStartPTR>efferentDelayPts - MOCatt=MOCattenuation(:, segmentStartPTR-efferentDelayPts:... - segmentEndPTR-efferentDelayPts); - else % no MOC available yet - MOCatt=ones(nBFs, segmentLength); - end - %% BM ------------------------------ - % Each BM location is computed separately - parfor BFno=1:nBFs - - % *linear* path - % repeats used to avoid parallel processin problems - linOutput = stapesDisplacement * linGAIN; % linear gain - [linOutput GTlinBdry1out{BFno}] = ... - filter(GTlin_b(BFno,:), GTlin_a(BFno,:), linOutput, GTlinBdry1{BFno}); - [linOutput GTlinBdry2out{BFno}] = ... - filter(GTlin_b(BFno,:), GTlin_a(BFno,:), linOutput, GTlinBdry2{BFno}); - [linOutput GTlinBdry3out{BFno}] = ... - filter(GTlin_b(BFno,:), GTlin_a(BFno,:), linOutput, GTlinBdry3{BFno}); - - % *nonLinear* path - % efferent attenuation (0 <> 1) - MOC=MOCatt(BFno); - - - % first gammatone filter - [nonlinOutput firstGTnonlinBdry1out{BFno}] = ... - filter(GTnonlin_b(BFno,:), GTnonlin_a(BFno,:), ... - stapesDisplacement, firstGTnonlinBdry1{BFno}); - - [nonlinOutput firstGTnonlinBdry2out{BFno}] = ... - filter(GTnonlin_b(BFno,:), GTnonlin_a(BFno,:), ... - stapesDisplacement, firstGTnonlinBdry2{BFno}); - - [nonlinOutput firstGTnonlinBdry3out{BFno}] = ... - filter(GTnonlin_b(BFno,:), GTnonlin_a(BFno,:), ... - stapesDisplacement, firstGTnonlinBdry3{BFno}); - - % broken stick instantaneous compression - % nonlinear gain is weakend by MOC before applied to BM response - y= nonlinOutput.*(MOC* DRNLa); % linear section. - % compress those parts of the signal above the compression - % threshold - abs_x = abs(nonlinOutput); - idx=find(abs_x>DRNLcompressionThreshold); - if ~isempty(idx)>0 - y(idx)=sign(nonlinOutput(idx)).*... - (DRNLb*abs_x(idx).^DRNLc); - end - nonlinOutput=y; - - % second filter removes distortion products - [nonlinOutput secondGTnonlinBdry1out{BFno}] = ... - filter(GTnonlin_b(BFno,:), GTnonlin_a(BFno,:), ... - nonlinOutput, secondGTnonlinBdry1{BFno}); - - [nonlinOutput secondGTnonlinBdry2out{BFno}] = ... - filter(GTnonlin_b(BFno,:), GTnonlin_a(BFno,:), ... - nonlinOutput, secondGTnonlinBdry2{BFno}); - - [nonlinOutput secondGTnonlinBdry3out{BFno}] = ... - filter(GTnonlin_b(BFno,:), GTnonlin_a(BFno,:), ... - nonlinOutput, secondGTnonlinBdry3{BFno}); - - - % combine the two paths to give the DRNL displacement - DRNLresponse(BFno,:)=linOutput+nonlinOutput; - end % BF - GTlinBdry1=GTlinBdry1out; - GTlinBdry2=GTlinBdry2out; - GTlinBdry3=GTlinBdry3out; - firstGTnonlinBdry1=firstGTnonlinBdry1out; - firstGTnonlinBdry2=firstGTnonlinBdry2out; - firstGTnonlinBdry3=firstGTnonlinBdry3out; - secondGTnonlinBdry1=secondGTnonlinBdry1out; - secondGTnonlinBdry2=secondGTnonlinBdry2out; - secondGTnonlinBdry3=secondGTnonlinBdry3out; - % segment debugging plots - % figure(98) - % if size(DRNLresponse,1)>3 - % imagesc(DRNLresponse) % matrix display - % title('DRNLresponse'); % single or double channel response - % else - % plot(segmentTime, DRNLresponse) - % end - - % and save it - DRNLoutput(:, segmentStartPTR:segmentEndPTR)= DRNLresponse; - - - %% IHC ------------------------------------ - % BM displacement to IHCciliaDisplacement is a high-pass filter - % because of viscous coupling - for idx=1:nBFs - [IHCciliaDisplacement(idx,:) IHCciliaBndry{idx}] = ... - filter(IHCciliaFilter_b,IHCciliaFilter_a, ... - DRNLresponse(idx,:), IHCciliaBndry{idx}); - end - - % apply scalar - IHCciliaDisplacement=IHCciliaDisplacement* IHC_C; - - % and save it - IHC_cilia_output(:,segmentStartPTR:segmentStartPTR+segmentLength-1)=... - IHCciliaDisplacement; - - % compute apical conductance - G=IHCGmax./(1+exp(-(IHCciliaDisplacement-IHCu0)/IHCs0).*... - (1+exp(-(IHCciliaDisplacement-IHCu1)/IHCs1))); - Gu=G + IHCGa; - - % Compute receptor potential - for idx=1:segmentLength - IHC_Vnow=IHC_Vnow+ (-Gu(:, idx).*(IHC_Vnow-IHC_Et)-... - IHC_Gk*(IHC_Vnow-IHC_Ekp))* dt/IHC_Cab; - IHC_RP(:,idx)=IHC_Vnow; - end - - % segment debugging plots - % if size(IHC_RP,1)>3 - % surf(IHC_RP), shading interp, title('IHC_RP') - % else - % plot(segmentTime, IHC_RP) - % end - - % and save it - IHCoutput(:, segmentStartPTR:segmentStartPTR+segmentLength-1)=IHC_RP; - - - %% synapse ----------------------------- - % Compute the vesicle release rate for each fiber type at each BF - % replicate IHC_RP for each fiber type - Vsynapse=repmat(IHC_RP, nANfiberTypes,1); - - % look-up table of target fraction channels open for a given IHC_RP - mICaINF= 1./( 1 + exp(-gamma * Vsynapse) /beta); - % fraction of channel open - apply time constant - for idx=1:segmentLength - % mICaINF is the current 'target' value of mICa - mICaCurrent=mICaCurrent+(mICaINF(:,idx)-mICaCurrent)*dt./tauM; - mICa(:,idx)=mICaCurrent; - end - - ICa= (GmaxCa* mICa.^3) .* (Vsynapse- ECa); - - for idx=1:segmentLength - CaCurrent=CaCurrent + ICa(:,idx)*dt - CaCurrent*dt./tauCa; - synapticCa(:,idx)=CaCurrent; - end - synapticCa=-synapticCa; % treat IHCpreSynapseParams as positive substance - - % NB vesicleReleaseRate is /s and is independent of dt - vesicleReleaseRate = synapse_z * synapticCa.^synapse_power; % rate - - % segment debugging plots - % if size(vesicleReleaseRate,1)>3 - % surf(vesicleReleaseRate), shading interp, title('vesicleReleaseRate') - % else - % plot(segmentTime, vesicleReleaseRate) - % end - - - %% AN - switch AN_spikesOrProbability - case 'probability' - % No refractory effect is applied - for t = 1:segmentLength; - M_Pq=PAN_M-Pavailable; - M_Pq(M_Pq<0)=0; - Preplenish = M_Pq .* PAN_ydt; - Pejected = Pavailable.* vesicleReleaseRate(:,t)*dt; - Preprocessed = M_Pq.*Preprocess.* PAN_xdt; - - ANprobability(:,t)= min(Pejected,1); - reuptakeandlost= PAN_rdt_plus_ldt .* Pcleft; - reuptake= PAN_rdt.* Pcleft; - - Pavailable= Pavailable+ Preplenish- Pejected+ Preprocessed; - Pcleft= Pcleft + Pejected - reuptakeandlost; - Preprocess= Preprocess + reuptake - Preprocessed; - Pavailable(Pavailable<0)=0; - savePavailableSeg(:,t)=Pavailable; % synapse tracking - end - % and save it as *rate* - ANrate=ANprobability/dt; - ANprobRateOutput(:, segmentStartPTR:... - segmentStartPTR+segmentLength-1)= ANrate; - % monitor synapse contents (only sometimes used) - savePavailable(:, segmentStartPTR:segmentStartPTR+segmentLength-1)=... - savePavailableSeg; - - % Estimate efferent effects. ARattenuation (0 <> 1) - % acoustic reflex - ARAttSeg=mean(ANrate(1:nBFs,:),1); %LSR channels are 1:nBF - % smooth - [ARAttSeg, ARboundaryProb] = ... - filter(ARfilt_b, ARfilt_a, ARAttSeg, ARboundaryProb); - ARAttSeg=ARAttSeg-ARrateThreshold; - ARAttSeg(ARAttSeg<0)=0; % prevent negative strengths - ARattenuation(segmentStartPTR:segmentEndPTR)=... - (1-ARrateToAttenuationFactorProb.* ARAttSeg); - - % MOC attenuation - % within-channel HSR response only - HSRbegins=nBFs*(nANfiberTypes-1)+1; - rates=ANrate(HSRbegins:end,:); - for idx=1:nBFs - [smoothedRates, MOCprobBoundary{idx}] = ... - filter(MOCfilt_b, MOCfilt_a, rates(idx,:), ... - MOCprobBoundary{idx}); - smoothedRates=smoothedRates-MOCrateThresholdProb; - smoothedRates(smoothedRates<0)=0; - MOCattenuation(idx,segmentStartPTR:segmentEndPTR)= ... - (1- smoothedRates* rateToAttenuationFactorProb); - end - MOCattenuation(MOCattenuation<0)=0.001; - - - case 'spikes' - ANtimeCount=0; - % implement speed upt - for t = ANspeedUpFactor:ANspeedUpFactor:segmentLength; - ANtimeCount=ANtimeCount+1; - % convert release rate to probabilities - releaseProb=vesicleReleaseRate(:,t)*ANdt; - % releaseProb is the release probability per channel - % but each channel has many synapses - releaseProb=repmat(releaseProb',nFibersPerChannel,1); - releaseProb=reshape(releaseProb, nFibersPerChannel*nChannels,1); - - % AN_available=round(AN_available); % vesicles must be integer, (?needed) - M_q=AN_M- AN_available; % number of missing vesicles - M_q(M_q<0)= 0; % cannot be less than 0 - - % AN_N1 converts probability to discrete events - % it considers each event that might occur - % (how many vesicles might be released) - % and returns a count of how many were released - - % slow line -% probabilities= 1-(1-releaseProb).^AN_available; - probabilities= 1-intpow((1-releaseProb), AN_available); - ejected= probabilities> rand(length(AN_available),1); - - reuptakeandlost = AN_rdt_plus_ldt .* AN_cleft; - reuptake = AN_rdt.* AN_cleft; - - % slow line -% probabilities= 1-(1-AN_reprocess.*AN_xdt).^M_q; - probabilities= 1-intpow((1-AN_reprocess.*AN_xdt), M_q); - reprocessed= probabilities>rand(length(M_q),1); - - % slow line -% probabilities= 1-(1-AN_ydt).^M_q; - probabilities= 1-intpow((1-AN_ydt), M_q); - - replenish= probabilities>rand(length(M_q),1); - - AN_available = AN_available + replenish - ejected ... - + reprocessed; - AN_cleft = AN_cleft + ejected - reuptakeandlost; - AN_reprocess = AN_reprocess + reuptake - reprocessed; - - % ANspikes is logical record of vesicle release events>0 - ANspikes(:, ANtimeCount)= ejected; - end % t - - % zero any events that are preceded by release events ... - % within the refractory period - % The refractory period consist of two periods - % 1) the absolute period where no spikes occur - % 2) a relative period where a spike may occur. This relative - % period is realised as a variable length interval - % where the length is chosen at random - % (esentially a linear ramp up) - - % Andreas has a fix for this - for t = 1:ANtimeCount-2*lengthAbsRefractory; - % identify all spikes across fiber array at time (t) - % idx is a list of channels where spikes occurred - % ?? try sparse matrices? - idx=find(ANspikes(:,t)); - for j=idx % consider each spike - % specify variable refractory period - % between abs and 2*abs refractory period - nPointsRefractory=lengthAbsRefractory+... - round(rand*lengthAbsRefractory); - % disable spike potential for refractory period - % set all values in this range to 0 - ANspikes(j,t+1:t+nPointsRefractory)=0; - end - end %t - - % segment debugging - % plotInstructions.figureNo=98; - % plotInstructions.displaydt=ANdt; - % plotInstructions.numPlots=1; - % plotInstructions.subPlotNo=1; - % UTIL_plotMatrix(ANspikes, plotInstructions); - - % and save it. NB, AN is now on 'speedUp' time - ANoutput(:, reducedSegmentPTR: shorterSegmentEndPTR)=ANspikes; - - - %% CN Macgregor first neucleus ------------------------------- - % input is from AN so ANdt is used throughout - % Each CNneuron has a unique set of input fibers selected - % at random from the available AN fibers (CNinputfiberLists) - - % Create the dendritic current for that neuron - % First get input spikes to this neuron - synapseNo=1; - for ch=1:nChannels - for idx=1:nCNneuronsPerChannel - % determine candidate fibers for this unit - fibersUsed=CNinputfiberLists(synapseNo,:); - % ANpsth has a bin width of dt - % (just a simple sum across fibers) - AN_PSTH(synapseNo,:) = ... - sum(ANspikes(fibersUsed,:), 1); - synapseNo=synapseNo+1; - end - end - - % One alpha function per spike - [alphaRows alphaCols]=size(CNtrailingAlphas); - - for unitNo=1:nCNneurons - CNcurrentTemp(unitNo,:)= ... - conv(AN_PSTH(unitNo,:),CNalphaFunction); - end - % add post-synaptic current left over from previous segment - CNcurrentTemp(:,1:alphaCols)=... - CNcurrentTemp(:,1:alphaCols)+ CNtrailingAlphas; - - % take post-synaptic current for this segment - CNcurrentInput= CNcurrentTemp(:, 1:reducedSegmentLength); - - % trailingalphas are the ends of the alpha functions that - % spill over into the next segment - CNtrailingAlphas= ... - CNcurrentTemp(:, reducedSegmentLength+1:end); - - if CN_c>0 - % variable threshold condition (slow) - for t=1:reducedSegmentLength - CNtimeSinceLastSpike=CNtimeSinceLastSpike-dts; - s=CN_E>CN_Th & CNtimeSinceLastSpike<0 ; - CNtimeSinceLastSpike(s)=0.0005; % 0.5 ms for sodium spike - dE =(-CN_E/CN_tauM + ... - CNcurrentInput(:,t)/CN_cap+(CN_Gk/CN_cap).*(CN_Ek-CN_E))*dt; - dGk=-CN_Gk*dt./tauGk + CN_b*s; - dTh=-(CN_Th-CN_Th0)*dt/CN_tauTh + CN_c*s; - CN_E=CN_E+dE; - CN_Gk=CN_Gk+dGk; - CN_Th=CN_Th+dTh; - CNmembranePotential(:,t)=CN_E+s.*(CN_Eb-CN_E)+CN_Er; - end - else - % static threshold (faster) - for t=1:reducedSegmentLength - CNtimeSinceLastSpike=CNtimeSinceLastSpike-dt; - s=CN_E>CN_Th0 & CNtimeSinceLastSpike<0 ; % =1 if both conditions met - CNtimeSinceLastSpike(s)=0.0005; % 0.5 ms for sodium spike - dE = (-CN_E/CN_tauM + ... - CNcurrentInput(:,t)/CN_cap+(CN_Gk/CN_cap).*(CN_Ek-CN_E))*dt; - dGk=-CN_Gk*dt./tauGk +CN_b*s; - CN_E=CN_E+dE; - CN_Gk=CN_Gk+dGk; - % add spike to CN_E and add resting potential (-60 mV) - CNmembranePotential(:,t)=CN_E+s.*(CN_Eb-CN_E)+CN_Er; - end - end - - % extract spikes. A spike is a substantial upswing in voltage - CN_spikes=CNmembranePotential> -0.01; - - % now remove any spike that is immediately followed by a spike - % NB 'find' works on columns (whence the transposing) - CN_spikes=CN_spikes'; - idx=find(CN_spikes); - idx=idx(1:end-1); - CN_spikes(idx+1)=0; - CN_spikes=CN_spikes'; - - % segment debugging - % plotInstructions.figureNo=98; - % plotInstructions.displaydt=ANdt; - % plotInstructions.numPlots=1; - % plotInstructions.subPlotNo=1; - % UTIL_plotMatrix(CN_spikes, plotInstructions); - - % and save it - CNoutput(:, reducedSegmentPTR:shorterSegmentEndPTR)=... - CN_spikes; - - - %% IC ---------------------------------------------- - % MacGregor or some other second order neurons - - % combine CN neurons in same channel, i.e. same BF & same tauCa - % to generate inputs to single IC unit - channelNo=0; - for idx=1:nCNneuronsPerChannel:nCNneurons-nCNneuronsPerChannel+1; - channelNo=channelNo+1; - CN_PSTH(channelNo,:)=... - sum(CN_spikes(idx:idx+nCNneuronsPerChannel-1,:)); - end - - [alphaRows alphaCols]=size(ICtrailingAlphas); - for ICneuronNo=1:nICcells - ICcurrentTemp(ICneuronNo,:)= ... - conv(CN_PSTH(ICneuronNo,:), IC_CNalphaFunction); - end - - % add the unused current from the previous convolution - ICcurrentTemp(:,1:alphaCols)=ICcurrentTemp(:,1:alphaCols)... - + ICtrailingAlphas; - % take what is required and keep the trailing part for next time - inputCurrent=ICcurrentTemp(:, 1:reducedSegmentLength); - ICtrailingAlphas=ICcurrentTemp(:, reducedSegmentLength+1:end); - - if IC_c==0 - % faster computation when threshold is stable (C==0) - for t=1:reducedSegmentLength - s=IC_E>IC_Th0; - dE = (-IC_E/IC_tauM + inputCurrent(:,t)/IC_cap +... - (IC_Gk/IC_cap).*(IC_Ek-IC_E))*dt; - dGk=-IC_Gk*dt/IC_tauGk +IC_b*s; - IC_E=IC_E+dE; - IC_Gk=IC_Gk+dGk; - ICmembranePotential(:,t)=IC_E+s.*(IC_Eb-IC_E)+IC_Er; - end - else - % threshold is changing (IC_c>0; e.g. bushy cell) - for t=1:reducedSegmentLength - dE = (-IC_E/IC_tauM + ... - inputCurrent(:,t)/IC_cap + (IC_Gk/IC_cap)... - .*(IC_Ek-IC_E))*dt; - IC_E=IC_E+dE; - s=IC_E>IC_Th; - ICmembranePotential(:,t)=IC_E+s.*(IC_Eb-IC_E)+IC_Er; - dGk=-IC_Gk*dt/IC_tauGk +IC_b*s; - IC_Gk=IC_Gk+dGk; - - % After a spike, the threshold is raised - % otherwise it settles to its baseline - dTh=-(IC_Th-Th0)*dt/IC_tauTh +s*IC_c; - IC_Th=IC_Th+dTh; - end - end - - ICspikes=ICmembranePotential> -0.01; - % now remove any spike that is immediately followed by a spike - % NB 'find' works on columns (whence the transposing) - ICspikes=ICspikes'; - idx=find(ICspikes); - idx=idx(1:end-1); - ICspikes(idx+1)=0; - ICspikes=ICspikes'; - - nCellsPerTau= nICcells/nANfiberTypes; - firstCell=1; - lastCell=nCellsPerTau; - for tauCount=1:nANfiberTypes - % separate rates according to fiber types - ICfiberTypeRates(tauCount, ... - reducedSegmentPTR:shorterSegmentEndPTR)=... - sum(ICspikes(firstCell:lastCell, :))... - /(nCellsPerTau*ANdt); - firstCell=firstCell+nCellsPerTau; - lastCell=lastCell+nCellsPerTau; - end - ICoutput(:, reducedSegmentPTR:shorterSegmentEndPTR)=ICspikes; - - if nBFs==1 % single channel - x= repmat(ICmembranePotential(1,:), ANspeedUpFactor,1); - x= reshape(x,1,segmentLength); - if nANfiberTypes>1 % save HSR and LSR - y= repmat(ICmembranePotential(end,:), ANspeedUpFactor,1); - y= reshape(y,1,segmentLength); - x=[x; y]; - end - ICmembraneOutput(:, segmentStartPTR:segmentEndPTR)= x; - end - - % estimate efferent effects. - % ARis based on LSR units. LSR channels are 1:nBF - if nANfiberTypes>1 % AR is multi-channel only - ARAttSeg=sum(ICspikes(1:nBFs,:),1)/ANdt; - [ARAttSeg, ARboundary] = ... - filter(ARfilt_b, ARfilt_a, ARAttSeg, ARboundary); - ARAttSeg=ARAttSeg-ARrateThreshold; - ARAttSeg(ARAttSeg<0)=0; % prevent negative strengths - % scale up to dt from ANdt - x= repmat(ARAttSeg, ANspeedUpFactor,1); - x=reshape(x,1,segmentLength); - ARattenuation(segmentStartPTR:segmentEndPTR)=... - (1-ARrateToAttenuationFactor* x); - ARattenuation(ARattenuation<0)=0.001; - else - % single channel model; disable AR - ARattenuation(segmentStartPTR:segmentEndPTR)=... - ones(1,segmentLength); - end - - % MOC attenuation using HSR response only - % Separate MOC effect for each BF - HSRbegins=nBFs*(nANfiberTypes-1)+1; - rates=ICspikes(HSRbegins:end,:)/ANdt; - for idx=1:nBFs - [smoothedRates, MOCboundary{idx}] = ... - filter(MOCfilt_b, MOCfilt_a, rates(idx,:), ... - MOCboundary{idx}); - MOCattSegment(idx,:)=smoothedRates; - % expand timescale back to model dt from ANdt - x= repmat(MOCattSegment(idx,:), ANspeedUpFactor,1); - x= reshape(x,1,segmentLength); - MOCattenuation(idx,segmentStartPTR:segmentEndPTR)= ... - (1- MOCrateToAttenuationFactor* x); - end - MOCattenuation(MOCattenuation<0)=0.04; - % segment debugging - % plotInstructions.figureNo=98; - % plotInstructions.displaydt=ANdt; - % plotInstructions.numPlots=1; - % plotInstructions.subPlotNo=1; - % UTIL_plotMatrix(ICspikes, plotInstructions); - - end % AN_spikesOrProbability - segmentStartPTR=segmentStartPTR+segmentLength; - reducedSegmentPTR=reducedSegmentPTR+reducedSegmentLength; - - -end % segment - -path(restorePath)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/MAP/old MAP files/MAPrunner.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,71 @@ +function MAPrunner(MAPparamsName, AN_spikesOrProbability, ... + signalCharacteristics, paramChanges, showMapOptions) + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + + +%% #3 pure tone, harmonic sequence or speech file input +signalType= signalCharacteristics.type; +sampleRate= signalCharacteristics.sampleRate; +duration=signalCharacteristics.duration; % seconds +rampDuration=signalCharacteristics.rampDuration; % raised cosine ramp (seconds) +beginSilence=signalCharacteristics.beginSilence; +endSilence=signalCharacteristics.endSilence; +toneFrequency= signalCharacteristics.toneFrequency; % or a pure tone (Hz) +leveldBSPL=signalCharacteristics.leveldBSPL; + +BFlist=-1; + + +%% Generate stimuli + +switch signalType + case 'tones' + % Create pure tone stimulus + dt=1/sampleRate; % seconds + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + % catch rampTime error + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + intialSilence= zeros(1,round(0.1/dt)); + finalSilence= zeros(1,round(0.2/dt)); + inputSignal= [intialSilence inputSignal' finalSilence]; +end + + +%% run the model + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + +%% the model run is now complete. Now display the results +UTIL_showMAP(showMapOptions, paramChanges) + +path(restorePath) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/multiThreshold 1.46/OHIOtest.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,99 @@ +dbstop if error +path=pathdef; +restorePath=path; +addpath (['..' filesep 'utilities']) % model physiology tests + +globalStimParams.FS=100000; +doPlot=0; + +stim.type='OHIO'; +stim.OHIOtype='OHIOrand'; +stim.phases='sin'; +stim.beginSilence=0; +stim.endSilence=-1; +stim.rampOnDur=.002; +stim.rampOffDur=-1; + +% 1. ‘OHIOabs’ paradigm is a baseline procedure for measuring absolute +% thresholds (in dB SPL) of the single tone with 12 frequencies: +% 1 2 3 4 5 6 7 8 9 10 11 12 +% 494, 663, 870, 1125, 1442, 1838, 2338, 2957, 3725, 4689, 5866, 7334 + +% 2. ‘OHIOtemp’ is for measuring thresholds for temporally integrated +% combinations of 2, 4, 8, and 12 tones presented simultaneously. +% In our experiment, we used 4680Hz frequency. + +% 3. ‘OHIOspec’ is for measuring thresholds for spectrally integrated +% combinations of 2(7335 and 5866Hz), 4(7334, 5866, 4680, and 3725Hz), +% 8(7334, 5866, 4680, 3725, 2957, 2338, 1838, and +% 1442Hz), and +% 12(all 12 frequencies) tones presented simultaneously. + +% 4. ‘OHIOspectemp’ is for measuring thresholds for patterned signals +% differing in both the spectral and temporal domains. +% The frequency conditions are the same as that of ‘OHIOspec’. + +% 5. ‘OHIOrand’ is for measuring thresholds for spectrotemporally varying +% signals with random frequency presentation. + +nTonesList=[2 4 8 12]; +allFreqs=[494, 663, 870, 1125, 1442, 1838, 2338, 2957, 3725, 4689, 5866, 7334]; +absThresholds= 50*ones(1,12); % protem + +for nTones=nTonesList + switch stim.OHIOtype + case ' OHIOabs' + % one tone frequency at a time + stim.frequencies=allFreqs(1); + stim.amplitudesdB=0; + + case 'OHIOrand' + % chose nTones frequencies at random + x=rand(1,12); + [sorted idx]=sort(x); + stim.frequencies=allFreqs(idx(1:nTones)); + stim.amplitudesdB=absThresholds(idx); + + case 'OHIOtemp' + % 4680 Hz repeated nTones times + stim.frequencies=4680*ones(1,nTones); + stim.amplitudesdB=repmat(absThresholds(10),1,nTones); + + case {'OHIOspect', 'OHIOspectemp'} + % nTones frequencies either simulataneously or sequentially + switch nTones + case 2 + stim.frequencies=[7335 5866]; + idx=[12 11]; + stim.amplitudesdB=absThresholds(idx); + case 4 + stim.frequencies=[7334, 5866, 4680, 3725]; + idx=[12:-1:9 ]; + stim.amplitudesdB=absThresholds(idx); + case 8 + stim.frequencies=... + [7334, 5866, 4680, 3725, 2957, 2338, 1838, 1442]; + idx=[12:-1:5 ]; + stim.amplitudesdB=absThresholds(idx); + case 12 + stim.frequencies=allFreqs; + idx=[12:-1:1 ]; + stim.amplitudesdB=absThresholds(idx); + end + end + + switch stim.OHIOtype + case {'OHIOabs', 'OHIOspect'} + stim.toneDuration=.02; + globalStimParams.overallDuration=stim.toneDuration; + otherwise + stim.toneDuration=nTones*0.02; + globalStimParams.overallDuration=stim.toneDuration; + end + + disp(num2str(stim.frequencies)) + + [audio, msg]=stimulusCreate(globalStimParams, stim, doPlot); + wavplay(audio,globalStimParams.FS) +end +path=restorePath;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/multiThreshold 1.46/hs_err_pid8684.log Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,290 @@ +# +# An unexpected error has been detected by Java Runtime Environment: +# +# EXCEPTION_ACCESS_VIOLATION (0xc0000005) at pc=0x0000000031425110, pid=8684, tid=6796 +# +# Java VM: Java HotSpot(TM) 64-Bit Server VM (1.6.0-b105 mixed mode) +# Problematic frame: +# C [awt.dll+0x185110] +# +# If you would like to submit a bug report, please visit: +# http://java.sun.com/webapps/bugreport/crash.jsp +# + +--------------- T H R E A D --------------- + +Current thread (0x0000000030ebc400): JavaThread "AWT-EventQueue-0" [_thread_in_native, id=6796] + +siginfo: ExceptionCode=0xc0000005, reading address 0xffffffffffffffff + +Registers: +EAX=0x8000008463a878df, EBX=0x0000000000000001, ECX=0x000000003099f710, EDX=0x00000000308c01b0 +ESP=0x0000000031a7e520, EBP=0x0000000030dbd350, ESI=0x0000000030ebc590, EDI=0x0000000000000000 +EIP=0x0000000031425110, EFLAGS=0x0000000000010202 + +Top of Stack: (sp=0x0000000031a7e520) +0x0000000031a7e520: 0000000030ebc400 0000000000000000 +0x0000000031a7e530: 0000000031a7e670 0000000015dfcd98 +0x0000000031a7e540: 0000000000000001 0000000031426dd3 +0x0000000031a7e550: 0000000000000001 0000000030dbd350 +0x0000000031a7e560: 0000000030ebc590 0000000000000000 +0x0000000031a7e570: 00000000308c01b0 0000000016a0e600 +0x0000000031a7e580: 0000000000000004 00000000152cd810 +0x0000000031a7e590: 0000000000000000 0000000000000000 +0x0000000031a7e5a0: 0000000000000001 0000000000000102 +0x0000000031a7e5b0: 00000000122a308e 0000000000000001 +0x0000000031a7e5c0: 0000000031a7e648 00000000122afc24 +0x0000000031a7e5d0: 0000000000000000 0000000026b4b368 +0x0000000031a7e5e0: 0000000000000000 0000000015dfcd98 +0x0000000031a7e5f0: 0000000000000001 0000000000000000 +0x0000000031a7e600: 0000000031a7e5f0 0000000031a7e608 +0x0000000031a7e610: 0000000000000000 0000000031a7e670 + +Instructions: (pc=0x0000000031425110) +0x0000000031425100: e8 5b 72 f9 ff 48 8b 0d 04 9a 08 00 48 8b 04 d8 +0x0000000031425110: 48 8b 58 30 e8 67 72 f9 ff 48 8b c3 48 83 c4 20 + + +Stack: [0x0000000031a00000,0x0000000031a80000), sp=0x0000000031a7e520, free space=505k +Native frames: (J=compiled Java code, j=interpreted, Vv=VM code, C=native code) +C [awt.dll+0x185110] + +Java frames: (J=compiled Java code, j=interpreted, Vv=VM code) +j sun.awt.Win32GraphicsConfig.getBounds(I)Ljava/awt/Rectangle;+0 +j sun.awt.Win32GraphicsConfig.getBounds()Ljava/awt/Rectangle;+8 +j com.mathworks.mwswing.WindowUtils.getVirtualScreenBounds()Ljava/awt/Rectangle;+98 +j com.mathworks.mwswing.MJUtilities.getVirtualScreenBounds()Ljava/awt/Rectangle;+0 +j com.mathworks.widgets.tokenmatch.TokenMatchPopup.outOfScreenBounds(Ljava/awt/Rectangle;ZZ)Z+0 +j com.mathworks.widgets.tokenmatch.TokenMatchPopup.startTimerAndShowPopupIfNecessary(Ljavax/swing/text/JTextComponent;[IZILcom/mathworks/widgets/tokenmatch/TokenMatchPopup$TokenMatchListener;Lcom/mathworks/widgets/tokenmatch/TokenMatchPopup$Language;)V+380 +j com.mathworks.widgets.text.MWCaret$DelimiterMatchingImpl.startTimerAndShowPopupIfNecessary(Ljavax/swing/text/JTextComponent;[IZILcom/mathworks/widgets/tokenmatch/TokenMatchPopup$TokenMatchListener;Lcom/mathworks/widgets/tokenmatch/TokenMatchPopup$Language;)V+12 +j com.mathworks.widgets.text.MWCaret.doTokenMatchPopup([IZI)V+59 +j com.mathworks.widgets.text.MWCaret.drawNewMarks(ZZ[II)V+76 +j com.mathworks.widgets.text.MWCaret.updateMatchBrace(ZI)V+359 +j com.mathworks.widgets.text.MWKit.doBraceMatch(Ljavax/swing/text/JTextComponent;Z)V+60 +j com.mathworks.widgets.text.MWKit.access$400(Ljavax/swing/text/JTextComponent;Z)V+2 +j com.mathworks.widgets.text.MWKit$MWUpAction.actionPerformed(Ljava/awt/event/ActionEvent;Ljavax/swing/text/JTextComponent;)V+8 +j org.netbeans.editor.BaseAction.actionPerformed(Ljava/awt/event/ActionEvent;)V+229 +j javax.swing.SwingUtilities.notifyAction(Ljavax/swing/Action;Ljavax/swing/KeyStroke;Ljava/awt/event/KeyEvent;Ljava/lang/Object;I)Z+132 +J javax.swing.JComponent.processKeyBinding(Ljavax/swing/KeyStroke;Ljava/awt/event/KeyEvent;IZ)Z +J javax.swing.JComponent.processKeyBindings(Ljava/awt/event/KeyEvent;Z)Z +j javax.swing.JComponent.processKeyEvent(Ljava/awt/event/KeyEvent;)V+51 +J com.mathworks.widgets.SyntaxTextPaneBase.processKeyEvent(Ljava/awt/event/KeyEvent;)V +j java.awt.Component.processEvent(Ljava/awt/AWTEvent;)V+121 +j java.awt.Container.processEvent(Ljava/awt/AWTEvent;)V+18 +j java.awt.Component.dispatchEventImpl(Ljava/awt/AWTEvent;)V+562 +j java.awt.Container.dispatchEventImpl(Ljava/awt/AWTEvent;)V+42 +J java.awt.DefaultKeyboardFocusManager.preDispatchKeyEvent(Ljava/awt/event/KeyEvent;)Z +j java.awt.DefaultKeyboardFocusManager.typeAheadAssertions(Ljava/awt/Component;Ljava/awt/AWTEvent;)Z+157 +j java.awt.DefaultKeyboardFocusManager.dispatchEvent(Ljava/awt/AWTEvent;)Z+1455 +j java.awt.Component.dispatchEventImpl(Ljava/awt/AWTEvent;)V+128 +j java.awt.Container.dispatchEventImpl(Ljava/awt/AWTEvent;)V+42 +j java.awt.Window.dispatchEventImpl(Ljava/awt/AWTEvent;)V+19 +J java.awt.EventDispatchThread.pumpOneEventForFilters(I)Z +J java.awt.EventDispatchThread.pumpEventsForFilter(ILjava/awt/Conditional;Ljava/awt/EventFilter;)V +j java.awt.EventDispatchThread.pumpEventsForHierarchy(ILjava/awt/Conditional;Ljava/awt/Component;)V+11 +j java.awt.EventDispatchThread.pumpEvents(ILjava/awt/Conditional;)V+4 +j java.awt.EventDispatchThread.pumpEvents(Ljava/awt/Conditional;)V+3 +j java.awt.EventDispatchThread.run()V+9 +v ~StubRoutines::call_stub + +--------------- P R O C E S S --------------- + +Java 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Files\Adobe\AGL;C:\Program Files\MATLAB\R2010b\bin;C:\Program Files\MATLAB\R2010a\bin;C:\Program Files\MATLAB\R2008a\bin;C:\Program Files\MATLAB\R2008a\bin\win64;C:\Program Files (x86)\QuickTime\QTSystem\;C:\Program Files\Microsoft Windows Performance Toolkit\ +USERNAME=rmeddis +OS=Windows_NT +PROCESSOR_IDENTIFIER=Intel64 Family 6 Model 37 Stepping 2, GenuineIntel + + + +--------------- S Y S T E M --------------- + +OS: Windows NT 6.1 Build 7601 Service Pack 1 + +CPU:total 4 em64t ht + +Memory: 4k page, physical 8181592k(5048892k free), swap 16361336k(13057292k free) + +vm_info: Java HotSpot(TM) 64-Bit Server VM (1.6.0-b105) for windows-amd64, built on Nov 29 2006 00:38:01 by "java_re" with unknown MS VC++:1400 +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/multiThreshold 1.46/old files/MTprofile.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile +%created: 17_20hr18_Aug_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [16.4 13.1 8.77 9.38 15.8 21.4]; +x.ShortTone = [18.8 15.1 11.9 13.3 20.7 25]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +37.8 32.6 28.6 40.8 41.7 38 +41.5 36.3 40.1 44.5 44.6 37.5 +48.7 42.6 43 49.4 49.7 41.2 +55.4 45.6 52.2 52.9 52.3 43.2 +62.7 55.5 62.5 52.3 58 45.9 +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +44.3 58.4 66.1 73.5 81.9 82.9 +39.6 46.8 49.9 54 66.5 67.6 +36.8 36 35.1 42.8 49.9 38 +37 32.9 33.1 40.8 41.7 37.3 +34.6 30.6 30.6 41.9 42 38.3 +35.3 31.8 42.7 52.4 61 56.7 +40.5 45.3 61 75.5 85.3 90.5 +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/multiThreshold 1.46/paradigms/paradigmBase.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,73 @@ +function paradigmBase(handles) +global stimulusParameters experiment betweenRuns + +stimulusParameters.subjectSampleRate=44100; % compatible with file input +% stimulusParameters.subjectSampleRate=128000; % compatible with file input + +% assessment method +% {'oneIntervalUpDown', 'MaxLikelihood', '2I2AFC++', '2I2AFC+++'} +experiment.threshEstMethod='oneIntervalUpDown'; +% {'cued', 'noCue'}; +stimulusParameters.includeCue=1; +stimulusParameters.cueTestDifference=10; + +experiment.singleIntervalMaxTrials=10; +experiment.maxTrials=10; +experiment.allowCatchTrials= 1; + +% {'tone','noise', 'pinkNoise','whiteNoise','OHIO'} +stimulusParameters.WRVname='targetLevel'; +stimulusParameters.WRVstartValues=30 ; +stimulusParameters.WRVsteps=[10 2]; +stimulusParameters.WRVlimits=[-30 110]; + +% target variable: slope=1, start going down. +experiment.psyFunSlope=1; +withinRuns.direction='down'; + +betweenRuns.variableName1='targetFrequency'; +betweenRuns.variableList1=1000; +betweenRuns.variableName2='targetDuration'; +betweenRuns.variableList2=0.1 ; +% 'randomize within blocks', 'fixed sequence', 'randomize across blocks' +betweenRuns.randomizeSequence='randomize within blocks'; + +% delay > masker > gap > target + +stimulusParameters.stimulusDelay=0.3; + +% maskerTypes={'tone','noise', 'pinkNoise','TEN','whiteNoise'}; +experiment.maskerInUse=0; +stimulusParameters.maskerType='tone'; +stimulusParameters.maskerPhase='cos'; +stimulusParameters.maskerDuration=0.0; +stimulusParameters.maskerLevel= -50; +stimulusParameters.maskerRelativeFrequency= 1 ; + +stimulusParameters.gapDuration=0.0; + +% targetTypes={'tone','noise', 'pinkNoise','whiteNoise','OHIO'}; +stimulusParameters.targetType='tone'; +stimulusParameters.targetPhase='cos'; %{'sin','cos','alt','rand'} +stimulusParameters.targetFrequency=1000; +stimulusParameters.targetDuration=0.1; +stimulusParameters.OHIOnTones=1; +stimulusParameters.targetLevel=stimulusParameters.WRVstartValues(1); + +stimulusParameters.rampDuration=0.004; + +% forced choice window interval +stimulusParameters.AFCsilenceDuration=0.5; + +% {'none','noise', 'pinkNoise', 'TEN','noiseDich', 'pinkNoiseDich','whiteNoise'} +stimulusParameters.backgroundType='none'; +stimulusParameters.backgroundLevel=-100; + +% instructions to user +% single interval up/down no cue +stimulusParameters.instructions{1}= [{'YES if you hear the tone clearly'}, { }, { 'NO if not (or you are uncertain'}]; +% single interval up/down with cue +stimulusParameters.instructions{2}= [{'count the tones you hear clearly'}, { }, { 'ignore indistinct tones'}]; + +stimulusParameters.numOHIOtones=1; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/multiThreshold 1.46/paradigms/paradigm_OHIOabs.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function paradigm_OHIOabs(handles) +global stimulusParameters experiment betweenRuns + +paradigmBase(handles) % default + +betweenRuns.variableName1='targetFrequency'; +betweenRuns.variableList1=... +[494, 663, 870, 1125, 1442, 1838, 2338, 2957, 3725, 4689, 5866, 7334]; +betweenRuns.variableName2='targetDuration'; +betweenRuns.variableList2= 0.01; + +experiment.maskerInUse=0; + +stimulusParameters.targetFrequency=betweenRuns.variableList1; +stimulusParameters.targetDuration=betweenRuns.variableList2; +stimulusParameters.targetLevel=stimulusParameters.WRVstartValues(1); + +stimulusParameters.WRVstartValues=30; +experiment.singleIntervalMaxTrials=20; + +% forced choice window interval +stimulusParameters.AFCsilenceDuration=0.5; + + +% instructions to user +% single interval up/down no cue +stimulusParameters.instructions{1}=[{'YES if you hear the added click'}, { }, { 'NO if not (or you are uncertain'}]; +% single interval up/down with cue +stimulusParameters.instructions{2}=[{'count how many distinct clicks you hear'},{'ignore the tones'},{' '},... + {'The clicks must be **clearly distinct** to count'}]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/multiThreshold 1.46/paradigms/paradigm_OHIOrand.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,33 @@ +function paradigm_OHIOrand(handles) +global stimulusParameters experiment betweenRuns + +paradigmBase(handles) % default + +betweenRuns.variableName1='OHIOnTones'; +betweenRuns.variableList1=... +[2 4 8 12]; +betweenRuns.variableName2='targetDuration'; +betweenRuns.variableList2= 0.01; + +experiment.maskerInUse=0; + +stimulusParameters.OHIOnTones=betweenRuns.variableList1; +stimulusParameters.targetDuration=betweenRuns.variableList2; +stimulusParameters.targetLevel=stimulusParameters.WRVstartValues(1); + + +stimulusParameters.WRVstartValues=30; +experiment.singleIntervalMaxTrials=20; + + +% forced choice window interval +stimulusParameters.AFCsilenceDuration=0.5; + + +% instructions to user +% single interval up/down no cue +stimulusParameters.instructions{1}=[{'YES if you hear the added click'}, { }, { 'NO if not (or you are uncertain'}]; +% single interval up/down with cue +stimulusParameters.instructions{2}=[{'count how many distinct clicks you hear'},{'ignore the tones'},{' '},... + {'The clicks must be **clearly distinct** to count'}]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/multiThreshold 1.46/paradigms/paradigm_OHIOspect.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,33 @@ +function paradigm_OHIOspect(handles) +global stimulusParameters experiment betweenRuns + +paradigmBase(handles) % default + +betweenRuns.variableName1='OHIOnTones'; +betweenRuns.variableList1=... +[2 4 8 12]; +betweenRuns.variableName2='targetDuration'; +betweenRuns.variableList2= 0.01; + +experiment.maskerInUse=0; + +stimulusParameters.OHIOnTones=betweenRuns.variableList1; +stimulusParameters.targetDuration=betweenRuns.variableList2; +stimulusParameters.targetLevel=stimulusParameters.WRVstartValues(1); + + +stimulusParameters.WRVstartValues=30; +experiment.singleIntervalMaxTrials=20; + + +% forced choice window interval +stimulusParameters.AFCsilenceDuration=0.5; + + +% instructions to user +% single interval up/down no cue +stimulusParameters.instructions{1}=[{'YES if you hear the added click'}, { }, { 'NO if not (or you are uncertain'}]; +% single interval up/down with cue +stimulusParameters.instructions{2}=[{'count how many distinct clicks you hear'},{'ignore the tones'},{' '},... + {'The clicks must be **clearly distinct** to count'}]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/multiThreshold 1.46/paradigms/paradigm_OHIOspectemp.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,33 @@ +function paradigm_OHIOspectemp(handles) +global stimulusParameters experiment betweenRuns + +paradigmBase(handles) % default + +betweenRuns.variableName1='OHIOnTones'; +betweenRuns.variableList1=... +[2 4 8 12]; +betweenRuns.variableName2='targetDuration'; +betweenRuns.variableList2= 0.01; + +experiment.maskerInUse=0; + +stimulusParameters.OHIOnTones=betweenRuns.variableList1; +stimulusParameters.targetDuration=betweenRuns.variableList2; +stimulusParameters.targetLevel=stimulusParameters.WRVstartValues(1); + + +stimulusParameters.WRVstartValues=30; +experiment.singleIntervalMaxTrials=20; + + +% forced choice window interval +stimulusParameters.AFCsilenceDuration=0.5; + + +% instructions to user +% single interval up/down no cue +stimulusParameters.instructions{1}=[{'YES if you hear the added click'}, { }, { 'NO if not (or you are uncertain'}]; +% single interval up/down with cue +stimulusParameters.instructions{2}=[{'count how many distinct clicks you hear'},{'ignore the tones'},{' '},... + {'The clicks must be **clearly distinct** to count'}]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/multiThreshold 1.46/paradigms/paradigm_OHIOtemp.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,33 @@ +function paradigm_OHIOtemp(handles) +global stimulusParameters experiment betweenRuns + +paradigmBase(handles) % default + +betweenRuns.variableName1='OHIOnTones'; +betweenRuns.variableList1=... +[2 4 8 12]; +betweenRuns.variableName2='targetDuration'; +betweenRuns.variableList2= 0.01; + +experiment.maskerInUse=0; + +stimulusParameters.OHIOnTones=betweenRuns.variableList1; +stimulusParameters.targetDuration=betweenRuns.variableList2; +stimulusParameters.targetLevel=stimulusParameters.WRVstartValues(1); + + +stimulusParameters.WRVstartValues=30; +experiment.singleIntervalMaxTrials=20; + + +% forced choice window interval +stimulusParameters.AFCsilenceDuration=0.5; + + +% instructions to user +% single interval up/down no cue +stimulusParameters.instructions{1}=[{'YES if you hear the added click'}, { }, { 'NO if not (or you are uncertain'}]; +% single interval up/down with cue +stimulusParameters.instructions{2}=[{'count how many distinct clicks you hear'},{'ignore the tones'},{' '},... + {'The clicks must be **clearly distinct** to count'}]; +
Binary file multiThreshold 1.46/savedData/statsModel/statsModel 26-Sep-2011 08_44_18.mat has changed
Binary file multiThreshold 1.46/savedData/statsModel/statsModel 26-Sep-2011 08_44_28.mat has changed
--- a/multithreshold 1.46/MTprofile.m Thu Oct 06 15:43:20 2011 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,31 +0,0 @@ -function x = MTprofile -%created: 17_20hr18_Aug_2011 - -x.BFs = [250 500 1000 2000 4000 8000]; - -x.LongTone = [16.4 13.1 8.77 9.38 15.8 21.4]; -x.ShortTone = [18.8 15.1 11.9 13.3 20.7 25]; - -x.Gaps = [0.01 0.03 0.05 0.07 0.09]; -x.TMCFreq = [250 500 1000 2000 4000 8000]; -x.TMC = [ -37.8 32.6 28.6 40.8 41.7 38 -41.5 36.3 40.1 44.5 44.6 37.5 -48.7 42.6 43 49.4 49.7 41.2 -55.4 45.6 52.2 52.9 52.3 43.2 -62.7 55.5 62.5 52.3 58 45.9 -]; -x.TMC = x.TMC'; - -x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; -x.IFMCFreq = [250 500 1000 2000 4000 8000]; -x.IFMCs = [ -44.3 58.4 66.1 73.5 81.9 82.9 -39.6 46.8 49.9 54 66.5 67.6 -36.8 36 35.1 42.8 49.9 38 -37 32.9 33.1 40.8 41.7 37.3 -34.6 30.6 30.6 41.9 42 38.3 -35.3 31.8 42.7 52.4 61 56.7 -40.5 45.3 61 75.5 85.3 90.5 -]; -x.IFMCs = x.IFMCs';
Binary file multithreshold 1.46/documentation/The multiThreshold guide to getting started.doc has changed
--- a/multithreshold 1.46/multiThreshold.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/multiThreshold.m Mon Nov 28 13:34:28 2011 +0000 @@ -46,7 +46,7 @@ % Edit the above text to modify the response to help multiThreshold -% Last Modified by GUIDE v2.5 20-Sep-2011 11:47:22 +% Last Modified by GUIDE v2.5 25-Oct-2011 07:51:14 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; @@ -93,7 +93,7 @@ % Then wait for user action global stimulusParameters experiment betweenRuns global targetTypes maskerTypes backgroundTypes -global variableNames paradigmNames threshEstNames cueNames +global variableNames paradigmNames threshEstNames cueNames betweenRunsVariables % Specify order of fields in main structures % identify as empty values or empty strings only @@ -106,13 +106,14 @@ variableNames={'stimulusDelay','maskerDuration','maskerLevel',... 'maskerRelativeFrequency', 'targetFrequency', 'gapDuration',... 'targetDuration','targetLevel','rampDuration',... - 'cueTestDifference', 'WRVstartValues', 'WRVsteps', 'WRVlimits'}; + 'cueTestDifference', 'WRVstartValues', 'WRVsteps', 'WRVlimits',... + 'OHIOnTones'}; % Variable variables % (names of variable that can changed between runs) betweenRunsVariables={'stimulusDelay','maskerDuration','maskerLevel',... 'maskerRelativeFrequency','targetFrequency', 'gapDuration',... - 'targetDuration','targetLevel','numOHIOtones'}; + 'targetDuration','targetLevel','OHIOnTones'}; % populate the 'between runs variable' menus set(handles.popupmenuVaryParameter1,'string',betweenRunsVariables) set(handles.popupmenuVaryParameter2,'string',betweenRunsVariables) @@ -143,6 +144,8 @@ % Establish available paradigms by scanning paradigms folder paradigmNames= what('paradigms'); paradigmNames= paradigmNames.m; % select m files only +idx=strmatch('paradigm_', paradigmNames); % with 'paradigm_' +paradigmNames=paradigmNames(idx); for i=1:length(paradigmNames) % strip off file extension paradigmNames{i}=paradigmNames{i}(10:end-2); end @@ -183,7 +186,7 @@ '2I2AFC++', '2I2AFC+++'}; set(handles.popupmenuThreshEst, 'string', threshEstNames); experiment.stopCriteria2IFC=[75 3 5]; -experiment.stopCriteriaSI=[20]; +experiment.stopCriteriaSI=20; % ** editBoxes that are only set by hand % music (relative) level, 'tada!' (manual setting only) @@ -191,7 +194,7 @@ set(handles.editMusicLevel,'string','0') % Catch Trial Rate -set(handles.editCatchTrialRate,'string','0.2 0.1 2 '); +set(handles.editCatchTrialRate,'string','0.2 0.1 '); % calibration stimulusParameters.restoreCalibration=7; @@ -205,6 +208,7 @@ % saveData experiment.saveData=1; set(handles.editSaveData,'string',num2str(experiment.saveData)) +set(handles.pushbuttonSave,'visible','off') % printTracks experiment.printTracks=0; @@ -336,6 +340,25 @@ set(handles.editBackgroundLevel,'string', num2str... (stimulusParameters.backgroundLevel)) +% values related to assessment method +switch experiment.threshEstMethod + case {'MaxLikelihood','oneIntervalUpDown'} + set(handles.editstopCriteriaBox, 'string', ... + num2str(experiment.singleIntervalMaxTrials)) + case {'2I2AFC++','2I2AFC+++'} + set(handles.editstopCriteriaBox, 'string', ... + num2str(experiment.stopCriteria2IFC)) + otherwise + error([' aResetPopupMenus: threshEstMethod not recognised -> ' ... + experiment.threshEstMethod]) +end +% assessment method popup may be changed between paradigms +% e.g. SRT must be one interval +x=get(handles.popupmenuThreshEst, 'string'); +set(handles.popupmenuThreshEst, 'value', ... + strmatch(experiment.threshEstMethod, x)); + + % on RUN the sample rate will be picked from the text box % However, MAP overrules the sample rate and sets its own aSetSampleRate(stimulusParameters.subjectSampleRate, handles); @@ -352,7 +375,7 @@ % ------------------------------------------------------ aResetPopupMenus function aResetPopupMenus(handles) global stimulusParameters betweenRuns variableNames -global targetTypes maskerTypes experiment backgroundTypes +global targetTypes maskerTypes experiment backgroundTypes betweenRunsVariables switch experiment.threshEstMethod case {'MaxLikelihood','oneIntervalUpDown'} @@ -375,11 +398,11 @@ %set variables popupmenus as specified in betweenRuns variableParameter1ID=0; variableParameter2ID=0; -for i=1:length(variableNames) - if strcmp(variableNames{i},betweenRuns.variableName1) +for i=1:length(betweenRunsVariables) % variableNames + if strcmp(betweenRunsVariables{i},betweenRuns.variableName1) variableParameter1ID=i; end - if strcmp(variableNames{i},betweenRuns.variableName2) + if strcmp(betweenRunsVariables{i},betweenRuns.variableName2) variableParameter2ID=i; end end @@ -416,6 +439,15 @@ set(handles.edittargetDuration, 'visible', 'on') set(handles.edittargetFrequency, 'visible', 'on') +switch experiment.paradigm(1:3) + case 'OHI' + set(handles.editOHIOnTones, 'visible', 'on') + set(handles.textOHIOnTones, 'visible', 'on') + otherwise + set(handles.editOHIOnTones, 'visible', 'off') + set(handles.textOHIOnTones, 'visible', 'off') +end + switch experiment.ear case {'statsModelLogistic', 'statsModelRareEvent'} set(handles.editStatsModel, 'visible', 'on') @@ -688,6 +720,8 @@ global experiment expGUIhandles stimulusParameters tic expGUIhandles=handles; +set(handles.pushbuttonSave,'visible','off') + set(handles.pushbuttonStop, 'backgroundColor', [.941 .941 .941]) % set(handles.editparamChanges,'visible','off') @@ -718,9 +752,15 @@ % --- Executes on button press in pushbuttonSingleShot. function pushbuttonSingleShot_Callback(hObject, eventdata, handles) -global experiment +global experiment paradigmNames experiment.singleShot=1; +% startup paradigm, 'training' (could be anywhere on the list) +startupParadigm='training'; +idx= find(strcmp(paradigmNames,startupParadigm)); +set(handles.popupmenuParadigm,'value', idx) +aParadigmSelection(handles); + % special test for spontaneous activity x=get(handles.editWRVstartValues, 'string'); y=get(handles.edittargetDuration, 'string'); @@ -740,7 +780,7 @@ % ------------------------------------------aReadAndCheckParameterBoxes function errorMsg=aReadAndCheckParameterBoxes(handles) global experiment stimulusParameters betweenRuns statsModel -global variableNames LevittControl paramChanges +global variableNames LevittControl paramChanges betweenRunsVariables % When the program sets the parameters all should be well % But when the user changes them... @@ -801,7 +841,7 @@ end chosenOption=get(handles.popupmenuVaryParameter1,'value'); -betweenRuns.variableName1=variableNames{chosenOption}; +betweenRuns.variableName1=betweenRunsVariables{chosenOption}; eval(['betweenRuns.variableList1 = stimulusParameters.' ... betweenRuns.variableName1 ';']); @@ -891,6 +931,9 @@ 'catch trial base rates must be less than catch trial start rate'; return, end +% force the decay rate for catchTrialRate +% to avoid having to explain it to the user +stimulusParameters.catchTrialRates(3)=2; % sample rate % The sample rate is set in the paradigm file. @@ -1040,7 +1083,12 @@ % identify model parameter changes if any paramChanges=get(handles.editparamChanges,'string'); if ~strcmp(paramChanges, ';'), paramChanges=[paramChanges ';']; end +try eval(paramChanges); +catch + error('Problems with suggested parameter changes') +end + % -------------------------------------------- aSetSampleRate function aSetSampleRate(sampleRate, handles) @@ -1099,7 +1147,7 @@ stimulusParameters.includeCue=0; % no cue set(handles.popupmenuCueNoCue,'value', 2) - set(handles.editCatchTrialRate,'string','0 0 2 ');%no catch trials + set(handles.editCatchTrialRate,'string','0 0');%no catch trials set(handles.editName,'string', 'Normal') % force name experiment.name=get(handles.editName,'string'); % read name back set(handles.editcalibrationdB,'string','0') @@ -1110,6 +1158,9 @@ set(handles.editSaveData,'string', '0') set(handles.editSubjectFont,'string', '10'); experiment.MacGThreshold=0; % num MacG spikes to exceed threshold + otherwise + set(handles.editCatchTrialRate,'string','0.2 0.1');%no catch trials + end aResetPopupMenus(handles) @@ -1119,19 +1170,22 @@ % ------------------------------------------------------------- cueSetUp function cueSetUp(handles) -global stimulusParameters +global stimulusParameters experiment -chosenOption=get(handles.popupmenuCueNoCue,'value'); -if chosenOption==1 - stimulusParameters.includeCue=1; - set(handles.editcueTestDifference,'visible', 'on') - set(handles.textcueTestDifference,'visible', 'on') - stimulusParameters.subjectText=stimulusParameters.instructions{2}; -else - stimulusParameters.includeCue=0; - set(handles.editcueTestDifference,'visible', 'off') - set(handles.textcueTestDifference,'visible', 'off') - stimulusParameters.subjectText= stimulusParameters.instructions{1}; +switch experiment.threshEstMethod + case {'oneIntervalUpDown', 'MaxLikelihood'} + chosenOption=get(handles.popupmenuCueNoCue,'value'); + if chosenOption==1 + stimulusParameters.includeCue=1; + set(handles.editcueTestDifference,'visible', 'on') + set(handles.textcueTestDifference,'visible', 'on') + stimulusParameters.subjectText=stimulusParameters.instructions{2}; + else + stimulusParameters.includeCue=0; + set(handles.editcueTestDifference,'visible', 'off') + set(handles.textcueTestDifference,'visible', 'off') + stimulusParameters.subjectText= stimulusParameters.instructions{1}; + end end % -------------------------------------------- popupmenuThreshEst_Callback @@ -1761,3 +1815,34 @@ end + + +function pushbuttonSave_Callback(hObject, eventdata, handles) +global experiment + +subjectName=experiment.name; +if ~isdir(['savedData' filesep subjectName ]) + mkdir(['savedData' filesep subjectName ]) +end + +% date and time and replace ':' with '_' +date=datestr(now);idx=findstr(':',date);date(idx)='_'; +fileName=[subjectName ' ' date '.mat']; +movefile(['savedData' filesep 'mostRecentResults.mat'], ... + ['savedData' filesep subjectName filesep fileName]) +set(handles.pushbuttonSave,'visible','off') + + + +function editOHIOnTones_Callback(hObject, eventdata, handles) + + +function editOHIOnTones_CreateFcn(hObject, eventdata, handles) + +% Hint: edit controls usually have a white background on Windows. +% See ISPC and COMPUTER. +if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) + set(hObject,'BackgroundColor','white'); +end + +
--- a/multithreshold 1.46/nextStimulus.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/nextStimulus.m Mon Nov 28 13:34:28 2011 +0000 @@ -2,7 +2,7 @@ % Handles everything concerned with the stimulus presentation % called from startNewRun in subjGUI -global experiment stimulusParameters withinRuns +global experiment stimulusParameters withinRuns betweenRuns experiment.status='presentingStimulus'; errormsg=''; @@ -234,16 +234,133 @@ case {'training','absThreshold', 'absThreshold_8', ... 'TENtest', 'threshold_duration','discomfort',... 'overShoot','overShootB','overShootMB1', ... - 'overShootMB2', 'OHIO','OHIOabs','OHIOspect'} + 'overShootMB2', 'OHIO','OHIOabs','OHIOspect'... + 'OHIOrand', 'OHIOtemp', 'OHIOspectemp'} cueTargetLevel=targetLevel+cueTestDifference; case {'forwardMasking','forwardMaskingD','trainingIFMC', ... - 'TMC','TMC_16ms', 'TMC - ELP', 'IFMC','IFMC_8ms', 'FMreProbe'} + 'TMC','TMC_16ms', 'TMC - ELP', 'IFMC','IFMC_8ms', ... + 'FMreProbe'} % cue masker is weaker to make target more audible cueMaskerLevel=maskerLevel-cueTestDifference; end end +% thresholds (in dB SPL) of the single tone with 12 frequencies: +% 1 2 3 4 5 6 7 8 9 10 11 12 +% 494, 663, 870, 1125, 1442, 1838, 2338, 2957, 3725, 4689, 5866, 7334 + +% 2. ‘OHIOtemp’ is for measuring thresholds for temporally integrated +% combinations of 2, 4, 8, and 12 tones presented simultaneously. +% In our experiment, we used 4680Hz frequency. + +% 3. ‘OHIOspec’ is for measuring thresholds for spectrally integrated +% combinations of 2(7335 and 5866Hz), 4(7334, 5866, 4680, and 3725Hz), +% 8(7334, 5866, 4680, 3725, 2957, 2338, 1838, and +% 1442Hz), and +% 12(all 12 frequencies) tones presented simultaneously. + +% 4. ‘OHIOspectemp’ is for measuring thresholds for patterned signals +% differing in both the spectral and temporal domains. +% The frequency conditions are the same as that of ‘OHIOspec’. + +% 5. ‘OHIOrand’ is for measuring thresholds for spectrotemporally varying +% signals with random frequency presentation. + +switch experiment.paradigm(1:3) + case 'OHI' + targetType='OHIO'; + OHIOtype=experiment.paradigm; + % 1. ‘OHIOabs’ paradigm is a baseline procedure for measuring absolute + + nTones=betweenRuns.var1Sequence(betweenRuns.runNumber); + allFreqs=[494, 663, 870, 1125, 1442, 1838, 2338, 2957, 3725, 4689, 5866, 7334]; + toneLevelBoost= ... + [1 0 0 1 1 4 8 12 12 14 17 19 ]; + + + % for nTones=nTonesList + switch experiment.paradigm + % case ' OHIOabs' + % % one tone frequency at a time + % stim.frequencies=allFreqs(1); + % stim.amplitudesdB=0; + + case 'OHIOrand' + % chose nTones frequencies at random + x=rand(1,12); + [sorted idx]=sort(x); + cueTargetFrequency=allFreqs(idx(1:nTones)); + cueTargetLevel=toneLevelBoost(idx)+... + targetLevel + cueTestDifference; + targetFrequency=allFreqs(idx(1:nTones)); + targetLevel=targetLevel + toneLevelBoost(idx); + + case 'OHIOtemp' + % 4680 Hz repeated nTones times + cueTargetFrequency=4680*ones(1,nTones); + cueTargetLevel=repmat(toneLevelBoost(10),1,nTones)+... + targetLevel + cueTestDifference; + targetFrequency=4680*ones(1,nTones); + targetLevel= targetLevel+repmat(toneLevelBoost(10),1,nTones); + + case {'OHIOspect', 'OHIOspectemp'} + % nTones frequencies either simulataneously or sequentially + switch nTones + case 2 + cueTargetFrequency=[7335 5866]; + targetFrequency=[7335 5866]; + idx=[12 11]; + cueTargetLevel=targetLevel + toneLevelBoost(idx)+cueTestDifference; + targetLevel=targetLevel + toneLevelBoost(idx); + case 4 + cueTargetFrequency=[7334, 5866, 4680, 3725]; + targetFrequency=[7334, 5866, 4680, 3725]; + idx=[12:-1:9 ]; + cueTargetLevel=targetLevel + toneLevelBoost(idx)+cueTestDifference; + targetLevel=targetLevel + toneLevelBoost(idx); + case 8 + cueTargetFrequency=... + [7334, 5866, 4680, 3725, 2957, 2338, 1838, 1442]; + targetFrequency=... + [7334, 5866, 4680, 3725, 2957, 2338, 1838, 1442]; + idx=[12:-1:5 ]; + cueTargetLevel=targetLevel + toneLevelBoost(idx)+cueTestDifference; + targetLevel=targetLevel + toneLevelBoost(idx); + case 12 + cueTargetFrequency=allFreqs; + targetFrequency=allFreqs; + cueTargetLevel=targetLevel + toneLevelBoost(1:12)+cueTestDifference; + targetLevel=targetLevel + toneLevelBoost(1:12); + end + end + + otherwise + OHIOtype='none'; +end + +switch experiment.paradigm(1:3) + case 'OHI' + + switch experiment.threshEstMethod + case {'2I2AFC++', '2I2AFC+++'} + % the cue stimulus (masker + probe) is the 'no' window + % the target stimulus (masker+probe) is the 'yes' window + % the order of presentation is decided at the last minute. + cueTargetLevel=-100; + end + + + switch experiment.paradigm + case {'OHIOabs', 'OHIOspect'} + OHIOtoneDuration=.02+stimulusParameters.stimulusDelay; + globalStimParams.overallDuration=OHIOtoneDuration; + otherwise + OHIOtoneDuration=nTones*0.02+stimulusParameters.stimulusDelay; + globalStimParams.overallDuration=OHIOtoneDuration; + end +end + % ----------------------------- catch trial if withinRuns.catchTrial targetLevel=-100; % no target @@ -370,8 +487,8 @@ return end + case 'gapDuration' % legacy programming - case 'gapDuration' if gapDuration<0 errormsg=['gapDuration (' num2str(gapDuration) ... ') is less than zero ***']; @@ -497,6 +614,20 @@ globalStimParams.nSignalPoints=... round(globalStimParams.overallDuration*globalStimParams.FS); +% special case +switch experiment.paradigm(1:3) + case 'OHI' + switch experiment.paradigm + case {'OHIOabs', 'OHIOspect'} + OHIOtoneDuration=.02+stimulusParameters.stimulusDelay; + globalStimParams.overallDuration=OHIOtoneDuration; + otherwise + OHIOtoneDuration=nTones*0.02+stimulusParameters.stimulusDelay; + globalStimParams.overallDuration=OHIOtoneDuration; + end +end + + % ----------------------------------------------cue stimulus % cue masker componentNo=1; @@ -527,6 +658,7 @@ componentNo=2; precedingSilence=precedingSilence + maskerDuration+cueGapDuration; stimComponents(targetEar,componentNo).type=targetType; +stimComponents(targetEar,componentNo).OHIOtype=OHIOtype; stimComponents(targetEar,componentNo).toneDuration=targetDuration; stimComponents(targetEar,componentNo).frequencies=cueTargetFrequency; stimComponents(targetEar,componentNo).amplitudesdB=cueTargetLevel; @@ -605,6 +737,7 @@ componentNo=2; targetDelay=precedingSilence+ maskerDuration+ gapDuration; stimComponents(targetEar,componentNo).type=targetType; +stimComponents(targetEar,componentNo).OHIOtype=OHIOtype; stimComponents(targetEar,componentNo).toneDuration=targetDuration; stimComponents(targetEar,componentNo).frequencies=targetFrequency; stimComponents(targetEar,componentNo).amplitudesdB=targetLevel; @@ -661,8 +794,14 @@ % case 'SRT' % set(handles.editdigitInput,'visible','off') otherwise - stimulusParameters.testTargetBegins=targetDelay; - stimulusParameters.testTargetEnds=targetDelay+targetDuration; + switch experiment.paradigm(1:3) + case 'OHI' + stimulusParameters.testTargetBegins=0; + stimulusParameters.testTargetEnds=OHIOtoneDuration; + otherwise + stimulusParameters.testTargetBegins=targetDelay; + stimulusParameters.testTargetEnds=targetDelay+targetDuration; + end end % ------------------------------------------------------------- play!
--- a/multithreshold 1.46/old files/MAPmodel.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/old files/MAPmodel.m Mon Nov 28 13:34:28 2011 +0000 @@ -20,7 +20,7 @@ AN_spikesOrProbability='spikes'; % [response, method]=MAPsequenceSeg(audio, method, 1:8); -global ICoutput ANdt +global ICoutput dtSpikes MAP1_14(audio, 1/method.dt, method.nonlinCF,... MAPparamsName, AN_spikesOrProbability); @@ -29,7 +29,7 @@ options.showModelOutput=1; options.printFiringRates=1; options.showACF=0; - options.showEfferent=1; + options.showEfferent=0; UTIL_showMAP(options) end @@ -41,7 +41,7 @@ % ---------------------------------------------------------- end model run -dt=ANdt; +dt=dtSpikes; time=dt:dt:dt*length(MacGregorResponse); % group delay on unit response
--- a/multithreshold 1.46/paradigms/paradigm_IFMC.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/paradigms/paradigm_IFMC.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,7 +1,7 @@ function paradigm_IFMC(handles) global stimulusParameters experiment betweenRuns -paradigm_training(handles) % default +paradigmBase(handles) % default stimulusParameters.WRVname='maskerLevel'; stimulusParameters.WRVstartValues=10;
--- a/multithreshold 1.46/paradigms/paradigm_TENtest.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/paradigms/paradigm_TENtest.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,7 +1,7 @@ function paradigm_TENtest(handles) global stimulusParameters experiment betweenRuns -paradigm_training(handles) % default +paradigmBase(handles) % default stimulusParameters.WRVname='targetLevel'; stimulusParameters.WRVstartValues=40; @@ -29,6 +29,6 @@ % single interval up/down no cue stimulusParameters.instructions{1}=[{'YES if you hear the added click'}, { }, { 'NO if not (or you are uncertain'}]; % single interval up/down with cue -stimulusParameters.instructions{2}=[{'count how many distinct clicks you hear'},{'ignore the tones'},{' '},... +stimulusParameters.instructions{2}=[{'count how many distinct clicks you hear'},{'ignore the noise'},{' '},... {'The clicks must be **clearly distinct** to count'}];
--- a/multithreshold 1.46/paradigms/paradigm_TMC.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/paradigms/paradigm_TMC.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,7 +1,7 @@ function paradigm_TMC(handles) global stimulusParameters experiment betweenRuns -paradigm_training(handles) % default +paradigmBase(handles) % default stimulusParameters.WRVname='maskerLevel'; stimulusParameters.WRVstartValues=50;
--- a/multithreshold 1.46/paradigms/paradigm_absThreshold.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/paradigms/paradigm_absThreshold.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,7 +1,7 @@ function paradigm_absThreshold(handles) global stimulusParameters experiment betweenRuns -paradigm_training(handles) % default +paradigmBase(handles) % default betweenRuns.variableName1='targetFrequency'; betweenRuns.variableList1=[250 500 1000 2000 4000 8000 ];
--- a/multithreshold 1.46/paradigms/paradigm_absThreshold_16.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/paradigms/paradigm_absThreshold_16.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,7 +1,7 @@ function paradigm_absThreshold16(handles) global stimulusParameters experiment betweenRuns -paradigm_training(handles) % default +paradigmBase(handles) % default betweenRuns.variableName1='targetFrequency'; betweenRuns.variableList1=1000;
--- a/multithreshold 1.46/paradigms/paradigm_discomfort.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/paradigms/paradigm_discomfort.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,7 +1,7 @@ function paradigm_discomfort(handles) global stimulusParameters experiment betweenRuns -paradigm_training(handles) % default +paradigmBase(handles) % default stimulusParameters.WRVname='targetLevel'; stimulusParameters.WRVstartValues=75 ;
--- a/multithreshold 1.46/paradigms/paradigm_forwardMasking.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/paradigms/paradigm_forwardMasking.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,7 +1,7 @@ function paradigm_forwardMasking(handles) global stimulusParameters experiment betweenRuns -paradigm_training(handles) % default +paradigmBase(handles) % default stimulusParameters.WRVname='targetLevel'; stimulusParameters.WRVstartValues=50;
--- a/multithreshold 1.46/paradigms/paradigm_overShoot.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/paradigms/paradigm_overShoot.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,7 +1,7 @@ function paradigm_overShoot(handles) global stimulusParameters betweenRuns experiment -paradigm_training(handles) % default +paradigmBase(handles) % default stimulusParameters.WRVname='targetLevel'; stimulusParameters.WRVstartValues=50;
--- a/multithreshold 1.46/paradigms/paradigm_profile.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/paradigms/paradigm_profile.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,7 +1,7 @@ function paradigm_profile(handles) global stimulusParameters experiment betweenRuns -paradigm_training(handles) % default +paradigmBase(handles) % default betweenRuns.variableName1='targetFrequency'; betweenRuns.variableList1=[250 500 1000 2000 4000 8000 ];
--- a/multithreshold 1.46/paradigms/paradigm_psychometric.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/paradigms/paradigm_psychometric.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,7 +1,7 @@ function paradigm_psychometric(handles) global stimulusParameters experiment betweenRuns -paradigm_training(handles) % default +paradigmBase(handles) % default experiment.printTracks=1; experiment.maxTrials=30;
--- a/multithreshold 1.46/paradigms/paradigm_threshold_duration.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/paradigms/paradigm_threshold_duration.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,7 +1,7 @@ function paradigm_threshold_duration(handles) global stimulusParameters experiment betweenRuns -paradigm_training(handles) % default +paradigmBase(handles) % default stimulusParameters.WRVname='targetLevel'; stimulusParameters.WRVstartValues=40;
--- a/multithreshold 1.46/paradigms/paradigm_training.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/paradigms/paradigm_training.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,8 +1,10 @@ function paradigm_training(handles) global stimulusParameters experiment betweenRuns -stimulusParameters.subjectSampleRate=44100; % compatible with file input -stimulusParameters.subjectSampleRate=64000; % compatible with file input +paradigmBase(handles) % default + +% rest may be unnecessary + % stimulusParameters.subjectSampleRate=128000; % compatible with file input % assessment method
--- a/multithreshold 1.46/paradigms/paradigm_trainingIFMC.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/paradigms/paradigm_trainingIFMC.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,7 +1,7 @@ -function paradigm_trainingIFMC(handles) +function paradigmBaseIFMC(handles) global stimulusParameters experiment betweenRuns -paradigm_training(handles) % default +paradigmBase(handles) % default stimulusParameters.WRVname='maskerLevel'; stimulusParameters.WRVstartValues=-10; @@ -16,7 +16,7 @@ betweenRuns.variableList1=[ 0.5 .9 .7 1.3 ]; betweenRuns.variableName2='targetFrequency'; % keep old list of target frequencies -betweenRuns.variableList2=str2num(get(handles.edittargetFrequency,'string')); +betweenRuns.variableList2=[ 2000 ]; experiment.maskerInUse=1; stimulusParameters.maskerType='tone';
--- a/multithreshold 1.46/plotProfile.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/plotProfile.m Mon Nov 28 13:34:28 2011 +0000 @@ -4,7 +4,7 @@ %% plot profile if nargin<1 - fgName = myFile; + fgName = 'profile_JSAN_R'; bgName = ''; end @@ -68,13 +68,14 @@ freq=foreground.MaskerRatio'*foreground.IFMCFreq(BFno); subplot(2,1,2) semilogx(freq,foreground.IFMCs(BFno,:),'r','lineWidth',3), hold on - ylim([0 100]) + ylim([-20 100]) xlim([100 12000]) % grid on end xlabel('frequency (Hz)') ylabel('masker dB / probe dB') set(gca,'XTick',foreground.IFMCFreq) +set(gca,'Ytick', [-20 0 50 100]) if ~isempty(bgName) for BFno=1:length(background.IFMCFreq)
--- a/multithreshold 1.46/printReport.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/printReport.m Mon Nov 28 13:34:28 2011 +0000 @@ -19,6 +19,7 @@ printReportGuide.showPsychometric=0; printReportGuide.HorizontalTracks=1; + if nargin==0 % print new report printReportGuide.showTracks=experiment.printTracks; @@ -180,9 +181,14 @@ disp('Psychometric function') fprintf(' level \tfreq\tprob\n') fprintf('%6.0f\t%6.2f\t%6.0f\n',[levelsBinVector; binFrequencies; psy]) - fprintf('\n') - fprintf('k \t %6.2f\n',logistic.bestK) - fprintf('g \t%7.5f\n',rareEvent.bestGain) + + switch experiment.threshEstMethod + % only one value required for level change + case {'MaxLikelihood', 'oneIntervalUpDown'}; +% fprintf('\n') +% fprintf('k \t %6.2f\n',logistic.bestK) +% fprintf('g \t%7.5f\n',rareEvent.bestGain) + end fprintf('\n') end
--- a/multithreshold 1.46/subjGUI_MT.m Thu Oct 06 15:43:20 2011 +0100 +++ b/multithreshold 1.46/subjGUI_MT.m Mon Nov 28 13:34:28 2011 +0000 @@ -986,11 +986,12 @@ otherwise title([stimulusParameters.WRVname ' = ' ... num2str(withinRuns.variableValue, '%5.1f') ... - '; TH= ' num2str(estThresh, '%5.1f')]) + '; TH= ' num2str(estThresh, '%5.1f') ' dB']) end end xlim([0 experiment.maxTrials+withinRuns.beginningOfPhase2]); ylim(stimulusParameters.WRVlimits) +ylabel ('dB SPL') grid on % Panel 4: Summary of threshold estimates (not used here) @@ -1275,7 +1276,7 @@ end xlimRM([ min(betweenRuns.variableList1) max(betweenRuns.variableList1) ]) ylim(stimulusParameters.WRVlimits) -ylabel('thresholds') +ylabel('thresholds (dB)') xlabel(betweenRuns.variableName1) set(gca,'ytick', [0 20 40 60 80 100]) try @@ -1305,6 +1306,7 @@ % update experimenter GUI addToMsg('Experiment completed.',1) + set(expGUIhandles.pushbuttonSave,'visible','on') printReport experiment.status='endOfExperiment'; return @@ -1433,7 +1435,7 @@ global experiment stimulusParameters audio withinRuns % global outerMiddleEarParams DRNLParams AN_IHCsynapseParams -global ICoutput ANdt dt savedBFlist ANprobRateOutput expGUIhandles +global ICoutput dtSpikes dt savedBFlist ANprobRateOutput expGUIhandles global paramChanges savePath=path; @@ -1479,7 +1481,7 @@ options.showModelOutput=1; options.printFiringRates=1; options.showACF=0; - options.showEfferent=1; + options.showEfferent=0; options.surfProbability=0; showMapOptions.surfSpikes=0; UTIL_showMAP(options) @@ -1493,7 +1495,7 @@ if strcmp(AN_spikesOrProbability,'spikes') MacGregorResponse= sum(ICoutput,1); % use IC - dt=ANdt; + dt=dtSpikes; time=dt:dt:dt*length(MacGregorResponse); else % for one channel, ANprobResponse=ANprobRateOutput @@ -1508,7 +1510,8 @@ windowOnsetDelay= 0.004; windowOffsetDelay= 0.022; % long ringing time -% now find the response of the MacGregor model during the target presentation + group delay +% now find the response of the MacGregor model during +% the target presentation + group delay switch experiment.threshEstMethod case {'2I2AFC++', '2I2AFC+++'} idx= time>stimulusParameters.testTargetBegins+windowOnsetDelay ... @@ -1550,7 +1553,8 @@ num2str([withinRuns.variableValue nSpikesTrueWindow ... nSpikesFalseWindow nSpikesDuringTarget], '%4.0f') ] ) otherwise - % single interval + + % single interval up/down idx=find(time>stimulusParameters.testTargetBegins +windowOnsetDelay... & time<stimulusParameters.testTargetEnds+windowOffsetDelay); if strcmp(AN_spikesOrProbability,'spikes') @@ -1747,7 +1751,9 @@ end try - serobj = serial('COM4') ; % Creating serial port object now its connected to COM4 !!! button boxes in booths are connected to COM2 + % !!! button boxes in booths are connected to COM2. User beware of + % connection port. + serobj = serial('COM2') ; % Creating serial port object serobj.Baudrate = 9600; % Set the baud rate at the specific value set(serobj, 'Parity', 'none') ; % Set parity as none set(serobj, 'Databits', 8) ; % set the number of data bits
--- a/parameterStore/MAPparamsNormal.m Thu Oct 06 15:43:20 2011 +0100 +++ b/parameterStore/MAPparamsNormal.m Mon Nov 28 13:34:28 2011 +0000 @@ -25,7 +25,7 @@ method.segmentDuration=efferentDelay; if nargin<3, showParams=0; end -if nargin<2, sampleRate=50000; end +if nargin<2, sampleRate=44100; end if nargin<1 || BFlist(1)<0 % if BFlist= -1, set BFlist to default lowestBF=250; highestBF= 8000; numChannels=21; % 21 chs (250-8k)includes BFs at 250 500 1000 2000 4000 8000 @@ -52,18 +52,15 @@ % highpass stapes filter % Huber gives 2e-9 m at 80 dB and 1 kHz (2e-13 at 0 dB SPL) -OMEParams.OMEstapesLPcutoff= 1000; +OMEParams.OMEstapesHPcutoff= 1000; OMEParams.stapesScalar= 45e-9; % Acoustic reflex: maximum attenuation should be around 25 dB (Price, 1966) % i.e. a minimum ratio of 0.056. % 'spikes' model: AR based on brainstem spiking activity (LSR) -OMEParams.rateToAttenuationFactor=0.008; % * N(all ICspikes) -% OMEParams.rateToAttenuationFactor=0; % i.e. no AR - +OMEParams.rateToAttenuationFactor=0.05; % * N(all ICspikes) % 'probability model': Ar based on AN firing probabilities (LSR) -OMEParams.rateToAttenuationFactorProb=0.006; % * N(all ANrates) -% OMEParams.rateToAttenuationFactorProb=0; % i.e. no AR +OMEParams.rateToAttenuationFactorProb=0.02; % * N(all ANrates) % asymptote should be around 100-200 ms OMEParams.ARtau=.250; % AR smoothing function 250 ms fits Hung and Dallos @@ -77,19 +74,19 @@ % *** DRNL nonlinear path % broken stick compression -DRNLParams.a=5e4; % DRNL.a=0 means no OHCs (no nonlinear path) +DRNLParams.a=2e4; % DRNL.a=0 means no OHCs (no nonlinear path) DRNLParams.c=.2; % compression exponent -DRNLParams.CtBMdB = 10; %Compression threshold dB re 10e-9 m displacement +DRNLParams.ctBMdB = 10; %Compression threshold dB re 10e-9 m displacement % filters DRNLParams.nonlinOrder= 3; % order of nonlinear gammatone filters DRNLParams.nonlinCFs=BFlist; -p=0.2895; q=250; % human (% p=0.14; q=366; % cat) -DRNLParams.nlBWs= p * BFlist + q; -DRNLParams.p=p; DRNLParams.q=q; % save p and q for printing only +DRNLParams.p=0.2895; DRNLParams.q=250; % save p and q for printing only +% p=0.2895; q=250; % human (% p=0.14; q=366; % cat) +DRNLParams.nlBWs= DRNLParams.p * BFlist + DRNLParams.q; % *** DRNL linear path: -DRNLParams.g=200; % linear path gain factor +DRNLParams.g=100; % linear path gain factor DRNLParams.linOrder=3; % order of linear gammatone filters % linCF is not necessarily the same as nonlinCF minLinCF=153.13; coeffLinCF=0.7341; % linCF>nonlinBF for BF < 1 kHz @@ -100,23 +97,22 @@ % *** DRNL MOC efferents DRNLParams.MOCdelay = efferentDelay; % must be < segment length! -DRNLParams.minMOCattenuationdB=-30; +DRNLParams.minMOCattenuationdB=-35; % 'spikes' model: MOC based on brainstem spiking activity (HSR) -DRNLParams.MOCtau =.025; % smoothing for MOC -DRNLParams.rateToAttenuationFactor = .00635; % strength of MOC -% DRNLParams.rateToAttenuationFactor = 0; % strength of MOC +DRNLParams.MOCtau =.0285; % smoothing for MOC +DRNLParams.rateToAttenuationFactor = .03; % strength of MOC +DRNLParams.rateToAttenuationFactor = .0055; % strength of MOC -% 'probability' model: MOC based on AN spiking activity (HSR) +% 'probability' model: MOC based on AN probability (HSR) DRNLParams.MOCtauProb =.285; % smoothing for MOC -DRNLParams.rateToAttenuationFactorProb = 0.0075; % strength of MOC -% DRNLParams.rateToAttenuationFactorProb = .0; % strength of MOC -DRNLParams.MOCrateThresholdProb =50; % spikes/s probability only +DRNLParams.rateToAttenuationFactorProb = 0.007; % strength of MOC +DRNLParams.MOCrateThresholdProb =67; % spikes/s probability only %% #4 IHC_cilia_RPParams -IHC_cilia_RPParams.tc= 0.0003; % 0.0003 filter time simulates viscocity -IHC_cilia_RPParams.C= 0.03; % 0.1 scalar (C_cilia ) +IHC_cilia_RPParams.tc= 0.00012; % 0.0003 Shamma +IHC_cilia_RPParams.C= 0.08; % 0.1 scalar (C_cilia ) IHC_cilia_RPParams.u0= 5e-9; IHC_cilia_RPParams.s0= 30e-9; IHC_cilia_RPParams.u1= 1e-9; @@ -148,7 +144,8 @@ % reminder: changing z has a strong effect on HF thresholds (like Et) IHCpreSynapseParams.z= 2e42; % scalar Ca -> vesicle release rate -LSRtauCa=40e-6; HSRtauCa=80e-6; % seconds +LSRtauCa=30e-6; HSRtauCa=80e-6; % seconds +% LSRtauCa=40e-6; HSRtauCa=90e-6; % seconds % IHCpreSynapseParams.tauCa= [15e-6 80e-6]; %LSR and HSR fiber IHCpreSynapseParams.tauCa= [LSRtauCa HSRtauCa]; %LSR and HSR fiber @@ -159,7 +156,8 @@ % absolute refractory period. Relative refractory period is the same. AN_IHCsynapseParams.refractory_period= 0.00075; AN_IHCsynapseParams.TWdelay=0.004; % ?delay before stimulus first spike -AN_IHCsynapseParams.ANspeedUpFactor=5; % longer epochs for computing spikes. +AN_IHCsynapseParams.spikesTargetSampleRate=10000; +% AN_IHCsynapseParams.ANspeedUpFactor=5; % longer epochs for computing spikes. % c=kym/(y(l+r)+kl) (spontaneous rate) % c=(approx) ym/l (saturated rate) @@ -205,7 +203,7 @@ MacGregorMultiParams.fibersPerNeuron=10; % N input fibers MacGregorMultiParams.dendriteLPfreq=50; % dendritic filter - MacGregorMultiParams.currentPerSpike=35e-9; % *per spike + MacGregorMultiParams.currentPerSpike=28e-9; % *per spike % MacGregorMultiParams.currentPerSpike=30e-9; % *per spike MacGregorMultiParams.Cap=1.67e-8; % ??cell capacitance (Siemens) @@ -226,7 +224,6 @@ MacGregorParams.type = 'chopper cell'; MacGregorParams.fibersPerNeuron=10; % N input fibers MacGregorParams.dendriteLPfreq=100; % dendritic filter -MacGregorParams.currentPerSpike=120e-9;% *(A) per spike MacGregorParams.currentPerSpike=40e-9;% *(A) per spike MacGregorParams.Cap=16.7e-9; % cell capacitance (Siemens) @@ -246,18 +243,26 @@ % MacGregorParams.saveAllData=0; %% #9 filteredSACF -minPitch= 300; maxPitch= 3000; numPitches=60; % specify lags -pitches=100*log10(logspace(minPitch/100, maxPitch/100, numPitches)); -filteredSACFParams.lags=1./pitches; % autocorrelation lags vector -filteredSACFParams.acfTau= .003; % time constant of running ACF -filteredSACFParams.lambda= 0.12; % slower filter to smooth ACF -filteredSACFParams.plotFilteredSACF=1; % 0 plots unfiltered ACFs -filteredSACFParams.plotACFs=0; % special plot (see code) -% filteredSACFParams.usePressnitzer=0; % attenuates ACF at long lags -filteredSACFParams.lagsProcedure= 'useAllLags'; -% filteredSACFParams.lagsProcedure= 'useBernsteinLagWeights'; -% filteredSACFParams.lagsProcedure= 'omitShortLags'; -filteredSACFParams.criterionForOmittingLags=3; +% identify periodicities to be logged + minPitch= 80; maxPitch= 500; numPitches=50; + maxLag=1/minPitch; minLag=1/maxPitch; + lags= linspace(minLag, maxLag, numPitches); + pitches=10.^ linspace(log10(minPitch), log10(maxPitch),numPitches); + pitches=fliplr(pitches); + % convert to lags for ACF + filteredSACFParams.lags=lags; % autocorrelation lags vector + filteredSACFParams.acfTau= .003; % time constant of running ACF + filteredSACFParams.lambda= 0.12; % slower filter to smooth ACF + % request plot of within-channel ACFs at fixed intervals in time + filteredSACFParams.plotACFs=1; + filteredSACFParams.plotACFsInterval=0.002; + filteredSACFParams.plotMoviePauses=.1; + + filteredSACFParams.usePressnitzer=0; % attenuates ACF at long lags + filteredSACFParams.lagsProcedure= 'useAllLags'; + % 'useAllLags' or 'omitShortLags' + filteredSACFParams.criterionForOmittingLags=3; + % checks if AN_IHCsynapseParams.numFibers<MacGregorMultiParams.fibersPerNeuron @@ -269,7 +274,7 @@ % paramChanges if nargin>3 && ~isempty(paramChanges) if ~iscellstr(paramChanges) - error(['paramChanges error: paramChanges not a cell array']) + error('paramChanges error: paramChanges not a cell array') end nChanges=length(paramChanges);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/parameterStore/MAPparamsOHCloss.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,328 @@ +function method=MAPparamsOHCloss ... + (BFlist, sampleRate, showParams, paramChanges) +% MAPparamsOHCloss.m is a parameter file, similar in all respects to +% MAPparamsNormal except that the parameter 'DRNLParams.a' is set to zero. +% +% MAPparams<> establishes a complete set of MAP parameters +% Parameter file names must be of the form <MAPparams><name> +% +% Input arguments +% BFlist (optional) specifies the desired list of channel BFs +% otherwise defaults set below +% sampleRate (optional), default is 50000. +% showParams (optional) =1 prints out the complete set of parameters +% Output argument +% method passes a miscelleny of values +% the use of 'method' is being phased out. use globals + +global inputStimulusParams OMEParams DRNLParams IHC_cilia_RPParams +global IHCpreSynapseParams AN_IHCsynapseParams +global MacGregorParams MacGregorMultiParams filteredSACFParams +global experiment % used only by calls from multiThreshold +% global IHC_VResp_VivoParams + +currentFile=mfilename; % i.e. the name of this mfile +method.parameterSource=currentFile(10:end); % for the record + +efferentDelay=0.010; +method.segmentDuration=efferentDelay; + +if nargin<3, showParams=0; end +if nargin<2, sampleRate=44100; end +if nargin<1 || BFlist(1)<0 % if BFlist= -1, set BFlist to default + lowestBF=250; highestBF= 8000; numChannels=21; + % 21 chs (250-8k)includes BFs at 250 500 1000 2000 4000 8000 + BFlist=round(logspace(log10(lowestBF),log10(highestBF),numChannels)); +end +% BFlist=1000; % single channel option + +% preserve for backward campatibility +method.nonlinCF=BFlist; +method.dt=1/sampleRate; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% set model parameters +%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%% #1 inputStimulus +inputStimulusParams=[]; +inputStimulusParams.sampleRate= sampleRate; + +%% #2 outerMiddleEar +OMEParams=[]; % clear the structure first +% outer ear resonances band pass filter [gain lp order hp] +OMEParams.externalResonanceFilters= [ 10 1 1000 4000]; + +% highpass stapes filter +% Huber gives 2e-9 m at 80 dB and 1 kHz (2e-13 at 0 dB SPL) +OMEParams.OMEstapesHPcutoff= 1000; +OMEParams.stapesScalar= 45e-9; + +% Acoustic reflex: maximum attenuation should be around 25 dB (Price, 1966) +% i.e. a minimum ratio of 0.056. +% 'spikes' model: AR based on brainstem spiking activity (LSR) +OMEParams.rateToAttenuationFactor=0.05; % * N(all ICspikes) +% 'probability model': Ar based on AN firing probabilities (LSR) +OMEParams.rateToAttenuationFactorProb=0.02; % * N(all ANrates) + +% asymptote should be around 100-200 ms +OMEParams.ARtau=.250; % AR smoothing function 250 ms fits Hung and Dallos +% delay must be longer than the segment length +OMEParams.ARdelay=efferentDelay; %Moss gives 8.5 ms latency +OMEParams.ARrateThreshold=40; + +%% #3 DRNL +DRNLParams=[]; % clear the structure first +% DRNLParams.BFlist=BFlist; + +% *** DRNL nonlinear path +% broken stick compression +% DRNLParams.a=2e4; % normal value (commented out) +DRNLParams.a=0; % DRNL.a=0 means no OHCs (no nonlinear path) +DRNLParams.c=.2; % compression exponent +DRNLParams.ctBMdB = 10; %Compression threshold dB re 10e-9 m displacement + +% filters +DRNLParams.nonlinOrder= 3; % order of nonlinear gammatone filters +DRNLParams.nonlinCFs=BFlist; +DRNLParams.p=0.2895; DRNLParams.q=250; % save p and q for printing only +% p=0.2895; q=250; % human (% p=0.14; q=366; % cat) +DRNLParams.nlBWs= DRNLParams.p * BFlist + DRNLParams.q; + +% *** DRNL linear path: +DRNLParams.g=100; % linear path gain factor +DRNLParams.linOrder=3; % order of linear gammatone filters +% linCF is not necessarily the same as nonlinCF +minLinCF=153.13; coeffLinCF=0.7341; % linCF>nonlinBF for BF < 1 kHz +DRNLParams.linCFs=minLinCF+coeffLinCF*BFlist; +% bandwidths (linear) +minLinBW=100; coeffLinBW=0.6531; +DRNLParams.linBWs=minLinBW + coeffLinBW*BFlist; % bandwidths of linear filters + +% *** DRNL MOC efferents +DRNLParams.MOCdelay = efferentDelay; % must be < segment length! +DRNLParams.minMOCattenuationdB=-35; + +% 'spikes' model: MOC based on brainstem spiking activity (HSR) +DRNLParams.MOCtau =.0285; % smoothing for MOC +DRNLParams.rateToAttenuationFactor = .03; % strength of MOC +DRNLParams.rateToAttenuationFactor = .0055; % strength of MOC + +% 'probability' model: MOC based on AN probability (HSR) +DRNLParams.MOCtauProb =.285; % smoothing for MOC +DRNLParams.rateToAttenuationFactorProb = 0.007; % strength of MOC +DRNLParams.MOCrateThresholdProb =67; % spikes/s probability only + + +%% #4 IHC_cilia_RPParams +IHC_cilia_RPParams.tc= 0.00012; % 0.0003 Shamma +IHC_cilia_RPParams.C= 0.08; % 0.1 scalar (C_cilia ) +IHC_cilia_RPParams.u0= 5e-9; +IHC_cilia_RPParams.s0= 30e-9; +IHC_cilia_RPParams.u1= 1e-9; +IHC_cilia_RPParams.s1= 1e-9; + +IHC_cilia_RPParams.Gmax= 6e-9; % 2.5e-9 maximum conductance (Siemens) +IHC_cilia_RPParams.Ga= 1e-9; % 4.3e-9 fixed apical membrane conductance +IHC_cilia_RPParams.Ga= .8e-9; % 4.3e-9 fixed apical membrane conductance + +% #5 IHC_RP +IHC_cilia_RPParams.Cab= 4e-012; % IHC capacitance (F) +% IHC_cilia_RPParams.Cab= 1e-012; % IHC capacitance (F) +IHC_cilia_RPParams.Et= 0.100; % endocochlear potential (V) + +IHC_cilia_RPParams.Gk= 2e-008; % 1e-8 potassium conductance (S) +IHC_cilia_RPParams.Ek= -0.08; % -0.084 K equilibrium potential +IHC_cilia_RPParams.Rpc= 0.04; % combined resistances + + +%% #5 IHCpreSynapse +IHCpreSynapseParams=[]; +IHCpreSynapseParams.GmaxCa= 14e-9;% maximum calcium conductance +% IHCpreSynapseParams.GmaxCa= 12e-9;% maximum calcium conductance +IHCpreSynapseParams.ECa= 0.066; % calcium equilibrium potential +IHCpreSynapseParams.beta= 400; % determine Ca channel opening +IHCpreSynapseParams.gamma= 100; % determine Ca channel opening +IHCpreSynapseParams.tauM= 0.00005; % membrane time constant ?0.1ms +IHCpreSynapseParams.power= 3; +% reminder: changing z has a strong effect on HF thresholds (like Et) +IHCpreSynapseParams.z= 2e42; % scalar Ca -> vesicle release rate + +LSRtauCa=30e-6; HSRtauCa=80e-6; % seconds +% LSRtauCa=40e-6; HSRtauCa=90e-6; % seconds +% IHCpreSynapseParams.tauCa= [15e-6 80e-6]; %LSR and HSR fiber +IHCpreSynapseParams.tauCa= [LSRtauCa HSRtauCa]; %LSR and HSR fiber + +%% #6 AN_IHCsynapse +AN_IHCsynapseParams=[]; % clear the structure first +% number of AN fibers at each BF (used only for spike generation) +AN_IHCsynapseParams.numFibers= 100; +% absolute refractory period. Relative refractory period is the same. +AN_IHCsynapseParams.refractory_period= 0.00075; +AN_IHCsynapseParams.TWdelay=0.004; % ?delay before stimulus first spike +AN_IHCsynapseParams.spikesTargetSampleRate=10000; +% AN_IHCsynapseParams.ANspeedUpFactor=5; % longer epochs for computing spikes. + +% c=kym/(y(l+r)+kl) (spontaneous rate) +% c=(approx) ym/l (saturated rate) +AN_IHCsynapseParams.M= 12; % maximum vesicles at synapse +AN_IHCsynapseParams.y= 4; % depleted vesicle replacement rate +AN_IHCsynapseParams.y= 6; % depleted vesicle replacement rate + +AN_IHCsynapseParams.x= 30; % replenishment from re-uptake store +AN_IHCsynapseParams.x= 60; % replenishment from re-uptake store + +% reduce l to increase saturated rate +AN_IHCsynapseParams.l= 100; % *loss rate of vesicles from the cleft +AN_IHCsynapseParams.l= 250; % *loss rate of vesicles from the cleft + +AN_IHCsynapseParams.r= 500; % *reuptake rate from cleft into cell +% AN_IHCsynapseParams.r= 300; % *reuptake rate from cleft into cell + + +%% #7 MacGregorMulti (first order brainstem neurons) +MacGregorMultiParams=[]; +MacGregorMultiType='chopper'; % MacGregorMultiType='primary-like'; %choose +switch MacGregorMultiType + case 'primary-like' + MacGregorMultiParams.nNeuronsPerBF= 10; % N neurons per BF + MacGregorMultiParams.type = 'primary-like cell'; + MacGregorMultiParams.fibersPerNeuron=4; % N input fibers + MacGregorMultiParams.dendriteLPfreq=200; % dendritic filter + MacGregorMultiParams.currentPerSpike=0.11e-6; % (A) per spike + MacGregorMultiParams.Cap=4.55e-9; % cell capacitance (Siemens) + MacGregorMultiParams.tauM=5e-4; % membrane time constant (s) + MacGregorMultiParams.Ek=-0.01; % K+ eq. potential (V) + MacGregorMultiParams.dGkSpike=3.64e-5; % K+ cond.shift on spike,S + MacGregorMultiParams.tauGk= 0.0012; % K+ conductance tau (s) + MacGregorMultiParams.Th0= 0.01; % equilibrium threshold (V) + MacGregorMultiParams.c= 0.01; % threshold shift on spike, (V) + MacGregorMultiParams.tauTh= 0.015; % variable threshold tau + MacGregorMultiParams.Er=-0.06; % resting potential (V) + MacGregorMultiParams.Eb=0.06; % spike height (V) + + case 'chopper' + MacGregorMultiParams.nNeuronsPerBF= 10; % N neurons per BF + MacGregorMultiParams.type = 'chopper cell'; + MacGregorMultiParams.fibersPerNeuron=10; % N input fibers + + MacGregorMultiParams.dendriteLPfreq=50; % dendritic filter + MacGregorMultiParams.currentPerSpike=28e-9; % *per spike +% MacGregorMultiParams.currentPerSpike=30e-9; % *per spike + + MacGregorMultiParams.Cap=1.67e-8; % ??cell capacitance (Siemens) + MacGregorMultiParams.tauM=0.002; % membrane time constant (s) + MacGregorMultiParams.Ek=-0.01; % K+ eq. potential (V) + MacGregorMultiParams.dGkSpike=1.33e-4; % K+ cond.shift on spike,S + MacGregorMultiParams.tauGk= 0.0005;% K+ conductance tau (s) + MacGregorMultiParams.Th0= 0.01; % equilibrium threshold (V) + MacGregorMultiParams.c= 0; % threshold shift on spike, (V) + MacGregorMultiParams.tauTh= 0.02; % variable threshold tau + MacGregorMultiParams.Er=-0.06; % resting potential (V) + MacGregorMultiParams.Eb=0.06; % spike height (V) + MacGregorMultiParams.PSTHbinWidth= 1e-4; +end + +%% #8 MacGregor (second-order neuron). Only one per channel +MacGregorParams=[]; % clear the structure first +MacGregorParams.type = 'chopper cell'; +MacGregorParams.fibersPerNeuron=10; % N input fibers +MacGregorParams.dendriteLPfreq=100; % dendritic filter +MacGregorParams.currentPerSpike=40e-9;% *(A) per spike + +MacGregorParams.Cap=16.7e-9; % cell capacitance (Siemens) +MacGregorParams.tauM=0.002; % membrane time constant (s) +MacGregorParams.Ek=-0.01; % K+ eq. potential (V) +MacGregorParams.dGkSpike=1.33e-4; % K+ cond.shift on spike,S +MacGregorParams.tauGk= 0.0012; % K+ conductance tau (s) +MacGregorParams.Th0= 0.01; % equilibrium threshold (V) +MacGregorParams.c= 0; % threshold shift on spike, (V) +MacGregorParams.tauTh= 0.02; % variable threshold tau +MacGregorParams.Er=-0.06; % resting potential (V) +MacGregorParams.Eb=0.06; % spike height (V) +MacGregorParams.debugging=0; % (special) +% wideband accepts input from all channels (of same fiber type) +% use wideband to create inhibitory units +MacGregorParams.wideband=0; % special for wideband units +% MacGregorParams.saveAllData=0; + +%% #9 filteredSACF +minPitch= 300; maxPitch= 3000; numPitches=60; % specify lags +pitches=100*log10(logspace(minPitch/100, maxPitch/100, numPitches)); +filteredSACFParams.lags=1./pitches; % autocorrelation lags vector +filteredSACFParams.acfTau= .003; % time constant of running ACF +filteredSACFParams.lambda= 0.12; % slower filter to smooth ACF +filteredSACFParams.plotFilteredSACF=1; % 0 plots unfiltered ACFs +filteredSACFParams.plotACFs=0; % special plot (see code) +% filteredSACFParams.usePressnitzer=0; % attenuates ACF at long lags +filteredSACFParams.lagsProcedure= 'useAllLags'; +% filteredSACFParams.lagsProcedure= 'omitShortLags'; +filteredSACFParams.criterionForOmittingLags=3; + +% checks +if AN_IHCsynapseParams.numFibers<MacGregorMultiParams.fibersPerNeuron + error('MacGregorMulti: too few input fibers for input to MacG unit') +end + + +%% now accept last minute parameter changes required by the calling program +% paramChanges +if nargin>3 && ~isempty(paramChanges) + if ~iscellstr(paramChanges) + error('paramChanges error: paramChanges not a cell array') + end + + nChanges=length(paramChanges); + for idx=1:nChanges + x=paramChanges{idx}; + x=deblank(x); + if ~isempty(x) + if ~strcmp(x(end),';') + error(['paramChanges error (terminate with semicolon) ' x]) + end + st=strtrim(x(1:strfind(x,'.')-1)); + fld=strtrim(x(strfind(x,'.')+1:strfind(x,'=')-1)); + value=x(strfind(x,'=')+1:end); + if isempty(st) || isempty(fld) || isempty(value) + error(['paramChanges error:' x]) + end + + x1=eval(['isstruct(' st ')']); + cmd=['isfield(' st ',''' fld ''')']; + x2=eval(cmd); + if ~(x1*x2) + error(['paramChanges error:' x]) + end + end + + % no problems so go ahead + eval(paramChanges{idx}) + end +end + + +%% write all parameters to the command window +% showParams is currently set at the top of htis function +if showParams + fprintf('\n %%%%%%%%\n') + fprintf('\n%s\n', method.parameterSource) + fprintf('\n') + nm=UTIL_paramsList(whos); + for i=1:length(nm) + % eval(['UTIL_showStruct(' nm{i} ', ''' nm{i} ''')']) + if ~strcmp(nm(i), 'method') + eval(['UTIL_showStructureSummary(' nm{i} ', ''' nm{i} ''', 10)']) + end + end + + % highlight parameter changes made locally + if nargin>3 && ~isempty(paramChanges) + fprintf('\n Local parameter changes:\n') + for i=1:length(paramChanges) + disp(paramChanges{i}) + end + end +end + +% for backward compatibility +experiment.comparisonData=[];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/parameterStore/MAPparamsPL.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,329 @@ +function method=MAPparamsPL ... + (BFlist, sampleRate, showParams, paramChanges) +% MAPparamsPL.m is a parameter file, similar in all respects to +% MAPparamsNormal except that a primary-like' neuron is used at the +% level of the cochlear nuclues (rather than a chopper cell). +% +% MAPparams<> establishes a complete set of MAP parameters +% Parameter file names must be of the form <MAPparams><name> +% +% Input arguments +% BFlist (optional) specifies the desired list of channel BFs +% otherwise defaults set below +% sampleRate (optional), default is 50000. +% showParams (optional) =1 prints out the complete set of parameters +% Output argument +% method passes a miscelleny of values +% the use of 'method' is being phased out. use globals + +global inputStimulusParams OMEParams DRNLParams IHC_cilia_RPParams +global IHCpreSynapseParams AN_IHCsynapseParams +global MacGregorParams MacGregorMultiParams filteredSACFParams +global experiment % used only by calls from multiThreshold +% global IHC_VResp_VivoParams + +currentFile=mfilename; % i.e. the name of this mfile +method.parameterSource=currentFile(10:end); % for the record + +efferentDelay=0.010; +method.segmentDuration=efferentDelay; + +if nargin<3, showParams=0; end +if nargin<2, sampleRate=50000; end +if nargin<1 || BFlist(1)<0 % if BFlist= -1, set BFlist to default + lowestBF=250; highestBF= 8000; numChannels=21; + % 21 chs (250-8k)includes BFs at 250 500 1000 2000 4000 8000 + BFlist=round(logspace(log10(lowestBF),log10(highestBF),numChannels)); +end +% BFlist=1000; % single channel option + +% preserve for backward campatibility +method.nonlinCF=BFlist; +method.dt=1/sampleRate; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% set model parameters +%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%% #1 inputStimulus +inputStimulusParams=[]; +inputStimulusParams.sampleRate= sampleRate; + +%% #2 outerMiddleEar +OMEParams=[]; % clear the structure first +% outer ear resonances band pass filter [gain lp order hp] +OMEParams.externalResonanceFilters= [ 10 1 1000 4000]; + +% highpass stapes filter +% Huber gives 2e-9 m at 80 dB and 1 kHz (2e-13 at 0 dB SPL) +OMEParams.OMEstapesHPcutoff= 1000; +OMEParams.stapesScalar= 45e-9; + +% Acoustic reflex: maximum attenuation should be around 25 dB (Price, 1966) +% i.e. a minimum ratio of 0.056. +% 'spikes' model: AR based on brainstem spiking activity (LSR) +OMEParams.rateToAttenuationFactor=0.05; % * N(all ICspikes) +% 'probability model': Ar based on AN firing probabilities (LSR) +OMEParams.rateToAttenuationFactorProb=0.02; % * N(all ANrates) + +% asymptote should be around 100-200 ms +OMEParams.ARtau=.250; % AR smoothing function 250 ms fits Hung and Dallos +% delay must be longer than the segment length +OMEParams.ARdelay=efferentDelay; %Moss gives 8.5 ms latency +OMEParams.ARrateThreshold=40; + +%% #3 DRNL +DRNLParams=[]; % clear the structure first +% DRNLParams.BFlist=BFlist; + +% *** DRNL nonlinear path +% broken stick compression +DRNLParams.a=2e4; % DRNL.a=0 means no OHCs (no nonlinear path) +DRNLParams.c=.2; % compression exponent +DRNLParams.ctBMdB = 10; %Compression threshold dB re 10e-9 m displacement + +% filters +DRNLParams.nonlinOrder= 3; % order of nonlinear gammatone filters +DRNLParams.nonlinCFs=BFlist; +DRNLParams.p=0.2895; DRNLParams.q=250; % save p and q for printing only +% p=0.2895; q=250; % human (% p=0.14; q=366; % cat) +DRNLParams.nlBWs= DRNLParams.p * BFlist + DRNLParams.q; + +% *** DRNL linear path: +DRNLParams.g=100; % linear path gain factor +DRNLParams.linOrder=3; % order of linear gammatone filters +% linCF is not necessarily the same as nonlinCF +minLinCF=153.13; coeffLinCF=0.7341; % linCF>nonlinBF for BF < 1 kHz +DRNLParams.linCFs=minLinCF+coeffLinCF*BFlist; +% bandwidths (linear) +minLinBW=100; coeffLinBW=0.6531; +DRNLParams.linBWs=minLinBW + coeffLinBW*BFlist; % bandwidths of linear filters + +% *** DRNL MOC efferents +DRNLParams.MOCdelay = efferentDelay; % must be < segment length! +DRNLParams.minMOCattenuationdB=-35; + +% 'spikes' model: MOC based on brainstem spiking activity (HSR) +DRNLParams.MOCtau =.0285; % smoothing for MOC +DRNLParams.rateToAttenuationFactor = .03; % strength of MOC +DRNLParams.rateToAttenuationFactor = .0055; % strength of MOC + +% 'probability' model: MOC based on AN probability (HSR) +DRNLParams.MOCtauProb =.285; % smoothing for MOC +DRNLParams.rateToAttenuationFactorProb = 0.007; % strength of MOC +DRNLParams.MOCrateThresholdProb =67; % spikes/s probability only + + +%% #4 IHC_cilia_RPParams +IHC_cilia_RPParams.tc= 0.00012; % 0.0003 Shamma +IHC_cilia_RPParams.C= 0.08; % 0.1 scalar (C_cilia ) +IHC_cilia_RPParams.u0= 5e-9; +IHC_cilia_RPParams.s0= 30e-9; +IHC_cilia_RPParams.u1= 1e-9; +IHC_cilia_RPParams.s1= 1e-9; + +IHC_cilia_RPParams.Gmax= 6e-9; % 2.5e-9 maximum conductance (Siemens) +IHC_cilia_RPParams.Ga= 1e-9; % 4.3e-9 fixed apical membrane conductance +IHC_cilia_RPParams.Ga= .8e-9; % 4.3e-9 fixed apical membrane conductance + +% #5 IHC_RP +IHC_cilia_RPParams.Cab= 4e-012; % IHC capacitance (F) +% IHC_cilia_RPParams.Cab= 1e-012; % IHC capacitance (F) +IHC_cilia_RPParams.Et= 0.100; % endocochlear potential (V) + +IHC_cilia_RPParams.Gk= 2e-008; % 1e-8 potassium conductance (S) +IHC_cilia_RPParams.Ek= -0.08; % -0.084 K equilibrium potential +IHC_cilia_RPParams.Rpc= 0.04; % combined resistances + + +%% #5 IHCpreSynapse +IHCpreSynapseParams=[]; +IHCpreSynapseParams.GmaxCa= 14e-9;% maximum calcium conductance +% IHCpreSynapseParams.GmaxCa= 12e-9;% maximum calcium conductance +IHCpreSynapseParams.ECa= 0.066; % calcium equilibrium potential +IHCpreSynapseParams.beta= 400; % determine Ca channel opening +IHCpreSynapseParams.gamma= 100; % determine Ca channel opening +IHCpreSynapseParams.tauM= 0.00005; % membrane time constant ?0.1ms +IHCpreSynapseParams.power= 3; +% reminder: changing z has a strong effect on HF thresholds (like Et) +IHCpreSynapseParams.z= 2e42; % scalar Ca -> vesicle release rate + +LSRtauCa=30e-6; HSRtauCa=80e-6; % seconds +% LSRtauCa=40e-6; HSRtauCa=90e-6; % seconds +% IHCpreSynapseParams.tauCa= [15e-6 80e-6]; %LSR and HSR fiber +IHCpreSynapseParams.tauCa= [LSRtauCa HSRtauCa]; %LSR and HSR fiber + +%% #6 AN_IHCsynapse +AN_IHCsynapseParams=[]; % clear the structure first +% number of AN fibers at each BF (used only for spike generation) +AN_IHCsynapseParams.numFibers= 100; +% absolute refractory period. Relative refractory period is the same. +AN_IHCsynapseParams.refractory_period= 0.00075; +AN_IHCsynapseParams.TWdelay=0.004; % ?delay before stimulus first spike +AN_IHCsynapseParams.spikesTargetSampleRate=sampleRate; +% AN_IHCsynapseParams.ANspeedUpFactor=5; % longer epochs for computing spikes. + +% c=kym/(y(l+r)+kl) (spontaneous rate) +% c=(approx) ym/l (saturated rate) +AN_IHCsynapseParams.M= 12; % maximum vesicles at synapse +AN_IHCsynapseParams.y= 4; % depleted vesicle replacement rate +AN_IHCsynapseParams.y= 6; % depleted vesicle replacement rate + +AN_IHCsynapseParams.x= 30; % replenishment from re-uptake store +AN_IHCsynapseParams.x= 60; % replenishment from re-uptake store + +% reduce l to increase saturated rate +AN_IHCsynapseParams.l= 100; % *loss rate of vesicles from the cleft +AN_IHCsynapseParams.l= 250; % *loss rate of vesicles from the cleft + +AN_IHCsynapseParams.r= 500; % *reuptake rate from cleft into cell +% AN_IHCsynapseParams.r= 300; % *reuptake rate from cleft into cell + + +%% #7 MacGregorMulti (first order brainstem neurons) +MacGregorMultiParams=[]; +% MacGregorMultiType='chopper'; % MacGregorMultiType='primary-like'; %choose +MacGregorMultiType='primary-like'; % MacGregorMultiType='primary-like'; %choose +switch MacGregorMultiType + case 'primary-like' + MacGregorMultiParams.nNeuronsPerBF= 10; % N neurons per BF + MacGregorMultiParams.type = 'primary-like cell'; + MacGregorMultiParams.fibersPerNeuron=4; % N input fibers + MacGregorMultiParams.dendriteLPfreq=500; % dendritic filter + MacGregorMultiParams.currentPerSpike=0.11e-6; % (A) per spike + MacGregorMultiParams.Cap=4.55e-9; % cell capacitance (Siemens) + MacGregorMultiParams.tauM=5e-4; % membrane time constant (s) + MacGregorMultiParams.Ek=-0.01; % K+ eq. potential (V) + MacGregorMultiParams.dGkSpike=3.64e-5; % K+ cond.shift on spike,S + MacGregorMultiParams.tauGk= 0.0005; % K+ conductance tau (s) + MacGregorMultiParams.Th0= 0.01; % equilibrium threshold (V) + MacGregorMultiParams.c= 0.01; % threshold shift on spike, (V) + MacGregorMultiParams.tauTh= 0.015; % variable threshold tau + MacGregorMultiParams.Er=-0.06; % resting potential (V) + MacGregorMultiParams.Eb=0.06; % spike height (V) + + case 'chopper' + MacGregorMultiParams.nNeuronsPerBF= 10; % N neurons per BF + MacGregorMultiParams.type = 'chopper cell'; + MacGregorMultiParams.fibersPerNeuron=10; % N input fibers + + MacGregorMultiParams.dendriteLPfreq=50; % dendritic filter + MacGregorMultiParams.currentPerSpike=28e-9; % *per spike +% MacGregorMultiParams.currentPerSpike=30e-9; % *per spike + + MacGregorMultiParams.Cap=1.67e-8; % ??cell capacitance (Siemens) + MacGregorMultiParams.tauM=0.002; % membrane time constant (s) + MacGregorMultiParams.Ek=-0.01; % K+ eq. potential (V) + MacGregorMultiParams.dGkSpike=1.33e-4; % K+ cond.shift on spike,S + MacGregorMultiParams.tauGk= 0.0005;% K+ conductance tau (s) + MacGregorMultiParams.Th0= 0.01; % equilibrium threshold (V) + MacGregorMultiParams.c= 0; % threshold shift on spike, (V) + MacGregorMultiParams.tauTh= 0.02; % variable threshold tau + MacGregorMultiParams.Er=-0.06; % resting potential (V) + MacGregorMultiParams.Eb=0.06; % spike height (V) + MacGregorMultiParams.PSTHbinWidth= 1e-4; +end + +%% #8 MacGregor (second-order neuron). Only one per channel +MacGregorParams=[]; % clear the structure first +MacGregorParams.type = 'chopper cell'; +MacGregorParams.fibersPerNeuron=10; % N input fibers +MacGregorParams.dendriteLPfreq=100; % dendritic filter +MacGregorParams.currentPerSpike=40e-9;% *(A) per spike + +MacGregorParams.Cap=16.7e-9; % cell capacitance (Siemens) +MacGregorParams.tauM=0.002; % membrane time constant (s) +MacGregorParams.Ek=-0.01; % K+ eq. potential (V) +MacGregorParams.dGkSpike=1.33e-4; % K+ cond.shift on spike,S +MacGregorParams.tauGk= 0.0012; % K+ conductance tau (s) +MacGregorParams.Th0= 0.01; % equilibrium threshold (V) +MacGregorParams.c= 0; % threshold shift on spike, (V) +MacGregorParams.tauTh= 0.02; % variable threshold tau +MacGregorParams.Er=-0.06; % resting potential (V) +MacGregorParams.Eb=0.06; % spike height (V) +MacGregorParams.debugging=0; % (special) +% wideband accepts input from all channels (of same fiber type) +% use wideband to create inhibitory units +MacGregorParams.wideband=0; % special for wideband units +% MacGregorParams.saveAllData=0; + +%% #9 filteredSACF +minPitch= 300; maxPitch= 3000; numPitches=60; % specify lags +pitches=100*log10(logspace(minPitch/100, maxPitch/100, numPitches)); +filteredSACFParams.lags=1./pitches; % autocorrelation lags vector +filteredSACFParams.acfTau= .003; % time constant of running ACF +filteredSACFParams.lambda= 0.12; % slower filter to smooth ACF +filteredSACFParams.plotFilteredSACF=1; % 0 plots unfiltered ACFs +filteredSACFParams.plotACFs=0; % special plot (see code) +% filteredSACFParams.usePressnitzer=0; % attenuates ACF at long lags +filteredSACFParams.lagsProcedure= 'useAllLags'; +% filteredSACFParams.lagsProcedure= 'omitShortLags'; +filteredSACFParams.criterionForOmittingLags=3; + +% checks +if AN_IHCsynapseParams.numFibers<MacGregorMultiParams.fibersPerNeuron + error('MacGregorMulti: too few input fibers for input to MacG unit') +end + + +%% now accept last minute parameter changes required by the calling program +% paramChanges +if nargin>3 && ~isempty(paramChanges) + if ~iscellstr(paramChanges) + error('paramChanges error: paramChanges not a cell array') + end + + nChanges=length(paramChanges); + for idx=1:nChanges + x=paramChanges{idx}; + x=deblank(x); + if ~isempty(x) + if ~strcmp(x(end),';') + error(['paramChanges error (terminate with semicolon) ' x]) + end + st=strtrim(x(1:strfind(x,'.')-1)); + fld=strtrim(x(strfind(x,'.')+1:strfind(x,'=')-1)); + value=x(strfind(x,'=')+1:end); + if isempty(st) || isempty(fld) || isempty(value) + error(['paramChanges error:' x]) + end + + x1=eval(['isstruct(' st ')']); + cmd=['isfield(' st ',''' fld ''')']; + x2=eval(cmd); + if ~(x1*x2) + error(['paramChanges error:' x]) + end + end + + % no problems so go ahead + eval(paramChanges{idx}) + end +end + + +%% write all parameters to the command window +% showParams is currently set at the top of htis function +if showParams + fprintf('\n %%%%%%%%\n') + fprintf('\n%s\n', method.parameterSource) + fprintf('\n') + nm=UTIL_paramsList(whos); + for i=1:length(nm) + % eval(['UTIL_showStruct(' nm{i} ', ''' nm{i} ''')']) + if ~strcmp(nm(i), 'method') + eval(['UTIL_showStructureSummary(' nm{i} ', ''' nm{i} ''', 10)']) + end + end + + % highlight parameter changes made locally + if nargin>3 && ~isempty(paramChanges) + fprintf('\n Local parameter changes:\n') + for i=1:length(paramChanges) + disp(paramChanges{i}) + end + end +end + +% for backward compatibility +experiment.comparisonData=[];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile11_5hr05_Oct_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile11_5hr05_Oct_2011 +%created: 11_5hr05_Oct_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [26.6 23.8 29.1 47.2 62.9 48.4]; +x.ShortTone = [34.1 33.4 37.6 64.3 78.1 62.4]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +49.9 55.5 47.3 70.7 94.5 54.2 +52.3 55.4 42.6 69.4 95.2 56.7 +55.1 67.5 49.6 73.5 96.6 84 +60.3 72.2 53.5 85.4 99 70.2 +64.3 78.7 61.3 83.2 102 66.8 +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +54.7 74.2 79.9 80.4 93.4 85.4 +46.7 66 69.3 83.6 96.7 96.1 +49.7 55.1 42.4 82.6 97.3 82.5 +52.8 52.4 42.4 79.2 92.4 46.1 +54.8 48.2 43.7 79 97.7 56.4 +47.9 64.8 63.7 92.9 NaN 69.8 +68.6 87.1 87 98 NaN 107 +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile12_26hr05_Oct_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile12_26hr05_Oct_2011 +%created: 12_26hr05_Oct_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [10.5 7.64 6.56 10.1 13.2 18.6]; +x.ShortTone = [15.4 14.1 12.5 16 21.7 27.4]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +39.6 46.7 37.8 47.4 51 67.9 +47.7 49.6 47.2 44.2 63 83.5 +54 53.8 59.9 53.4 74.5 89.8 +61.2 63.7 58.8 58.2 80 97.5 +65.2 67.3 69.3 72.2 91.3 95.9 +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +47.8 70.6 76.5 77.2 78.5 94.9 +42.3 59.2 63.2 74.8 84.1 97.2 +36.9 47.3 43.8 45.5 65 85.5 +38.7 44.6 40.5 53 49.8 71.2 +39 38.5 39.6 27.3 41.3 68.5 +41.6 47.9 56.7 55.8 93.4 NaN +54.8 84.6 87 92.2 103 NaN +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile12_41hr04_Oct_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile12_41hr04_Oct_2011 +%created: 12_41hr04_Oct_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [40.1 42.7 53.1 81.2 92.3 82.3]; +x.ShortTone = [49.6 48.3 64.4 89 95.5 92.3]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +60.6 58.5 77.7 NaN NaN NaN +62.8 56 85.2 NaN NaN NaN +60.8 60.2 81.6 NaN NaN NaN +75.2 67.5 85.4 NaN NaN NaN +83.9 77.4 93.2 NaN NaN NaN +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +60.9 81.2 89.1 105 NaN NaN +57.6 69 85.4 105 NaN NaN +52.4 56.4 77.2 106 NaN NaN +52.8 57.7 78.3 105 NaN NaN +51.8 57.2 78.6 NaN NaN NaN +57.2 62.1 89.3 NaN NaN NaN +66.8 93.5 103 NaN NaN NaN +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile13_25hr04_Oct_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile13_25hr04_Oct_2011 +%created: 13_25hr04_Oct_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [33.8 32.5 44.1 68.4 84.1 69]; +x.ShortTone = [41.7 40.6 51.1 79.1 90.3 79.7]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +48 48.4 66.2 91.1 NaN 82.4 +55.1 53.6 60.7 92 NaN 91.5 +57.3 55 71.1 95.7 NaN 89.7 +64.4 65.8 64.6 98.8 NaN 82.3 +59.8 70.9 80.1 96 NaN 90.3 +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +56.3 76.4 86.4 91.5 104 102 +52.2 64.1 74.6 95.1 NaN NaN +48.8 52.8 59.2 92.9 NaN 98 +49.4 52 58.3 92.5 NaN 79 +49.7 47.7 61.2 95.5 NaN 87.8 +50.7 56.8 74.9 NaN NaN 94.9 +56.4 91.5 99.3 NaN NaN NaN +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile14_39hr04_Oct_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile14_39hr04_Oct_2011 +%created: 14_39hr04_Oct_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [27.1 24.1 32.5 52.6 63.4 51.3]; +x.ShortTone = [33.6 33.9 43.7 67.7 80.9 62.8]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +42 40.4 59.7 73.9 88.2 51.5 +44.1 47.7 57.7 77.6 93.8 40.2 +47.2 49.3 68 85.9 97.6 44.6 +55.2 54.9 72.4 86.1 96.6 55.8 +66.4 64 83.3 87.1 101 58 +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +44.5 73.9 86.4 84.9 94.8 81.2 +43.2 58.5 77 86.7 98.3 102 +43.9 50.9 61.4 72 99.3 51.3 +39.9 46.8 57.6 56 92.4 50.7 +40.3 44 62 78.2 88.7 40.3 +36.9 51.8 78.3 99.2 NaN 73.2 +51.2 89.5 98.1 NaN NaN NaN +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile14_59hr10_Nov_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile14_59hr10_Nov_2011 +%created: 14_59hr10_Nov_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [16.6 14 12.6 15.5 20.3 27.6]; +x.ShortTone = [17.9 16.5 15.4 18.5 26.3 32.9]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +28.7 30.7 34.8 46 40.9 52.4 +36.3 45.1 49.3 54.1 55 49.5 +73.5 82.5 80.2 59.8 54.7 68.4 +94.1 88.7 85.5 78.1 71.5 76.8 +97.9 91.6 89.2 78.5 75.2 91.1 +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +32.7 57.1 72.5 71.7 72 83.3 +30.7 39.3 50.9 69.7 81.3 89.2 +28.8 32.2 35.2 42.3 57.6 53 +28.4 30.6 32.2 38.7 39.2 46.7 +28.2 30.2 38.4 40.9 44.5 49.8 +28.3 34.2 55.6 79.7 71.3 88.7 +30.3 55.6 86.3 91.5 97.1 103 +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile14_9hr20_Sep_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile14_9hr20_Sep_2011 +%created: 14_9hr20_Sep_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [10.7 6.17 2.35 2.12 4.94 10.6]; +x.ShortTone = [15.2 11.4 7.7 8.55 12.3 17.7]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +27.9 23.1 19 28.7 25.7 26.5 +36.7 30.3 26.8 34.1 33.3 34.8 +44.8 41.1 47.5 61.2 38.3 38.1 +54.8 59.5 66.4 65.4 51 51.4 +79.2 80.3 75.1 80.5 63 41.3 +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +34.3 57.9 67.5 67.7 65.3 72.2 +31.7 41.5 47.6 66.9 72.1 79.7 +29.2 27.3 22.1 24.9 27.6 30.3 +27.8 22.3 19.4 24.3 24.5 28.7 +27.8 22.6 18.4 23.3 25.2 30.9 +28.8 23.7 24.7 43.5 36.1 45.8 +32.7 39.1 76.1 82.8 88.1 94.8 +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile15_22hr04_Oct_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile15_22hr04_Oct_2011 +%created: 15_22hr04_Oct_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [11.1 7.48 8.71 8.55 14.3 18.4]; +x.ShortTone = [16.1 13.6 14.2 15.1 21 27.3]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +26.7 30.8 33.1 28.5 37.3 50.4 +35.1 33.5 41.6 34.8 44.2 57.1 +44.6 41.8 49.8 39.5 75.1 80.9 +53.8 57.8 61 37.2 71 89.3 +64.7 62.9 63.4 52.2 81.5 97.9 +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +34.9 62.4 79.4 80.7 83.6 94.1 +32.8 42 61.6 78.3 87.9 99.3 +29.2 28 34.4 27.3 46.9 58.5 +29.5 28.1 31 36 41 42.8 +29.6 28.1 37.1 23.2 32.3 50.2 +32.4 35.1 46.5 52.2 59.4 88.7 +37.7 52.5 92.7 97 107 NaN +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile16_0hr04_Oct_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile16_0hr04_Oct_2011 +%created: 16_0hr04_Oct_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [70 68 66.5 70.6 75.7 81.9]; +x.ShortTone = [78.6 73.9 72.5 75.7 82 88.8]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +92.4 89.6 91.5 94.6 101 NaN +95.1 90.6 92.8 99.3 NaN NaN +95.2 90.7 94.2 99.5 NaN NaN +95 91.4 94.6 102 NaN NaN +96.7 91.9 97.2 NaN NaN NaN +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +99.7 95.7 91.8 91.5 94.5 103 +97.4 93.3 90 93.1 97.9 104 +94.4 90 89.9 96.5 99.8 NaN +93 89.4 90.7 97.2 103 NaN +92.1 88.6 92.3 98 102 NaN +90.1 89.5 96.1 103 NaN NaN +89 92.6 103 NaN NaN NaN +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile16_31hr04_Oct_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile16_31hr04_Oct_2011 +%created: 16_31hr04_Oct_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [65.9 64.4 61.8 65 67.5 69]; +x.ShortTone = [71.9 68.3 65.8 69.7 75.3 81.6]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +86 84.2 84.7 91 95.8 99.8 +88.1 84.1 85.6 92.4 97.4 103 +88.7 86.6 87.4 95.1 98.7 103 +89.1 87.2 88.1 95.4 101 NaN +90.5 88 89.6 96.8 103 NaN +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +94.4 91.1 86 83.8 86.1 95.9 +90.8 87.7 82.7 87.7 90.8 95.9 +87.3 84.6 83 89.1 92 97.9 +86.4 84 84.5 90.4 94.3 99.6 +85.4 82.7 85.3 92 96 102 +83.2 83.1 89.5 96.8 100 NaN +83.2 86.9 95.4 103 NaN 103 +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile16_43hr14_Nov_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile16_43hr14_Nov_2011 +%created: 16_43hr14_Nov_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [64.1 57.3 57.3 60.1 66.1 74.9]; +x.ShortTone = [66.6 60.3 60.2 62.6 68.7 78.1]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +92.8 84.9 83.6 83.9 85.4 94.1 +100 93.4 93.2 88.2 87.6 96.8 +NaN 96.3 96.1 91.8 89.8 98.7 +NaN 97.9 102 97.1 95.4 101 +NaN 101 NaN NaN 97.5 NaN +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +98 89.5 83.2 76.7 78.1 87.8 +96.1 86.3 83.1 80.2 81.6 89.3 +94.4 85.1 82.8 82.8 84.9 91.3 +92.1 83.9 83.2 82.4 85.9 94.4 +90.7 85.6 84.7 84.9 86 94.7 +90.5 86.6 88.7 88.3 91 101 +90.1 91.3 94.3 95.6 95.9 106 +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile19_7hr09_Oct_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile19_7hr09_Oct_2011 +%created: 19_7hr09_Oct_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [4.55 -0.275 -0.15 2.09 5.95 9.32]; +x.ShortTone = [7.73 5.16 3.75 6.61 13.1 17.1]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +18 16.2 16.3 25.8 36.3 32.3 +20.9 20.2 17.6 45.2 42.5 32.3 +22.8 24 29.6 44.6 39.6 34.7 +33.8 35.2 24.5 58 55.3 33.3 +54.1 47.6 47.9 66.9 68.1 37.1 +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +23.3 39.5 60.5 72.1 74.4 80.8 +21 24.9 33.6 69 80.3 85.8 +19.1 18.1 18.4 31.7 44.9 36.1 +19.2 16.4 15.4 24 32.9 29.5 +17.9 16.8 15.4 36 28.9 31.1 +18.7 17.1 28.9 46.9 50.3 45.9 +21.4 29.1 84.2 91.8 97 80.8 +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile20_5hr09_Oct_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile20_5hr09_Oct_2011 +%created: 20_5hr09_Oct_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [1.84 -0.775 -3.26 -1.62 5.65 10.4]; +x.ShortTone = [7.18 5.31 3.18 6.1 13.2 18.4]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +17.2 16.5 14 21 25.8 30.2 +17.8 16.4 15 27.9 33.8 31.8 +18.9 18.7 15.4 31.8 36.6 35.7 +21.1 19.2 19 42.3 58.1 34.4 +21.5 21.9 22.6 57.3 61.3 45.6 +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +21 34.6 57.7 70.4 73.1 81.7 +18.8 22.7 26.9 51 78.6 87.6 +17.8 17.7 15.7 19.5 35.1 33.1 +17.2 14.7 13.6 26.7 27.2 31.1 +16.7 14.9 13.1 22 30.7 32.9 +16.6 15.7 20.5 41.3 60 50.2 +19.4 24.2 82.7 89 97.6 103 +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile23_35hr09_Oct_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile23_35hr09_Oct_2011 +%created: 23_35hr09_Oct_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [4.82 -0.118 -1.64 0.904 6.77 12.7]; +x.ShortTone = [7.33 5.53 4.97 7.39 11.8 18.9]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +23.5 26.3 46.9 58.2 32.4 40.6 +35.6 56.1 69.3 67.7 49.3 47 +70.6 68.1 77.9 67.6 54.4 58.9 +77.3 78.5 81.6 79.9 65.9 64.6 +84.5 80.5 82.7 86.5 62.4 72.7 +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +28.5 64.5 78.3 75.4 75.4 86.9 +26.1 42.3 64.9 76.9 81.3 90.9 +22.3 31 50.4 60.2 47.5 53.9 +24 28.2 52 59.8 45.9 37.2 +23.1 32.1 51.2 56.6 45.7 42.5 +23.9 34.1 75.1 85.1 57.1 66 +32.3 79.1 91.2 95.8 96.7 105 +]; +x.IFMCs = x.IFMCs';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/profiles/MTprofile9_34hr18_Nov_2011.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x = MTprofile9_34hr18_Nov_2011 +%created: 9_34hr18_Nov_2011 + +x.BFs = [250 500 1000 2000 4000 8000]; + +x.LongTone = [23.4 22 22.6 52.7 66.9 55.7]; +x.ShortTone = [25.5 23.5 27.6 64.8 72.3 65.7]; + +x.Gaps = [0.01 0.03 0.05 0.07 0.09]; +x.TMCFreq = [250 500 1000 2000 4000 8000]; +x.TMC = [ +40.2 47 31.2 76.5 82.6 72.6 +51.3 57.9 39.4 78 86.9 79.3 +69.4 70.4 40.8 81.1 88.6 83.8 +81.4 79 45.4 85.1 91.4 86.7 +93.9 90.7 59.8 85.1 95.3 84.5 +]; +x.TMC = x.TMC'; + +x.MaskerRatio = [0.5 0.7 0.9 1 1.1 1.3 1.6]; +x.IFMCFreq = [250 500 1000 2000 4000 8000]; +x.IFMCs = [ +49.7 68.2 66.7 77.6 87.9 90.1 +47.1 56.2 51 82.5 90.5 94.2 +41.4 46.5 28.9 75.9 89.1 85.4 +39 45.2 27.4 75.5 84.3 73.2 +40.8 47.6 35.4 82.2 87.6 69.6 +41.4 80.6 77.9 93.1 99.9 102 +48 85.7 87.7 98.5 NaN NaN +]; +x.IFMCs = x.IFMCs';
--- a/testPrograms/LiebermanMOCdata.m Thu Oct 06 15:43:20 2011 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,79 +0,0 @@ -function LibermanData= LiebermanMOCdata - -LibermanData=[ - 2 0.2; -2.1 0.19; -2.2 0.18; -2.3 0.18; -2.4 0.16; -2.5 0.15; -2.6 0.15; -2.7 0.15; -2.8 0.12; -2.9 0.12; -3 0.1; -3.1 0.1; -3.2 0.05; -3.3 0.05; -3.4 0; -3.5 -0.1; -3.6 -0.4; -3.7 -1.2; -3.8 -1.6; -3.9 -1.8; -4 -1.85; -4.1 -2; -4.2 -2.05; -4.3 -2.05; -4.4 -2.15; -4.5 -2.2; -4.6 -2.15; -4.7 -2.1; -4.8 -2.15; -4.9 -2.2; -5 -2.1; -5.1 -2.1; -5.2 -2.25; -5.3 -2.1; -5.4 -2.15; -5.5 -2.1; -5.6 -2.15; -5.7 -2.1; -5.8 -2.2; -5.9 -2.05; -6 -2.15; -6.1 -2.05; -6.2 -2; -6.3 -2.2; -6.4 -2.1; -6.5 -2.05; -6.6 -2.05; -6.7 -2.05; -6.8 -2.2; -6.9 -2.1; -7 -2.05; -7.1 -2.05; -7.2 -0.7; -7.3 -0.1; -7.4 0; -7.5 0.1; -7.6 0.2; -7.7 0.35; -7.8 0.2; -7.9 0.15; -8 0.2; -8.1 0.15; -8.2 0.15; -8.3 0.15; -8.4 0.12; -8.5 0.1; -8.6 0.09; -8.7 0.08; -8.8 0.07; -8.9 0.06; -9 0.05; -]; -figure(98), subplot(2,1,2), hold on -scalar=-15; -plot(LibermanData(:,1)-2.5,scalar*LibermanData(:,2)) -
--- a/testPrograms/LiebermanMOCtest.m Thu Oct 06 15:43:20 2011 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,155 +0,0 @@ -function LiebermanMOCtest - -% In test_MAP1_14.m set these parameters - -% AN_spikesOrProbability='spikes'; - -% global dt ANdt savedBFlist saveAN_spikesOrProbability saveMAPparamsName... -% savedInputSignal OMEextEarPressure TMoutput OMEoutput ARattenuation ... -% DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... -% IHCoutput ANprobRateOutput ANoutput savePavailable ANtauCas ... -% CNtauGk CNoutput ICoutput ICmembraneOutput ICfiberTypeRates ... -% MOCattenuation -global dt ANdt saveAN_spikesOrProbability ANprobRateOutput ICoutput - -global DRNLParams - -LibermanData=[ - 2 0.2; -2.1 0.19;2.2 0.18;2.3 0.18;2.4 0.16;2.5 0.15;2.6 0.15;2.7 0.15; -2.8 0.12;2.9 0.12;3 0.1;3.1 0.1;3.2 0.05;3.3 0.05;3.4 0;3.5 -0.1; -3.6 -0.4;3.7 -1.2;3.8 -1.6;3.9 -1.8;4 -1.85;4.1 -2;4.2 -2.05; -4.3 -2.05;4.4 -2.15;4.5 -2.2;4.6 -2.15;4.7 -2.1;4.8 -2.15;4.9 -2.2; -5 -2.1;5.1 -2.1;5.2 -2.25;5.3 -2.1;5.4 -2.15;5.5 -2.1;5.6 -2.15; -5.7 -2.1;5.8 -2.2;5.9 -2.05;6 -2.15;6.1 -2.05;6.2 -2;6.3 -2.2;6.4 -2.1; -6.5 -2.05;6.6 -2.05;6.7 -2.05;6.8 -2.2;6.9 -2.1;7 -2.05;7.1 -2.05;7.2 -0.7; -7.3 -0.1;7.4 0;7.5 0.1;7.6 0.2;7.7 0.35;7.8 0.2;7.9 0.15;8 0.2;8.1 0.15;8.2 0.15; -8.3 0.15;8.4 0.12;8.5 0.1;8.6 0.09;8.7 0.08;8.8 0.07;8.9 0.06;9 0.05; -]; - -restorePath=path; -addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... - ['..' filesep 'utilities']) - -%% #1 parameter file name -MAPparamsName='Normal'; - - -%% #2 probability (fast) or spikes (slow) representation -AN_spikesOrProbability='spikes'; -% or -% AN_spikesOrProbability='probability'; - - -%% #3 pure tone, harmonic sequence or speech file input -signalType= 'tones'; -sampleRate= 50000; -rampDuration=.005; % raised cosine ramp (seconds) -toneFrequency= 1000; % or a pure tone (Hz) -duration=3.6; % Lieberman test -beginSilence=1; % 1 for Lieberman -endSilence=1; % 1 for Lieberman - -%% #4 rms level -% signal details -leveldBSPL= 80; % dB SPL (80 for Lieberman) - - -%% #5 number of channels in the model - -numChannels=1; -BFlist=toneFrequency; - - -%% #6 change model parameters -paramChanges={}; - -%% delare 'showMap' options to control graphical output -showMapOptions.printModelParameters=1; % prints all parameters -showMapOptions.showModelOutput=1; % plot of all stages -showMapOptions.printFiringRates=1; % prints stage activity levels -showMapOptions.showACF=0; % shows SACF (probability only) -showMapOptions.showEfferent=1; % tracks of AR and MOC -showMapOptions.surfProbability=1; % 2D plot of HSR response -showMapOptions.surfSpikes=0; % 2D plot of spikes histogram -showMapOptions.ICrates=0; % IC rates by CNtauGk - -%% Generate stimuli - - % Create pure tone stimulus - dt=1/sampleRate; % seconds - time=dt: dt: duration; - inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); - amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) - inputSignal=amp*inputSignal; - % apply ramps - % catch rampTime error - if rampDuration>0.5*duration, rampDuration=duration/2; end - rampTime=dt:dt:rampDuration; - ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... - ones(1,length(time)-length(rampTime))]; - inputSignal=inputSignal.*ramp; - ramp=fliplr(ramp); - inputSignal=inputSignal.*ramp; - % add silence - intialSilence= zeros(1,round(beginSilence/dt)); - finalSilence= zeros(1,round(endSilence/dt)); - inputSignal= [intialSilence inputSignal finalSilence]; - -%% run the model -tic -fprintf('\n') -disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) -disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) -disp('Computing ...') - -MAP1_14(inputSignal, sampleRate, BFlist, ... - MAPparamsName, AN_spikesOrProbability, paramChanges); - - -%% the model run is now complete. Now display the results -UTIL_showMAP(showMapOptions, paramChanges) - -if strcmp(signalType,'tones') - disp(['duration=' num2str(duration)]) - disp(['level=' num2str(leveldBSPL)]) - disp(['toneFrequency=' num2str(toneFrequency)]) - disp(['attenuation factor =' ... - num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) - disp(['attenuation factor (probability)=' ... - num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) - disp(AN_spikesOrProbability) -end -disp(paramChanges) - - - -%% superimpose Lieberman data - -global DRNLParams -% scale up DPOAE results to a maximum of whatever MAP_14 uses as its -% maximum -scalar=-DRNLParams.minMOCattenuationdB/min(LibermanData(:,2)); - -figure(98), subplot(2,1,2), hold on -plot(LibermanData(:,1)-2.5,scalar*LibermanData(:,2),'r:') - -PSTHbinwidth=0.001; - -if strcmp(saveAN_spikesOrProbability,'probability') - PSTH=UTIL_PSTHmaker... - (ANprobRateOutput(2,:), ANdt, PSTHbinwidth)*ANdt/PSTHbinwidth; -else - PSTH=UTIL_PSTHmaker(ICoutput(2,:), dt, PSTHbinwidth)*dt/PSTHbinwidth; -end - -time=PSTHbinwidth:PSTHbinwidth:PSTHbinwidth*length(PSTH); -figure(89) -plot(time, PSTH) -set(gcf,'name', 'Lieberman') -title(saveAN_spikesOrProbability) - -toc -path(restorePath) - -% figure(88), plot(MOCattenuation)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/MAPtwoToneDemo.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,152 @@ +% function MAPtwoToneDemo +% +% Demonstration of two-tone suppression in the AN using a +% F1 tone suppressed by a 1.5*F1 kHz tone +% + +global ANprobRateOutput DRNLoutput +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) +figure(5), clf + +disp('') +primaryToneFrequency=2000; + +probedBs=[-100 20 :20: 90]; +nProbeLevels=length(probedBs); + +% disp(['F1 F1 level= ' num2str([primaryToneFrequency probedB])]) + +suppressorLevels=0:10:80; +suppressorLevels=0:10:80; + +lowestBF=250; highestBF= 8000; numChannels=21; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +suppressorFrequencies=BFlist; +BFchannel=find(BFlist==primaryToneFrequency); + +sampleRate= 40000; % Hz (higher sample rate needed for BF>8000 Hz) +dt=1/sampleRate; % seconds + +duration=.050; % seconds +tone2Duration=duration/2; % s +startSilenceDuration=0.010; +startSilence=... + zeros(1,startSilenceDuration*sampleRate); +suppressorStartsPTR=... + round((startSilenceDuration+duration/2)*sampleRate); + +probedBCount=0; +for probedB=probedBs + probedBCount=probedBCount+1; + + peakResponse= zeros(length(suppressorLevels),length(suppressorFrequencies)); + suppFreqCount=0; + for suppressorFrequency=suppressorFrequencies + suppFreqCount=suppFreqCount+1; + + suppressorLevelCount=0; + for suppressorDB=suppressorLevels + suppressorLevelCount=suppressorLevelCount+1; + + % primary + suppressor + primaryLevelDB=probedB; + suppressorLevelDB=suppressorDB; + + + % primary BF tone + time1=dt: dt: duration; + amp=10^(primaryLevelDB/20)*28e-6; + inputSignal=amp*sin(2*pi*primaryToneFrequency*time1); + rampDuration=.005; rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ones(1,length(time1)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + inputSignal=inputSignal.*fliplr(ramp); + + % suppressor + time2= dt: dt: tone2Duration; + % B: tone on + amp=10^(suppressorLevelDB/20)*28e-6; + inputSignal2=amp*sin(2*pi*suppressorFrequency*time2); + rampDuration=.005; rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ones(1,length(time2)-length(rampTime))]; + inputSignal2=inputSignal2.*ramp; + inputSignal2=inputSignal2.*fliplr(ramp); + % A: initial silence (delay to suppressor) + silence=zeros(1,length(time2)); + inputSignal2=[silence inputSignal2]; + + % add tone and suppressor components + inputSignal=inputSignal+inputSignal2; + + inputSignal=... + [startSilence inputSignal ]; + + % run MAP + MAPparamsName='Normal'; + AN_spikesOrProbability='probability'; + paramChanges={'IHCpreSynapseParams.tauCa=80e-6;'}; + MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + response=ANprobRateOutput; + peakResponse(suppressorLevelCount,suppFreqCount)=... + mean(response(BFchannel,suppressorStartsPTR:end)); + disp(['F2 level= ', num2str([suppressorFrequency suppressorDB ... + peakResponse(suppressorLevelCount,suppFreqCount)])]) + + + %% make movie + figure(6), subplot(4,1,2) + axis tight + set(gca,'nextplot','replacechildren'); + plot(dt:dt:dt*length(inputSignal), inputSignal, 'k') + title('probe - suppressor') + ylim([-.5 .5]) + + figure(6), subplot(2,1,2) + PSTHbinWidth=0.010; + PSTH= UTIL_PSTHmakerb(... + response, dt, PSTHbinWidth); + [nY nX]=size(PSTH); + time=PSTHbinWidth*(0:nX-1); + surf(time, BFlist, PSTH) + zlim([0 500]), + caxis([0 500]) + shading interp + colormap(jet) + set(gca, 'yScale','log') + xlim([0 max(time)+dt]) + ylim([0 max(BFlist)]) + view([0 90]) % view([-8 68]) + title('probe - suppressor') + pause(0.05) + end + end + + %% plot matrix + + peakResponse=peakResponse/peakResponse(1,1); + + figure(5), subplot(2,nProbeLevels, probedBCount) + surf(suppressorFrequencies,suppressorLevels,peakResponse) + shading interp + view([0 90]) + set(gca,'Xscale','log') + xlim([min(suppressorFrequencies) max(suppressorFrequencies)]) + set(gca,'Xtick', [1000 4000],'xticklabel',{'1000', '4000'}) + ylim([min(suppressorLevels) max(suppressorLevels)]) + + subplot(2,nProbeLevels,nProbeLevels+probedBCount) + contour(suppressorFrequencies,suppressorLevels,peakResponse, ... + [.1:.1:.9 1.1] ) + set(gca,'Xscale','log') + set(gca,'Xtick', [1000 4000],'xticklabel',{'1000', '4000'}) + set(gcf, 'name',['primaryToneFrequency= ' num2str(primaryToneFrequency)]) + title([num2str(probedB) ' dB']) +end + +path=restorePath;
--- a/testPrograms/demoTwisterProbability.m Thu Oct 06 15:43:20 2011 +0100 +++ b/testPrograms/demoTwisterProbability.m Mon Nov 28 13:34:28 2011 +0000 @@ -63,9 +63,12 @@ showMapOptions.printFiringRates=1; showMapOptions.showACF=0; showMapOptions.showEfferent=0; -showMapOptions.surfProbability=1; % 2D plot of HSR response +showMapOptions.surfAN=1; % 3D plot of HSR response +showMapOptions.PSTHbinwidth=0.002; % 3D plot of HSR response +showMapOptions.view=[-14 76]; % 3D plot of HSR response -UTIL_showMAP(showMapOptions, paramChanges) + +UTIL_showMAP(showMapOptions) toc path(restorePath)
--- a/testPrograms/demoTwisterSpikes.m Thu Oct 06 15:43:20 2011 +0100 +++ b/testPrograms/demoTwisterSpikes.m Mon Nov 28 13:34:28 2011 +0000 @@ -27,13 +27,13 @@ %% #5 number of channels in the model % 21-channel model (log spacing) -numChannels=21; +numChannels=11; lowestBF=250; highestBF= 8000; BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); %% #6 change model parameters -paramChanges=[]; +paramChanges={}; %% delare showMap options showMapOptions=[]; % use defaults @@ -45,7 +45,7 @@ showMapOptions.showACF=0; showMapOptions.showEfferent=0; showMapOptions.surfSpikes=0; -showMapOptions.surfProbability=0; % 2D plot of HSR response +showMapOptions.surfAN=0; % 2D plot of HSR response %% Generate stimuli [inputSignal sampleRate]=wavread(fileName); @@ -69,7 +69,7 @@ % the model run is now complete. Now display the results -UTIL_showMAP(showMapOptions, paramChanges) +UTIL_showMAP(showMapOptions) toc path(restorePath)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/oldTestPrograms/LiebermanMOCdata.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,79 @@ +function LibermanData= LiebermanMOCdata + +LibermanData=[ + 2 0.2; +2.1 0.19; +2.2 0.18; +2.3 0.18; +2.4 0.16; +2.5 0.15; +2.6 0.15; +2.7 0.15; +2.8 0.12; +2.9 0.12; +3 0.1; +3.1 0.1; +3.2 0.05; +3.3 0.05; +3.4 0; +3.5 -0.1; +3.6 -0.4; +3.7 -1.2; +3.8 -1.6; +3.9 -1.8; +4 -1.85; +4.1 -2; +4.2 -2.05; +4.3 -2.05; +4.4 -2.15; +4.5 -2.2; +4.6 -2.15; +4.7 -2.1; +4.8 -2.15; +4.9 -2.2; +5 -2.1; +5.1 -2.1; +5.2 -2.25; +5.3 -2.1; +5.4 -2.15; +5.5 -2.1; +5.6 -2.15; +5.7 -2.1; +5.8 -2.2; +5.9 -2.05; +6 -2.15; +6.1 -2.05; +6.2 -2; +6.3 -2.2; +6.4 -2.1; +6.5 -2.05; +6.6 -2.05; +6.7 -2.05; +6.8 -2.2; +6.9 -2.1; +7 -2.05; +7.1 -2.05; +7.2 -0.7; +7.3 -0.1; +7.4 0; +7.5 0.1; +7.6 0.2; +7.7 0.35; +7.8 0.2; +7.9 0.15; +8 0.2; +8.1 0.15; +8.2 0.15; +8.3 0.15; +8.4 0.12; +8.5 0.1; +8.6 0.09; +8.7 0.08; +8.8 0.07; +8.9 0.06; +9 0.05; +]; +figure(98), subplot(2,1,2), hold on +scalar=-15; +plot(LibermanData(:,1)-2.5,scalar*LibermanData(:,2)) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/oldTestPrograms/MAPtwoToneDemo1D.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,126 @@ +% function MAPtwoToneDemo +% Not for distribution but code worth keeping +% Demonstration of two-tone suppression in the AN using a +% single channel +% + +dbstop if error +% create access to all MAP 1_8 facilities +% addpath ('..\modules', '..\utilities', '..\parameterStore', '..\wavFileStore' , '..\testPrograms') +addpath (['..' filesep 'modules'], ['..' filesep 'utilities'], ['..' filesep 'parameterStore'], ['..' filesep 'wavFileStore'] , ['..' filesep 'testPrograms']) + +moduleSequence= 1:7; % up to the AN +figure(3), clf +primaryToneFrequency=2000; +suppressorFrequency=primaryToneFrequency*1.5; + +primaryDB=30; +suppressors=20:10:80; +frameCount=0; +for suppressorDB=suppressors + frameCount=frameCount+1; + lowestBF=1000; highestBF= 5000; numChannels=30; + BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + BFlist=2000; + duration=.020; % seconds + sampleRate= 40000; % Hz (higher sample rate needed for BF>8000 Hz) + dt=1/sampleRate; % seconds + + % for conditionNo=1:3 % probe alone/ suppressor alone/ combined + for conditionNo=1:3 % probe alone/ suppressor alone/ combined + switch conditionNo + case 1 + primaryLevelDB=primaryDB; + suppressorLevelDB= -100; + plotGuide.subPlotNo= 1; % initialize subplot count + figureTitle=['probe alone: ' num2str(primaryToneFrequency) ' Hz; ' num2str(primaryLevelDB) ' dB SPL']; + + case 2 + primaryLevelDB=-100; + suppressorLevelDB=suppressorDB; + plotGuide.subPlotNo= 2; % initialize subplot count + figureTitle=['suppressor alone: ' num2str(suppressorFrequency) ' Hz; ' num2str(suppressorLevelDB) ' dB SPL']; + case 3 + primaryLevelDB=primaryDB; + suppressorLevelDB=suppressorDB; + plotGuide.subPlotNo= 3; % initialize subplot count + figureTitle=['probe + suppressor']; + end + + % primary BF tone + time1=dt: dt: duration; + amp=10^(primaryLevelDB/20)*28e-6; + inputSignal=amp*sin(2*pi*primaryToneFrequency*time1); + rampDuration=.005; rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ones(1,length(time1)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + inputSignal=inputSignal.*fliplr(ramp); + + % suppressor + tone2Duration=duration/2; % s + time2= dt: dt: tone2Duration; + % B: tone on + amp=10^(suppressorLevelDB/20)*28e-6; + inputSignal2=amp*sin(2*pi*suppressorFrequency*time2); + rampDuration=.005; rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ones(1,length(time2)-length(rampTime))]; + inputSignal2=inputSignal2.*ramp; + % A: initial silence (delay to suppressor) + silence=zeros(1,length(time2)); + inputSignal2=[silence inputSignal2]; + + % add tone and suppressor components + inputSignal=inputSignal+inputSignal2; + + % specify model parameters + method=MAPparamsDEMO(BFlist, sampleRate); + % parameter change (must be global to take effect) + global AN_IHCsynapseParams + AN_IHCsynapseParams.mode= 'probability'; +% method.useEfferent=0; + + method.plotGraphs= 1; % please plot + + figure(4), subplot(3,1,conditionNo) + plot(time1, inputSignal, 'k')% ************* + + [ANresponse, method, A]=MAPsequenceSeg(inputSignal, method, moduleSequence); + response{conditionNo}=ANresponse; +% F(frameCount) = getframe(gcf); + end + +% peakResponse=max(max([response{1} response{2} response{3}])); +% figure(3), subplot(4,1,1) +% mesh((response{1}), [0 peakResponse]) +% title(['primary: ' num2str(primaryDB) ' dB SPL']) +% zlim([0 5000]), view([-28 36]) +% +% figure(3), subplot(4,1,2) +% mesh((response{2}), [0 peakResponse]) +% title(['suppressor: ' num2str(suppressorDB) ' dB SPL']) +% zlim([0 5000]), view([-28 36]) +% +% figure(3), subplot(4,1,3) +% mesh((response{3}), [0 peakResponse]) +% title([' primary + suppressor ']) +% zlim([0 5000]), view([-28 36]) +% +% figure(3), subplot(4,1,4) +% sum= response{3}-response{1}-response{2}; +% mesh(sum) +% zlim([-2000 2000]) +% title(' difference matrix (combined - primary - suppressor)') +% view([-28 20]) +% disp( [num2str([ suppressorDB primaryDB max(max(sum))])] ) +% +% colormap(jet) + pause(1) +end + +figure(4) +xlabel('time (s)') +figure(3) +xlabel('time (s)') + +% figure(99), movie(F, 1, 1) +% UTIL_showAllMAPStructures \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/openGlobals.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,12 @@ +global OMEParams DRNLParams IHC_cilia_RPParams IHCpreSynapseParams +global AN_IHCsynapseParams MacGregorParams MacGregorMultiParams + +% All of the results of this function are stored as global +global savedParamChanges savedBFlist saveAN_spikesOrProbability ... + saveMAPparamsName savedInputSignal dt dtSpikes ... + OMEextEarPressure TMoutput OMEoutput DRNLoutput... + IHC_cilia_output IHCrestingCiliaCond IHCrestingV... + IHCoutput ANprobRateOutput ANoutput savePavailable saveNavailable ... + ANtauCas CNtauGk CNoutput ICoutput ICmembraneOutput ICfiberTypeRates... + MOCattenuation ARattenuation +
--- a/testPrograms/repeatTester.m Thu Oct 06 15:43:20 2011 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,52 +0,0 @@ -restorePath=path; -addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... - ['..' filesep 'utilities']) - -global OMEParams DRNLParams IHC_cilia_RPParams IHCpreSynapseParams -global AN_IHCsynapseParams MacGregorParams MacGregorMultiParams -global dt ANdt savedBFlist saveAN_spikesOrProbability saveMAPparamsName... - savedInputSignal OMEextEarPressure TMoutput OMEoutput ARattenuation ... - DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... - IHCoutput ANprobRateOutput ANoutput savePavailable ANtauCas ... - CNtauGk CNoutput ICoutput ICmembraneOutput ICfiberTypeRates ... - MOCattenuation - -signalCharacteristics.type='tones'; -signalCharacteristicssignalCharacteristics.sampleRate=50000; -signalCharacteristics.duration= 1; -signalCharacteristics.rampDuration=0.05; -signalCharacteristics.beginSilence=0.05; -signalCharacteristics.endSilence=0.05; -signalCharacteristics.toneFrequency=1000; -signalCharacteristics.leveldBSPL=50; - -showMapOptions.printModelParameters=0; % prints all parameters -showMapOptions.showModelOutput=0; % plot of all stages -showMapOptions.printFiringRates=0; % prints stage activity levels -showMapOptions.showACF=0; % shows SACF (probability only) -showMapOptions.showEfferent=0; % tracks of AR and MOC -showMapOptions.surfProbability=0; % 2D plot of HSR response -showMapOptions.surfSpikes=0; % 2D plot of spikes histogram -showMapOptions.ICrates=0; % IC rates by CNtauGk - -tic -fprintf('\n') -disp('Computing ...') - -levels=80:10:100; - figure(10), clf - -for level=levels -signalCharacteristics.leveldBSPL=level; -% MAPrunner(MAPparamsName, AN_spikesOrProbability, ... -% signalCharacteristics, paramChanges, showMapOptions) -MAPrunner('Normal', 'spikes', ... - signalCharacteristics, {}, showMapOptions) -ARmin=min(ARattenuation); -disp([num2str(level) ':' num2str(ARmin)]) -time=dt:dt:dt*length(ARattenuation); -hold on, plot(time,(1-ARattenuation)/(1-min(ARattenuation))) -pause(0.1) -end - -path(restorePath)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/runMAP1_14.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,190 @@ +function runMAP1_14 +% runMAP1_14 is a general purpose test routine that can be adjusted to +% explore different applications of MAP1_14 +% +% It is also designed as 'starter' code for building new applications. +% +% A range of options are supplied in the early part of the program +% +% #1 +% Identify the file (in 'MAPparamsName') containing the model parameters +% +% #2 +% Identify the kind of model required (in 'AN_spikesOrProbability'). +% A full brainstem model ('spikes') can be computed or a shorter model +% ('probability') that computes only so far as the auditory nerve +% +% #3 +% Choose between a tone signal or file input (in 'signalType') +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the channels in terms of their best frequencies in the vector +% BFlist. +% +% #6 +% Last minute changes to the model parameters can be made using +% a cell array of strings, 'paramChanges'. + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation: select one +% AN_spikesOrProbability='spikes'; +% or +AN_spikesOrProbability='probability'; + + +%% #3 A. pure tone, B. harmonic sequence or C. speech file input +% comment out unwanted code + +% A. tone +sampleRate= 95000; +signalType= 'tones'; +toneFrequency= 1000; % or a pure tone (Hz) +duration=0.500; % seconds +beginSilence=0.010; +endSilence=0.020; +rampDuration=.005; % raised cosine ramp (seconds) + +% or +% B. harmonic tone (Hz) - useful to demonstrate a broadband sound +% sampleRate= 44100; +% signalType= 'tones'; +% toneFrequency= F0:F0:8000; +% duration=0.500; % seconds +% beginSilence=0.250; +% endSilence=0.250; +% F0=210; +% rampDuration=.005; % raised cosine ramp (seconds) + +% or +% C. signalType= 'file'; +% fileName='twister_44kHz'; + +%% #4 rms level +% signal details +leveldBSPL= 80; % dB SPL (80 for Lieberman) + +%% #5 number of channels in the model +% 21-channel model (log spacing) +numChannels=21; +lowestBF=100; highestBF= 6000; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +% or specify your own channel BFs +% numChannels=1; +% BFlist=toneFrequency; + + +%% #6 change model parameters + +paramChanges={}; % no changes + +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read +% Each string must have the same format as the corresponding line in the +% file identified in 'MAPparamsName' +% This example declares only one fiber type with a calcium clearance time +% constant of 80e-6 s (HSR fiber) when the probability option is selected. +% paramChanges={'AN_IHCsynapseParams.ANspeedUpFactor=5;', ... +% 'IHCpreSynapseParams.tauCa=86e-6; '}; +paramChanges={ 'IHCpreSynapseParams.tauCa=86e-6; ',... + 'DRNLParams.rateToAttenuationFactorProb = 0;'}; + + + +%% delare 'showMap' options to control graphical output +% see UTIL_showMAP for more options +showMapOptions.printModelParameters=1; % prints all parameters +showMapOptions.showModelOutput=0; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showEfferent=0; % tracks of AR and MOC +showMapOptions.surfAN=1; % 2D plot of HSR response + +if strcmp(signalType, 'file') + % needed for labeling plot + showMapOptions.fileName=fileName; +else + showMapOptions.fileName=[]; +end + +%% Generate stimuli +switch signalType + case 'tones' + % Create pure tone stimulus + dt=1/sampleRate; % seconds + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + % catch rampTime error + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + intialSilence= zeros(1,round(0.1/dt)); + finalSilence= zeros(1,round(0.2/dt)); + inputSignal= [intialSilence inputSignal' finalSilence]; +end + + +%% run the model +tic +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) +disp('Computing ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +%% the model run is now complete. Now display the results +UTIL_showMAP(showMapOptions) + +if strcmp(signalType,'tones') + disp(['duration=' num2str(duration)]) + disp(['level=' num2str(leveldBSPL)]) + disp(['toneFrequency=' num2str(toneFrequency)]) + global DRNLParams + disp(['attenuation factor =' ... + num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) + disp(['attenuation factor (probability)=' ... + num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) + disp(AN_spikesOrProbability) +end +disp('paramChanges') +for i=1:length(paramChanges) + disp(paramChanges{i}) +end + +toc +path(restorePath) +
--- a/testPrograms/temp.m Thu Oct 06 15:43:20 2011 +0100 +++ b/testPrograms/temp.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,190 +1,332 @@ -function test_MAP1_14 -% test_MAP1_14 is a general purpose test routine that can be adjusted to -% test a number of different applications of MAP1_14 -% -% A range of options are supplied in the early part of the program -% -% One use of the function is to create demonstrations; filenames <demoxx> -% to illustrate particular features -% -% #1 -% Identify the file (in 'MAPparamsName') containing the model parameters -% -% #2 -% Identify the kind of model required (in 'AN_spikesOrProbability'). -% A full brainstem model (spikes) can be computed or a shorter model -% (probability) that computes only so far as the auditory nerve -% -% #3 -% Choose between a tone signal or file input (in 'signalType') -% -% #4 -% Set the signal rms level (in leveldBSPL) -% -% #5 -% Identify the channels in terms of their best frequencies in the vector -% BFlist. -% -% Last minute changes to the parameters fetched earlier can be made using -% the cell array of strings 'paramChanges'. -% Each string must have the same format as the corresponding line in the -% file identified in 'MAPparamsName' -% -% When the demonstration is satisfactory, freeze it by renaming it <demoxx> +function vectorStrength=testAN(probeFrequency,BFlist, levels, ... + paramsName,paramChanges) +% testAN generates rate/level functions for AN and brainstem units. +% also other information like PSTHs, MOC efferent activity levels. +% Vector strength calculations require the computation of period +% histograms. for this reason the sample rate must always be an integer +% multiple of the tone frequency. This applies to both the sampleRate and +% the spikes sampling rate. +% A full 'spikes' model is used. +% paramChanges is a cell array of strings containing MATLAB statements that +% change model parameters. See MAPparamsNormal for examples. +% e.g. +% testAN(1000,1000, -10:10:80,'Normal',{}); + +global IHC_VResp_VivoParams IHC_cilia_RPParams IHCpreSynapseParams +global AN_IHCsynapseParams +global ANoutput dtSpikes CNoutput ICoutput ICmembraneOutput ANtauCas +global ARattenuation MOCattenuation +tic dbstop if error restorePath=path; -addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... - ['..' filesep 'utilities']) +addpath (['..' filesep 'MAP'], ['..' filesep 'utilities'], ... + ['..' filesep 'parameterStore'], ['..' filesep 'wavFileStore'],... + ['..' filesep 'testPrograms']) -%% #1 parameter file name -MAPparamsName='Normal'; +if nargin<5, paramChanges={'IHCpreSynapseParams.tauCa= [30e-6 90e-6];'}; end +if nargin<4, paramsName='PL'; end +if nargin<3, levels=-10:10:80; end +if nargin==0, probeFrequency=1000; BFlist=1000; end +nLevels=length(levels); +toneDuration=.2; rampDuration=0.005; silenceDuration=.02; +localPSTHbinwidth=0.001; -%% #2 probability (fast) or spikes (slow) representation -AN_spikesOrProbability='spikes'; -% or -AN_spikesOrProbability='probability'; +%% guarantee that the sample rate is an interger multiple +% of the probe frequency +% we want 5 bins per period for spikes +spikesSampleRate=5*probeFrequency; +% model sample rate must be an integer multiple of this and in the region +% of 50000 +sampleRate=spikesSampleRate*round(50000/spikesSampleRate); +dt=1/sampleRate; +% avoid very slow spikes sampling rate +spikesSampleRate=spikesSampleRate*ceil(10000/spikesSampleRate); +%% pre-allocate storage +AN_HSRonset=zeros(nLevels,1); +AN_HSRsaturated=zeros(nLevels,1); +AN_LSRonset=zeros(nLevels,1); +AN_LSRsaturated=zeros(nLevels,1); +CNLSRrate=zeros(nLevels,1); +CNHSRsaturated=zeros(nLevels,1); +ICHSRsaturated=zeros(nLevels,1); +ICLSRsaturated=zeros(nLevels,1); +vectorStrength=zeros(nLevels,1); -%% #3 pure tone, harmonic sequence or speech file input -signalType= 'tones'; -sampleRate= 50000; -duration=0.500; % seconds -rampDuration=.005; % raised cosine ramp (seconds) -beginSilence=0.250; -endSilence=0.250; -toneFrequency= 1000; % or a pure tone (Hz) +AR=zeros(nLevels,1); +MOC=zeros(nLevels,1); -% or -% harmonic sequence (Hz) -% F0=210; -% toneFrequency= F0:F0:8000; +figure(15), clf +set(gcf,'position',[980 356 401 321]) +figure(5), clf +set(gcf,'position', [980 34 400 295]) +drawnow -% or -signalType= 'file'; -fileName='twister_44kHz'; +%% main computational loop (vary level) +levelNo=0; +for leveldB=levels + levelNo=levelNo+1; + amp=28e-6*10^(leveldB/20); + fprintf('%4.0f\t', leveldB) + %% generate tone and silences + time=dt:dt:toneDuration; + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + ramp=ramp.*fliplr(ramp); + + silence=zeros(1,round(silenceDuration/dt)); + + inputSignal=amp*sin(2*pi*probeFrequency'*time); + inputSignal= ramp.*inputSignal; + inputSignal=[silence inputSignal]; + + %% run the model + AN_spikesOrProbability='spikes'; + nExistingParamChanges=length(paramChanges); + paramChanges{nExistingParamChanges+1}=... + ['AN_IHCsynapseParams.spikesTargetSampleRate=' ... + num2str(spikesSampleRate) ';']; -%% #4 rms level -% signal details -leveldBSPL= 70; % dB SPL (80 for Lieberman) + MAP1_14(inputSignal, 1/dt, BFlist, ... + paramsName, AN_spikesOrProbability, paramChanges); + + nTaus=length(ANtauCas); + + %% Auditory nerve evaluate and display (Fig. 5) + %LSR (same as HSR if no LSR fibers present) + [nANFibers nTimePoints]=size(ANoutput); + numLSRfibers=nANFibers/nTaus; + numHSRfibers=numLSRfibers; + + LSRspikes=ANoutput(1:numLSRfibers,:); + PSTH=UTIL_PSTHmaker(LSRspikes, dtSpikes, localPSTHbinwidth); + PSTHLSR=mean(PSTH,1)/localPSTHbinwidth; % across fibers rates + PSTHtime=localPSTHbinwidth:localPSTHbinwidth:... + localPSTHbinwidth*length(PSTH); + AN_LSRonset(levelNo)= max(PSTHLSR); % peak in 5 ms window + AN_LSRsaturated(levelNo)= mean(PSTHLSR(round(length(PSTH)/2):end)); + + % AN HSR + HSRspikes= ANoutput(end- numHSRfibers+1:end, :); + PSTH=UTIL_PSTHmaker(HSRspikes, dtSpikes, localPSTHbinwidth); + PSTH=mean(PSTH,1)/localPSTHbinwidth; % sum across fibers (HSR only) + AN_HSRonset(levelNo)= max(PSTH); + AN_HSRsaturated(levelNo)= mean(PSTH(round(length(PSTH)/2): end)); + + figure(5), subplot(2,2,2) + hold off, bar(PSTHtime,PSTH, 'b') + hold on, bar(PSTHtime,PSTHLSR,'r') + ylim([0 1000]) + xlim([0 length(PSTH)*localPSTHbinwidth]) + grid on + set(gcf,'name',['PSTH: ' num2str(BFlist), ' Hz: ' num2str(leveldB) ' dB']); + + % AN - CV + % CV is computed 5 times. Use the middle one (3) as most typical + cvANHSR= UTIL_CV(HSRspikes, dtSpikes); + + % AN - vector strength + PSTH=sum(CNoutput (11:20,:)); + [PH, binTimes]=UTIL_periodHistogram... + (PSTH, dtSpikes, probeFrequency); + VS=UTIL_vectorStrength(PH); + vectorStrength(levelNo)=VS; + disp(['sat rate= ' num2str(AN_HSRsaturated(levelNo)) ... + '; phase-locking VS = ' num2str(VS)]) + title(['AN HSR: CV=' num2str(cvANHSR(3),'%5.2f') ... + 'VS=' num2str(VS,'%5.2f')]) + + %% CN - first-order neurons + + % CN driven by LSR fibers + [nCNneurons c]=size(CNoutput); + nLSRneurons=round(nCNneurons/nTaus); + CNLSRspikes=CNoutput(1:nLSRneurons,:); + PSTH=UTIL_PSTHmaker(CNLSRspikes, dtSpikes, localPSTHbinwidth); + PSTH=sum(PSTH)/nLSRneurons; +% CNLSRrate(levelNo)=mean(PSTH(round(length(PSTH)/2):end))/localPSTHbinwidth; + CNLSRrate(levelNo)=mean(PSTH)/localPSTHbinwidth; + + %CN HSR + MacGregorMultiHSRspikes=... + CNoutput(end-nLSRneurons+1:end,:); + PSTH=UTIL_PSTHmaker(MacGregorMultiHSRspikes, dtSpikes, localPSTHbinwidth); + PSTH=sum(PSTH)/nLSRneurons; + PSTH=mean(PSTH,1)/localPSTHbinwidth; % sum across fibers (HSR only) + +% CNHSRsaturated(levelNo)=mean(PSTH(length(PSTH)/2:end)); + CNHSRsaturated(levelNo)=mean(PSTH); + + figure(5), subplot(2,2,3) + bar(PSTHtime,PSTH) + ylim([0 1000]) + xlim([0 length(PSTH)*localPSTHbinwidth]) + cvMMHSR= UTIL_CV(MacGregorMultiHSRspikes, dtSpikes); + title(['CN CV= ' num2str(cvMMHSR(3),'%5.2f')]) + + %% IC LSR + [nICneurons c]=size(ICoutput); + nLSRneurons=round(nICneurons/nTaus); + ICLSRspikes=ICoutput(1:nLSRneurons,:); + PSTH=UTIL_PSTHmaker(ICLSRspikes, dtSpikes, localPSTHbinwidth); +% ICLSRsaturated(levelNo)=mean(PSTH(round(length(PSTH)/2):end))/localPSTHbinwidth; + ICLSRsaturated(levelNo)=mean(PSTH)/localPSTHbinwidth; + + %IC HSR + MacGregorMultiHSRspikes=... + ICoutput(end-nLSRneurons+1:end,:); + PSTH=UTIL_PSTHmaker(MacGregorMultiHSRspikes, dtSpikes, localPSTHbinwidth); + ICHSRsaturated(levelNo)= (sum(PSTH)/nLSRneurons)/toneDuration; + AR(levelNo)=min(ARattenuation); + MOC(levelNo)=min(MOCattenuation(length(MOCattenuation)/2:end)); + + time=dt:dt:dt*size(ICmembraneOutput,2); + figure(5), subplot(2,2,4) + % plot HSR (last of two) + plot(time,ICmembraneOutput(end-nLSRneurons+1, 1:end),'k') + ylim([-0.07 0]) + xlim([0 max(time)]) + title(['IC ' num2str(leveldB,'%4.0f') 'dB']) + drawnow + + figure(5), subplot(2,2,1) + plot(20*log10(MOC), 'k'), hold on + plot(20*log10(AR), 'r'), hold off + title(' MOC'), ylabel('dB attenuation') + ylim([-30 0]) + +% x=input('prompt') +end % level -%% #5 number of channels in the model -% 21-channel model (log spacing) -numChannels=21; -lowestBF=250; highestBF= 6000; -BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); +%% plot with levels on x-axis +figure(5), subplot(2,2,1) +plot(levels,20*log10(MOC), 'k'),hold on +plot(levels,20*log10(AR), 'r'), hold off +title(' MOC'), ylabel('dB attenuation') +ylim([-30 0]) +xlim([0 max(levels)]) -% or specify your own channel BFs -numChannels=1; -BFlist=toneFrequency; +fprintf('\n') +toneDuration=2; +rampDuration=0.004; +silenceDuration=.02; +nRepeats=200; % no. of AN fibers +fprintf('toneDuration %6.3f\n', toneDuration) +fprintf(' %6.0f AN fibers (repeats)\n', nRepeats) +fprintf('levels') +fprintf('%6.2f\t', levels) +fprintf('\n') +%% ---------------------- display summary results (Fig 15) +figure(15), clf +nRows=2; nCols=2; -%% #6 change model parameters - -paramChanges={}; - -% Parameter changes can be used to change one or more model parameters -% *after* the MAPparams file has been read -% This example declares only one fiber type with a calcium clearance time -% constant of 80e-6 s (HSR fiber) when the probability option is selected. -paramChanges={'AN_IHCsynapseParams.ANspeedUpFactor=5;', ... - 'IHCpreSynapseParams.tauCa=86e-6; '}; - - - -%% delare 'showMap' options to control graphical output -showMapOptions.printModelParameters=1; % prints all parameters -showMapOptions.showModelOutput=1; % plot of all stages -showMapOptions.printFiringRates=1; % prints stage activity levels -showMapOptions.showACF=1; % shows SACF (probability only) -showMapOptions.showEfferent=1; % tracks of AR and MOC -showMapOptions.surfProbability=1; % 2D plot of HSR response -showMapOptions.surfSpikes=1; % 2D plot of spikes histogram -showMapOptions.ICrates=0; % IC rates by CNtauGk - -% disable certain silly options -if strcmp(AN_spikesOrProbability, 'spikes') - % avoid nonsensical options - showMapOptions.surfProbability=0; - showMapOptions.showACF=0; +% AN rate - level ONSET functions +subplot(nRows,nCols,1) +plot(levels,AN_LSRonset,'ro'), hold on +plot(levels,AN_HSRonset,'ko'), hold off +ylim([0 1000]) +if length(levels)>1 + xlim([min(levels) max(levels)]) end -if strcmp(signalType, 'file') - % needed for labeling plot - showMapOptions.fileName=fileName; -else - showMapOptions.fileName=[]; +ttl=['tauCa= ' num2str(IHCpreSynapseParams.tauCa)]; +title( ttl) +xlabel('level dB SPL'), ylabel('peak rate (sp/s)'), grid on +text(0, 800, 'AN onset', 'fontsize', 14) + +% AN rate - level ADAPTED function +subplot(nRows,nCols,2) +plot(levels,AN_LSRsaturated, 'ro'), hold on +plot(levels,AN_HSRsaturated, 'ko'), hold off +ylim([0 400]) +set(gca,'ytick',0:50:300) +if length(levels)>1 +xlim([min(levels) max(levels)]) end +set(gca,'xtick',[levels(1):20:levels(end)]) +% grid on +ttl=[ 'spont=' num2str(mean(AN_HSRsaturated(1,:)),'%4.0f')... + ' sat=' num2str(mean(AN_HSRsaturated(end,1)),'%4.0f')]; +title( ttl) +xlabel('level dB SPL'), ylabel ('adapted rate (sp/s)') +text(0, 340, 'AN adapted', 'fontsize', 14), grid on -%% Generate stimuli +% CN rate - level function +subplot(nRows,nCols,3) +plot(levels,CNLSRrate, 'ro'), hold on +plot(levels,CNHSRsaturated, 'ko'), hold off +ylim([0 400]) +set(gca,'ytick',0:50:300) +if length(levels)>1 +xlim([min(levels) max(levels)]) +end +set(gca,'xtick',[levels(1):20:levels(end)]) +% grid on +ttl=[ 'spont=' num2str(mean(CNHSRsaturated(1,:)),'%4.0f') ' sat=' ... + num2str(mean(CNHSRsaturated(end,1)),'%4.0f')]; +title( ttl) +xlabel('level dB SPL'), ylabel ('adapted rate (sp/s)') +text(0, 350, 'CN', 'fontsize', 14), grid on -switch signalType - case 'tones' - % Create pure tone stimulus - dt=1/sampleRate; % seconds - time=dt: dt: duration; - inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); - amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) - inputSignal=amp*inputSignal; - % apply ramps - % catch rampTime error - if rampDuration>0.5*duration, rampDuration=duration/2; end - rampTime=dt:dt:rampDuration; - ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... - ones(1,length(time)-length(rampTime))]; - inputSignal=inputSignal.*ramp; - ramp=fliplr(ramp); - inputSignal=inputSignal.*ramp; - % add silence - intialSilence= zeros(1,round(beginSilence/dt)); - finalSilence= zeros(1,round(endSilence/dt)); - inputSignal= [intialSilence inputSignal finalSilence]; +% IC rate - level function +subplot(nRows,nCols,4) +plot(levels,ICLSRsaturated, 'ro'), hold on +plot(levels,ICHSRsaturated, 'ko'), hold off +ylim([0 400]) +set(gca,'ytick',0:50:300) +if length(levels)>1 +xlim([min(levels) max(levels)]) +end +set(gca,'xtick',[levels(1):20:levels(end)]), grid on +ttl=['spont=' num2str(mean(ICHSRsaturated(1,:)),'%4.0f') ... + ' sat=' num2str(mean(ICHSRsaturated(end,1)),'%4.0f')]; +title( ttl) +xlabel('level dB SPL'), ylabel ('adapted rate (sp/s)') +text(0, 350, 'IC', 'fontsize', 14) +set(gcf,'name',' AN CN IC rate/level') - case 'file' - %% file input simple or mixed - [inputSignal sampleRate]=wavread(fileName); - dt=1/sampleRate; - inputSignal=inputSignal(:,1); - targetRMS=20e-6*10^(leveldBSPL/20); - rms=(mean(inputSignal.^2))^0.5; - amp=targetRMS/rms; - inputSignal=inputSignal*amp; - intialSilence= zeros(1,round(0.1/dt)); - finalSilence= zeros(1,round(0.2/dt)); - inputSignal= [intialSilence inputSignal' finalSilence]; -end +fprintf('\n') +disp('levels vectorStrength') +fprintf('%3.0f \t %6.4f \n', [levels; vectorStrength']) +fprintf('\n') +fprintf('Phase locking, max vector strength=\t %6.4f\n\n',... + max(vectorStrength)) +allData=[ levels' AN_HSRonset AN_HSRsaturated... + AN_LSRonset AN_LSRsaturated ... + CNHSRsaturated CNLSRrate... + ICHSRsaturated ICLSRsaturated]; +fprintf('\n levels \tANHSR Onset \tANHSR adapted\tANLSR Onset \tANLSR adapted\tCNHSR\tCNLSR\tICHSR \tICLSR \n'); +UTIL_printTabTable(round(allData)) +fprintf('VS (phase locking)= \t%6.4f\n\n',... + max(vectorStrength)) -%% run the model -tic +UTIL_showStruct(IHC_cilia_RPParams, 'IHC_cilia_RPParams') +UTIL_showStruct(IHCpreSynapseParams, 'IHCpreSynapseParams') +UTIL_showStruct(AN_IHCsynapseParams, 'AN_IHCsynapseParams') + fprintf('\n') -disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) -disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) -disp('Computing ...') +disp('levels vectorStrength') +fprintf('%3.0f \t %6.4f \n', [levels; vectorStrength']) +fprintf('\n') +fprintf('Phase locking, max vector strength= \t%6.4f\n\n',... + max(vectorStrength)) -MAP1_14(inputSignal, sampleRate, BFlist, ... - MAPparamsName, AN_spikesOrProbability, paramChanges); +allData=[ levels' AN_HSRonset AN_HSRsaturated... + AN_LSRonset AN_LSRsaturated ... + CNHSRsaturated CNLSRrate... + ICHSRsaturated ICLSRsaturated]; +fprintf('\n levels \tANHSR Onset \tANHSR adapted\tANLSR Onset \tANLSR adapted\tCNHSR\tCNLSR\tICHSR \tICLSR \n'); +UTIL_printTabTable(round(allData)) +fprintf('VS (phase locking)= \t%6.4f\n\n',... + max(vectorStrength)) - -%% the model run is now complete. Now display the results -UTIL_showMAP(showMapOptions, paramChanges) - -if strcmp(signalType,'tones') - disp(['duration=' num2str(duration)]) - disp(['level=' num2str(leveldBSPL)]) - disp(['toneFrequency=' num2str(toneFrequency)]) - global DRNLParams - disp(['attenuation factor =' ... - num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) - disp(['attenuation factor (probability)=' ... - num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) - disp(AN_spikesOrProbability) -end -disp(paramChanges) +path(restorePath) toc -path(restorePath) -
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/tempPL.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,58 @@ +function testPhaseLocking(paramsName, paramChanges) + +if nargin<2 + paramChanges=[]; +end + +if nargin<1 + paramsName='PL'; +end + +testFrequencies=[250 500 1000 2000]; +levels=0:10:100; + +figure(14), clf +set(gcf,'position', [980 36 383 321]) +set(gcf,'name', 'phase locking') + +allStrengths=zeros(length(testFrequencies), length(levels)); +peakVectorStrength=zeros(1,length(testFrequencies)); + +freqCount=0; +for targetFrequency=testFrequencies; + %single test + freqCount=freqCount+1; + vectorStrength=... + temp(targetFrequency,targetFrequency, levels,... + paramsName, paramChanges); + allStrengths(freqCount,:)=vectorStrength'; + peakVectorStrength(freqCount)=max(vectorStrength'); +end +%% plot results +figure(14) +subplot(2,1,2) +plot(levels,allStrengths, '+') +xlabel('levels') +ylabel('vector strength') +legend (num2str(testFrequencies'),'location','eastOutside') + +subplot(2,1,1) +semilogx(testFrequencies,peakVectorStrength) +grid on +title ('peak vector strength') +xlabel('frequency') +ylabel('vector strength') + +johnson=[250 0.79 +500 0.82 +1000 0.8 +2000 0.7 +4000 0.25 +5500 0.05 +]; +hold on +plot(johnson(:,1),johnson(:,2),'o') +legend({'model','Johnson 80'},'location','eastOutside') +hold off + +
--- a/testPrograms/termp.m Thu Oct 06 15:43:20 2011 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,13 +0,0 @@ - -DRNLParams.a=10; -paramChanges={'DRNL.a=2;'} -for idx=1:length(paramChanges) -x=paramChanges{idx}; -st=x(1:strfind(x,'.')-1); -fld=x(strfind(x,'.')+1:strfind(x,'=')-1); -x1=eval(['isstruct(' st ')']); -x2=eval(['isfield(' st ',''' fld ''')']); -if ~x1*x2 - disp([' paramChange is faulty: ' x]) -end -end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/test2toneMovie.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,151 @@ +% function test2toneMovie +% +% Demonstration of two-tone suppression in the AN using a +% F1 tone suppressed by a 1.5*F1 kHz tone +% + +global ANprobRateOutput DRNLoutput +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) +figure(5), clf + +disp('') +primaryToneFrequency=8000; + +probedBs=10 :10: 50; +nProbeLevels=length(probedBs); + +% disp(['F1 F1 level= ' num2str([primaryToneFrequency probedB])]) + +suppressorLevels=0:10:80; + +lowestBF=250; highestBF= 8000; numChannels=21; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); +% fix so that probe is middle range and is at a BF +BFlist=BFlist*primaryToneFrequency/2000; +suppressorFrequencies=BFlist; +BFchannel=find(round(BFlist)==round(primaryToneFrequency)); + +sampleRate= 100000; % Hz (higher sample rate needed for BF>8000 Hz) +dt=1/sampleRate; % seconds + +duration=.050; % seconds + tone2Duration=duration/2; % s +startSilenceDuration=0.010; +startSilence=... + zeros(1,startSilenceDuration*sampleRate); +suppressorStartsPTR=... + round((startSilenceDuration+duration/2)*sampleRate); + +probedBCount=0; +for probedB=probedBs + probedBCount=probedBCount+1; + +peakResponse= zeros(length(suppressorLevels),length(suppressorFrequencies)); +suppFreqCount=0; +for suppressorFrequency=suppressorFrequencies + suppFreqCount=suppFreqCount+1; + + suppressorLevelCount=0; +for suppressorDB=suppressorLevels +suppressorLevelCount=suppressorLevelCount+1; + + % primary + suppressor + primaryLevelDB=probedB; + suppressorLevelDB=suppressorDB; + + +% primary BF tone + time1=dt: dt: duration; + amp=10^(primaryLevelDB/20)*28e-6; + inputSignal=amp*sin(2*pi*primaryToneFrequency*time1); + rampDuration=.005; rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ones(1,length(time1)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + inputSignal=inputSignal.*fliplr(ramp); + + % suppressor + time2= dt: dt: tone2Duration; + % B: tone on + amp=10^(suppressorLevelDB/20)*28e-6; + inputSignal2=amp*sin(2*pi*suppressorFrequency*time2); + rampDuration=.005; rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ones(1,length(time2)-length(rampTime))]; + inputSignal2=inputSignal2.*ramp; + inputSignal2=inputSignal2.*fliplr(ramp); + % A: initial silence (delay to suppressor) + silence=zeros(1,length(time2)); + inputSignal2=[silence inputSignal2]; + + % add tone and suppressor components + inputSignal=inputSignal+inputSignal2; + + inputSignal=... + [startSilence inputSignal ]; + + % run MAP + MAPparamsName='Normal'; + AN_spikesOrProbability='probability'; + paramChanges={'IHCpreSynapseParams.tauCa=80e-6;'}; + MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + response=ANprobRateOutput; +peakResponse(suppressorLevelCount,suppFreqCount)=... + mean(response(BFchannel,suppressorStartsPTR:end)); +disp(['F2 level= ', num2str([suppressorFrequency suppressorDB ... + peakResponse(suppressorLevelCount,suppFreqCount)])]) + + +%% make movie + figure(6), subplot(4,1,2) + axis tight + set(gca,'nextplot','replacechildren'); + plot(dt:dt:dt*length(inputSignal), inputSignal, 'k') + ylim([-.5 .5]) + + figure(6), subplot(2,1,2) + PSTHbinWidth=0.010; + PSTH= UTIL_PSTHmakerb(... + response, dt, PSTHbinWidth); + [nY nX]=size(PSTH); + time=PSTHbinWidth*(0:nX-1); + surf(time, BFlist, PSTH) + zlim([0 500]), + caxis([0 500]) + shading interp + colormap(jet) + set(gca, 'yScale','log') + xlim([0 max(time)+dt]) + ylim([0 max(BFlist)]) + view([0 90]) % view([-8 68]) + title([' primary + suppressor ']) + +end +end + +%% plot matrix + +peakResponse=peakResponse/peakResponse(1,1); + +figure(5), subplot(2,nProbeLevels, probedBCount) +surf(suppressorFrequencies,suppressorLevels,peakResponse) +shading interp +view([0 90]) +set(gca,'Xscale','log') +xlim([min(suppressorFrequencies) max(suppressorFrequencies)]) +set(gca,'Xtick', [1000 4000],'xticklabel',{'1000', '4000'}) +title([num2str(primaryToneFrequency) ' Hz']) + +subplot(2,nProbeLevels,nProbeLevels+probedBCount) +contourf(suppressorFrequencies,suppressorLevels,peakResponse, ... + [.1:.1:.9 0.99] ) +set(gca,'Xscale','log') +set(gca,'Xtick', [1000 4000],'xticklabel',{'1000', '4000'}) +set(gcf, 'name',['primaryToneFrequency= ' num2str(primaryToneFrequency)]) +title([num2str(probedB) ' dB']) +end + +path=restorePath;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/test2toneSuppression.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,216 @@ +% function test2toneSuppression +% +% Demonstration of two-tone suppression +% +% A probe tone is played at a fixed level and a second tone is introduced +% half way through the presentation. The response to the combined signal +% is recorded and analysed. +% +% The second tone called the seeep tone is presented at a reange of +% frequencies and levels. In a linear system we might expect the addition +% of a second tone to increase the magnitude of the output. +% In fact, it often results in a reduction in the response. +% This is called two-tone suppression. +% +% The effect of the sweep tone is represented in a countour plot showing +% both reductions and increases in response. +% The background colour in this plot is the response to the +% fixed tone alone + + +% Ruggero et al 1992 (Fig 2) +fixedToneFrequency=8600; +fixedToneLevelsdB=[45 :5: 90]; +% fixedToneLevelsdB=[30]; +fixedToneDuration=.050; % seconds +sweeptoneLevelsdB=[ 0 : 10:90]; +nSweepToneFrequencies=21; +sampleSweepFrequency=10600; + +global ANprobRateOutput DRNLoutput +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) +figure(5), clf +figure(87), clf + +nFixedToneLevels=length(fixedToneLevelsdB); + +lowestSweepFrequency=fixedToneFrequency/6; +highestSweepFrequency=fixedToneFrequency*3; +sweepToneFrequencies=round(logspace(log10(lowestSweepFrequency), ... + log10(highestSweepFrequency), nSweepToneFrequencies)); + +% key channels for snapshots +[a BFchannel]=min((sweepToneFrequencies-fixedToneFrequency).^2); +[a sampleChannel]=min((sweepToneFrequencies-sampleSweepFrequency).^2); + +sampleRate= max(44100, 4*highestSweepFrequency); +dt=1/sampleRate; % seconds + +startSilenceDuration=0.010; +startSilence= zeros(1,startSilenceDuration*sampleRate); + +sweepStartPTR=... + round((startSilenceDuration + fixedToneDuration/2)*sampleRate); + +BF_BMresponse=zeros(length(sweepToneFrequencies), ... + length(fixedToneLevelsdB), length(sweeptoneLevelsdB)); + +fixedTonedBCount=0; +for fixedTonedB=fixedToneLevelsdB + fixedTonedBCount=fixedTonedBCount+1; + + BMpeakResponse= zeros(length(sweeptoneLevelsdB),length(sweepToneFrequencies)); + ANpeakResponse= zeros(length(sweeptoneLevelsdB),length(sweepToneFrequencies)); + sweepToneLevelCount=0; + for sweepToneDB=sweeptoneLevelsdB + sweepToneLevelCount=sweepToneLevelCount+1; + suppFreqCount=0; + for sweepToneFrequency=sweepToneFrequencies + suppFreqCount=suppFreqCount+1; + + % fixedTone tone + time1=dt: dt: fixedToneDuration; + amp=10^(fixedTonedB/20)*28e-6; + inputSignal=amp*sin(2*pi*fixedToneFrequency*time1); + rampDuration=.005; rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ones(1,length(time1)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + inputSignal=inputSignal.*fliplr(ramp); + nsignalPoints=length(inputSignal); + sweepStart=round(nsignalPoints/2); + nSweepPoints=nsignalPoints-sweepStart; + + % sweepTone + time2= dt: dt: dt*nSweepPoints; + % B: tone on + amp=10^(sweepToneDB/20)*28e-6; + inputSignal2=amp*sin(2*pi*sweepToneFrequency*time2); + rampDuration=.005; rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ones(1,length(time2)-length(rampTime))]; + inputSignal2=inputSignal2.*ramp; + inputSignal2=inputSignal2.*fliplr(ramp); + % add tone and sweepTone components + inputSignal(sweepStart+1:end)= inputSignal(sweepStart+1:end)+inputSignal2; + + inputSignal=... + [startSilence inputSignal ]; + + %% run MAP + MAPparamsName='Normal'; + AN_spikesOrProbability='probability'; + % only use HSR fibers (NB no acoustic reflex) + paramChanges={'IHCpreSynapseParams.tauCa=80e-6;'}; + paramChanges={'IHCpreSynapseParams.tauCa=80e-6;',... + 'DRNLParams.g=0;'}; + MAP1_14(inputSignal, sampleRate, fixedToneFrequency, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + % find toneAlone response + if sweepToneLevelCount==1 && suppFreqCount==1 + BMfixedToneAloneRate=... + mean(abs(DRNLoutput(sweepStartPTR:end))); + ANfixedToneAloneRate=... + mean(abs(ANprobRateOutput(sweepStartPTR:end))); + end + + BF_BMresponse(suppFreqCount,fixedTonedBCount, ... + sweepToneLevelCount)=... + mean(abs(DRNLoutput(sweepStartPTR:end))); + + BMpeakResponse(sweepToneLevelCount,suppFreqCount)=... + mean(abs(DRNLoutput(sweepStartPTR:end)))... + /BMfixedToneAloneRate; + ANpeakResponse(sweepToneLevelCount,suppFreqCount)=... + mean(abs(ANprobRateOutput(sweepStartPTR:end)))... + /ANfixedToneAloneRate; + disp(['F2: ', num2str([sweepToneFrequency sweepToneDB ... + BMpeakResponse(sweepToneLevelCount,suppFreqCount)... + ANpeakResponse(sweepToneLevelCount,suppFreqCount)])... + ' dB']) + + figure(5) + time=dt:dt:dt*length(inputSignal); + subplot(3,1,1), plot(time, inputSignal) + title(['stimulus: Suppressor=' ... + num2str([sweepToneFrequency, sweepToneDB]) ' Hz/ dB']) + + time=dt:dt:dt*length(DRNLoutput); + subplot(3,1,2), plot(time, DRNLoutput) + xlim([0 fixedToneDuration]) + ylim([0 inf]) + + time=dt:dt:dt*length(ANprobRateOutput); + subplot(3,1,2), plot(time, ANprobRateOutput) + xlim([0 fixedToneDuration]) + ylim([0 500]) + title(['ANresponse: fixedTone' num2str([fixedToneFrequency, fixedTonedB]) ' Hz/ dB']) + + subplot(3,2,5) + contourf(sweepToneFrequencies,sweeptoneLevelsdB,BMpeakResponse, ... + [.1:.1:.9 1.05] ) + set(gca,'Xscale','log') + set(gca,'Xtick', [1000 4000],'xticklabel',{'1000', '4000'}) + set(gcf, 'name',['fixedToneFrequency= ' num2str(fixedToneFrequency)]) + title(['BM' num2str(fixedTonedB) ' dB']) + + subplot(3,2,6) + contourf(sweepToneFrequencies,sweeptoneLevelsdB,ANpeakResponse, ... + [.1:.1:.9 1.05] ) + set(gca,'Xscale','log') + set(gca,'Xtick', [1000 4000],'xticklabel',{'1000', '4000'}) + set(gcf, 'name',['fixedToneFrequency= ' num2str(fixedToneFrequency)]) + title(['AN:' num2str(fixedTonedB) ' dB']) + drawnow + end + end + + %% plot matrix + + figure (87) + subplot(3, nFixedToneLevels, fixedTonedBCount) + surf(sweepToneFrequencies,sweeptoneLevelsdB,BMpeakResponse) + set(gca,'Xscale','log') + zlabel('gain') + xlim([lowestSweepFrequency highestSweepFrequency]) + ylim([min(sweeptoneLevelsdB) max(sweeptoneLevelsdB)]) + title('BM response') + view([-11 52]) + + subplot(3, nFixedToneLevels, nFixedToneLevels+fixedTonedBCount) + contourf(sweepToneFrequencies,sweeptoneLevelsdB,BMpeakResponse, ... + [.1:.5:.9 0.99 1.05] ) + hold on + plot(fixedToneFrequency, fixedTonedB, 'or', 'markerfacecolor','w') + set(gca,'Xscale','log') + set(gca,'Xtick', [1000 5000],'xticklabel',{'1000', '5000'}) + ylabel('(BM) sweep level') + xlabel('(BM) sweep freq') + title(['fixed tone level=' num2str(fixedTonedB) ' dB']) +% colorbar + + subplot(3, nFixedToneLevels, 2*nFixedToneLevels+fixedTonedBCount) + contourf(sweepToneFrequencies,sweeptoneLevelsdB,ANpeakResponse, ... + [.1:.5:.9 0.99 1.05] ) + hold on + plot(fixedToneFrequency, fixedTonedB, 'or', 'markerfacecolor','w') + set(gca,'Xscale','log') + set(gca,'Xtick', [1000 5000],'xticklabel',{'1000', '5000'}) + ylabel('(AN) sweep level') + xlabel('(AN) sweep freq') + title(['fixed tone level=' num2str(fixedTonedB) ' dB']) +% colorbar + +end + +% Ruggero fig 2 (probe tone level is x-axis, sweep tone level is y-axis +figure(1),semilogy(fixedToneLevelsdB,squeeze(BF_BMresponse(sampleChannel,:,:))) +ylim([-inf inf]) +legend(num2str(sweeptoneLevelsdB'),'location','southeast') +xlabel('probe SPL') +ylabel ('displacement (m)') +title(['Probe ' num2str(fixedToneFrequency) ' Hz. Sweep ' ... + num2str(sweepToneFrequencies(sampleChannel)) ' Hz.']) +path=restorePath;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/test2toneSuppressionHold.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,151 @@ +% function test2toneSuppression +% +% Demonstration of two-tone suppression in the AN using a +% F1 tone suppressed by a 1.5*F1 kHz tone +% + +global ANprobRateOutput DRNLoutput +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) +figure(5), clf + +disp('') +primaryToneFrequency=8000; + +probedBs=10 :10: 50; +nProbeLevels=length(probedBs); + +% disp(['F1 F1 level= ' num2str([primaryToneFrequency probedB])]) + +suppressorLevels=0:10:80; + +lowestBF=250; highestBF= 8000; numChannels=21; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); +% fix so that probe is middle range and is at a BF +BFlist=BFlist*primaryToneFrequency/2000; +suppressorFrequencies=BFlist; +BFchannel=find(round(BFlist)==round(primaryToneFrequency)); + +sampleRate= 100000; % Hz (higher sample rate needed for BF>8000 Hz) +dt=1/sampleRate; % seconds + +duration=.050; % seconds + tone2Duration=duration/2; % s +startSilenceDuration=0.010; +startSilence=... + zeros(1,startSilenceDuration*sampleRate); +suppressorStartsPTR=... + round((startSilenceDuration+duration/2)*sampleRate); + +probedBCount=0; +for probedB=probedBs + probedBCount=probedBCount+1; + +peakResponse= zeros(length(suppressorLevels),length(suppressorFrequencies)); +suppFreqCount=0; +for suppressorFrequency=suppressorFrequencies + suppFreqCount=suppFreqCount+1; + + suppressorLevelCount=0; +for suppressorDB=suppressorLevels +suppressorLevelCount=suppressorLevelCount+1; + + % primary + suppressor + primaryLevelDB=probedB; + suppressorLevelDB=suppressorDB; + + +% primary BF tone + time1=dt: dt: duration; + amp=10^(primaryLevelDB/20)*28e-6; + inputSignal=amp*sin(2*pi*primaryToneFrequency*time1); + rampDuration=.005; rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ones(1,length(time1)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + inputSignal=inputSignal.*fliplr(ramp); + + % suppressor + time2= dt: dt: tone2Duration; + % B: tone on + amp=10^(suppressorLevelDB/20)*28e-6; + inputSignal2=amp*sin(2*pi*suppressorFrequency*time2); + rampDuration=.005; rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ones(1,length(time2)-length(rampTime))]; + inputSignal2=inputSignal2.*ramp; + inputSignal2=inputSignal2.*fliplr(ramp); + % A: initial silence (delay to suppressor) + silence=zeros(1,length(time2)); + inputSignal2=[silence inputSignal2]; + + % add tone and suppressor components + inputSignal=inputSignal+inputSignal2; + + inputSignal=... + [startSilence inputSignal ]; + + % run MAP + MAPparamsName='Normal'; + AN_spikesOrProbability='probability'; + paramChanges={'IHCpreSynapseParams.tauCa=80e-6;'}; + MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + response=ANprobRateOutput; +peakResponse(suppressorLevelCount,suppFreqCount)=... + mean(response(BFchannel,suppressorStartsPTR:end)); +disp(['F2 level= ', num2str([suppressorFrequency suppressorDB ... + peakResponse(suppressorLevelCount,suppFreqCount)])]) + + +%% make movie + figure(6), subplot(4,1,2) + axis tight + set(gca,'nextplot','replacechildren'); + plot(dt:dt:dt*length(inputSignal), inputSignal, 'k') + ylim([-.5 .5]) + + figure(6), subplot(2,1,2) + PSTHbinWidth=0.010; + PSTH= UTIL_PSTHmakerb(... + response, dt, PSTHbinWidth); + [nY nX]=size(PSTH); + time=PSTHbinWidth*(0:nX-1); + surf(time, BFlist, PSTH) + zlim([0 500]), + caxis([0 500]) + shading interp + colormap(jet) + set(gca, 'yScale','log') + xlim([0 max(time)+dt]) + ylim([0 max(BFlist)]) + view([0 90]) % view([-8 68]) + title([' primary + suppressor ']) + +end +end + +%% plot matrix + +peakResponse=peakResponse/peakResponse(1,1); + +figure(5), subplot(2,nProbeLevels, probedBCount) +surf(suppressorFrequencies,suppressorLevels,peakResponse) +shading interp +view([0 90]) +set(gca,'Xscale','log') +xlim([min(suppressorFrequencies) max(suppressorFrequencies)]) +set(gca,'Xtick', [1000 4000],'xticklabel',{'1000', '4000'}) +title([num2str(primaryToneFrequency) ' Hz']) + +subplot(2,nProbeLevels,nProbeLevels+probedBCount) +contourf(suppressorFrequencies,suppressorLevels,peakResponse, ... + [.1:.1:.9 0.99] ) +set(gca,'Xscale','log') +set(gca,'Xtick', [1000 4000],'xticklabel',{'1000', '4000'}) +set(gcf, 'name',['primaryToneFrequency= ' num2str(primaryToneFrequency)]) +title([num2str(probedB) ' dB']) +end + +path=restorePath;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/testACF.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,263 @@ +% function [P dt lags SACF]= testACF +% testACF is a *script* to demonstrate the smoothed ACF of +% Balaguer-Ballestera, E. Denham, S.L. and Meddis, R. (2008). +% +% Convert this to a *function* by uncommenting the first line +% The function returns the P matrix plotted in Figure 96. +% If a function is used, the following outputs are returned: +% P: smoothed SACF (lags x time matrix) +% dt: time interval between successive columns of P +% lags: lags used in computing P +% SACF: unsmoothed SACFs +% +% A range of options are supplied in the early part of the program +% +% #1 +% Identify the model parameter file (in 'MAPparamsName') +% +% #2 +% Identify the kind of model required (in 'AN_spikesOrProbability') +% 'probability' is recommended for ACF work +% +% #3 +% Choose between a harmonic complex or file input +% by commenting out unwanted code +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the model channel BFs in the vector 'BFlist'. +% +% #6 +% Last minute changes to the model parameters can be made using +% the cell array of strings 'paramChanges'. +% This is used here to control the details of the ACF computations +% Read the notes in this section for more information +% +% displays: +% Figure 97 shows the AN response to the stimulus. this is a channel x time +% display. The z-axis (and colour) is the AN fiber firing rate +% +% Figure 96 shows the P-matrix, the smoothed SACF. +% +% Figure 89 shows a summary of the evolution of the unsmoothed SACF +% over time. If you wish to take a snapshot of the P-matrix at a +% particular time, this figure can help identify when to take it. +% The index on the y-axis, identifies the required row numbers +% of the P or SACF matrix, e.g. P(:,2000) +% +% On request, (filteredSACFParams.plotACFs=1) Figure 89 shows the channel +% by channel ACFs at intervals during the computation as a movie. +% The number of ACF displays is controlled by 'plotACFsInterval' +% and the movie can be slowed or speeded up using 'plotMoviePauses' +% (see paramChanges section below). + +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% This global will find results from MAP1_14 +global savedInputSignal ANprobRateOutput ANoutput dt dtSpikes savedBFlist + % This global,from model parameter file +global filteredSACFParams + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +% User sets up requirements +%% #1 parameter file name +MAPparamsName='Normal'; % recommended + + +%% #2 probability (fast) or spikes (slow) representation: select one +% AN_spikesOrProbability='spikes'; +% or +AN_spikesOrProbability='probability'; % recommended + +%% #3 A. harmonic sequence or B. speech file input +% Comment out unwanted code +% A. harmonic tone (Hz) - useful to demonstrate a broadband sound +sampleRate= 44100; % recommended 44100 +signalType= 'tones'; +duration=0.100; % seconds +beginSilence=0.020; +endSilence=0.020; +rampDuration=.005; % raised cosine ramp (seconds) + +% toneFrequency is a vector of component frequencies +F0=120; +toneFrequency= [3*F0 4*F0 5*F0]; + +% or +% B. file input +signalType= 'file'; +fileName='Oh No'; +fileName='1z67931a_44kHz'; + + +%% #4 rms level +leveldBSPL= 60; % dB SPL (80 for Lieberman) + +%% #5 number of channels in the model +% 21-channel model (log spacing of BFs) +numChannels=21; +lowestBF=150; highestBF= 6000; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +%% #6 change model parameters +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read (see manual) + +% Take control of ACF parameters +% The filteredACF parameters are set in the MAPparamsNormal file +% However, it is convenient to change them here leving the file intacta + minPitch= 80; maxPitch= 500; numPitches=50; + minPitch= 200; maxPitch= 4000; numPitches=40; + maxLag=1/minPitch; minLag=1/maxPitch; + lags= linspace(minLag, maxLag, numPitches); + +paramChanges={... + 'filteredSACFParams.lags=lags; % autocorrelation lags vector;',... + 'filteredSACFParams.acfTau= 2; % (Wiegrebe) time constant ACF;',... + 'filteredSACFParams.lambda= 0.12; % slower filter to smooth ACF;',... + 'filteredSACFParams.plotACFs=1; % plot ACFs while computing;',... + 'filteredSACFParams.plotACFsInterval=0.01;',... + 'filteredSACFParams.plotMoviePauses=.1; ',... + 'filteredSACFParams.usePressnitzer=0; % attenuates ACF at long lags;',... + 'filteredSACFParams.lagsProcedure= ''useAllLags'';',... + }; + +% Notes: +% acfTau: time constant of unsmoothed ACF +% lambda: time constant of smoothed ACFS +% plotACFs: plot ACFs during computation (0 to switch off, for speed) +% plotACFsInterval: sampling interval for plots +% plotMoviePauses: pause duration between frames to allow viewing +% usePressnitzer: gives low weights to long lags +% lagsProcedure: used to fiddle with output (ignore) + +%% delare 'showMap' options to control graphical output +% see UTIL_showMAP for more options +showMapOptions=[]; +% showMapOptions.showModelOutput=0; % plot of all stages +showMapOptions.surfAN=1; % surface plot of HSR response +showMapOptions.PSTHbinwidth=0.001; % smoothing for PSTH + +if exist('fileName','var') + % needed for labeling plot + showMapOptions.fileName=fileName; +end + +%% Generate stimuli +switch signalType + case 'tones' + % Create tone stimulus + dt=1/sampleRate; % seconds + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + % catch rampTime error + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; +end + +wavplay(inputSignal, sampleRate) + +%% run the model + +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) +disp('Computing ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +%% The model run is now complete. Now display the results +% display the AN response +UTIL_showMAP(showMapOptions) + +% compute ACF +switch AN_spikesOrProbability + case 'probability' + % use only HSR fibers + inputToACF=ANprobRateOutput(end-length(savedBFlist)+1:end,:); + otherwise + inputToACF=ANoutput; + dt=dtSpikes; +end + +disp ('computing ACF...') + +% read paramChanges to get new filteredSACFParams +for i=1:length(paramChanges) +eval(paramChanges{i}); +end + +[P BFlist SACF]= filteredSACF(inputToACF, dt, savedBFlist, filteredSACFParams); +disp(' ACF done.') + +%% plot original waveform on summary/smoothed ACF plot +figure(96), clf +subplot(3,1,3) +t=dt*(1:length(savedInputSignal)); +plot(t,savedInputSignal, 'k') +xlim([0 t(end)]) +title(['stimulus: ' num2str(leveldBSPL, '%4.0f') ' dB SPL']); + +% plot SACF +figure(96) +subplot(2,1,1) +imagesc(P.^2) +colormap bone +ylabel('periodicities (Hz)'), xlabel('time (s)') +title('smoothed SACF. (periodicity x time)') +% y-axis specifies pitches (1/lags) +% Force MATLAB to show the lowest pitch +postedYvalues=[1 get(gca,'ytick')]; set(gca,'ytick',postedYvalues) +pitches=1./filteredSACFParams.lags; +set(gca,'ytickLabel', round(pitches(postedYvalues))) +% x-axis is time at which P is samples +[nCH nTimes]=size(P); +t=dt:dt:dt*nTimes; +tt=get(gca,'xtick'); +set(gca,'xtickLabel', round(100*t(tt))/100) + +%% On a new figure show a cascade of SACFs +figure(89), clf +% select 100 samples; +[r c]=size(SACF); +step=round(c/100); +idx=step:step:c; + +UTIL_cascadePlot(SACF(:,idx)', 1./pitches) + +xlabel('lag (s)'), ylabel('time pointer -->') +title(' SACF summary over time') +yValues=get(gca,'yTick'); +set(gca,'yTickLabel', num2str(yValues'*100)) + +path(restorePath) +
--- a/testPrograms/testAN.m Thu Oct 06 15:43:20 2011 +0100 +++ b/testPrograms/testAN.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,13 +1,21 @@ function vectorStrength=testAN(probeFrequency,BFlist, levels, ... paramsName,paramChanges) -% generates rate/level functions for AN and brainstem units. +% testAN generates rate/level functions for AN and brainstem units. % also other information like PSTHs, MOC efferent activity levels. +% Vector strength calculations require the computation of period +% histograms. for this reason the sample rate must always be an integer +% multiple of the tone frequency. This applies to both the sampleRate and +% the spikes sampling rate. +% A full 'spikes' model is used. +% paramChanges is a cell array of strings containing MATLAB statements that +% change model parameters. See MAPparamsNormal for examples. % e.g. -% testAN(1000,1000, -10:10:80,'Normal',[]); +% testAN(1000,1000, -10:10:80,'Normal',{}); + global IHC_VResp_VivoParams IHC_cilia_RPParams IHCpreSynapseParams global AN_IHCsynapseParams -global ANoutput ANdt CNoutput ICoutput ICmembraneOutput ANtauCas +global ANoutput dtSpikes CNoutput ICoutput ICmembraneOutput ANtauCas global ARattenuation MOCattenuation tic dbstop if error @@ -23,23 +31,18 @@ nLevels=length(levels); toneDuration=.2; rampDuration=0.002; silenceDuration=.02; -localPSTHbinwidth=0.001; +localPSTHbinwidth=0.0005; -%% guarantee that the sample rate is at least 10 times the frequency -sampleRate=50000; -while sampleRate< 10* probeFrequency - sampleRate=sampleRate+10000; -end - -%% adjust sample rate so that the pure tone stimulus has an integer -%% numver of epochs in a period +%% guarantee that the sample rate is an interger multiple +% of the probe frequency +% we want 5 bins per period for spikes +spikesSampleRate=5*probeFrequency; +% model sample rate must be an integer multiple of this and in the region +% of 50000 +sampleRate=spikesSampleRate*round(50000/spikesSampleRate); dt=1/sampleRate; -period=1/probeFrequency; -ANspeedUpFactor=5; %anticipating MAP (needs clearing up) -ANdt=dt*ANspeedUpFactor; -ANdt=period/round(period/ANdt); -dt=ANdt/ANspeedUpFactor; -sampleRate=1/dt; +% avoid very slow spikes sampling rate +spikesSampleRate=spikesSampleRate*ceil(10000/spikesSampleRate); %% pre-allocate storage AN_HSRonset=zeros(nLevels,1); @@ -83,7 +86,12 @@ inputSignal=[silence inputSignal]; %% run the model - AN_spikesOrProbability='spikes'; + AN_spikesOrProbability='spikes'; + nExistingParamChanges=length(paramChanges); + paramChanges{nExistingParamChanges+1}=... + ['AN_IHCsynapseParams.spikesTargetSampleRate=' ... + num2str(spikesSampleRate) ';']; + MAP1_14(inputSignal, 1/dt, BFlist, ... paramsName, AN_spikesOrProbability, paramChanges); @@ -96,16 +104,16 @@ numHSRfibers=numLSRfibers; LSRspikes=ANoutput(1:numLSRfibers,:); - PSTH=UTIL_PSTHmaker(LSRspikes, ANdt, localPSTHbinwidth); + PSTH=UTIL_PSTHmaker(LSRspikes, dtSpikes, localPSTHbinwidth); PSTHLSR=mean(PSTH,1)/localPSTHbinwidth; % across fibers rates PSTHtime=localPSTHbinwidth:localPSTHbinwidth:... localPSTHbinwidth*length(PSTH); AN_LSRonset(levelNo)= max(PSTHLSR); % peak in 5 ms window AN_LSRsaturated(levelNo)= mean(PSTHLSR(round(length(PSTH)/2):end)); - % HSR + % AN HSR HSRspikes= ANoutput(end- numHSRfibers+1:end, :); - PSTH=UTIL_PSTHmaker(HSRspikes, ANdt, localPSTHbinwidth); + PSTH=UTIL_PSTHmaker(HSRspikes, dtSpikes, localPSTHbinwidth); PSTH=mean(PSTH,1)/localPSTHbinwidth; % sum across fibers (HSR only) AN_HSRonset(levelNo)= max(PSTH); AN_HSRsaturated(levelNo)= mean(PSTH(round(length(PSTH)/2): end)); @@ -120,12 +128,12 @@ % AN - CV % CV is computed 5 times. Use the middle one (3) as most typical - cvANHSR= UTIL_CV(HSRspikes, ANdt); + cvANHSR= UTIL_CV(HSRspikes, dtSpikes); % AN - vector strength PSTH=sum(HSRspikes); [PH, binTimes]=UTIL_periodHistogram... - (PSTH, ANdt, probeFrequency); + (PSTH, dtSpikes, probeFrequency); VS=UTIL_vectorStrength(PH); vectorStrength(levelNo)=VS; disp(['sat rate= ' num2str(AN_HSRsaturated(levelNo)) ... @@ -139,37 +147,40 @@ [nCNneurons c]=size(CNoutput); nLSRneurons=round(nCNneurons/nTaus); CNLSRspikes=CNoutput(1:nLSRneurons,:); - PSTH=UTIL_PSTHmaker(CNLSRspikes, ANdt, localPSTHbinwidth); + PSTH=UTIL_PSTHmaker(CNLSRspikes, dtSpikes, localPSTHbinwidth); PSTH=sum(PSTH)/nLSRneurons; - CNLSRrate(levelNo)=mean(PSTH(round(length(PSTH)/2):end))/localPSTHbinwidth; +% CNLSRrate(levelNo)=mean(PSTH(round(length(PSTH)/2):end))/localPSTHbinwidth; + CNLSRrate(levelNo)=mean(PSTH)/localPSTHbinwidth; %CN HSR MacGregorMultiHSRspikes=... CNoutput(end-nLSRneurons+1:end,:); - PSTH=UTIL_PSTHmaker(MacGregorMultiHSRspikes, ANdt, localPSTHbinwidth); + PSTH=UTIL_PSTHmaker(MacGregorMultiHSRspikes, dtSpikes, localPSTHbinwidth); PSTH=sum(PSTH)/nLSRneurons; PSTH=mean(PSTH,1)/localPSTHbinwidth; % sum across fibers (HSR only) - CNHSRsaturated(levelNo)=mean(PSTH(length(PSTH)/2:end)); +% CNHSRsaturated(levelNo)=mean(PSTH(length(PSTH)/2:end)); + CNHSRsaturated(levelNo)=mean(PSTH); figure(5), subplot(2,2,3) bar(PSTHtime,PSTH) ylim([0 1000]) xlim([0 length(PSTH)*localPSTHbinwidth]) - cvMMHSR= UTIL_CV(MacGregorMultiHSRspikes, ANdt); + cvMMHSR= UTIL_CV(MacGregorMultiHSRspikes, dtSpikes); title(['CN CV= ' num2str(cvMMHSR(3),'%5.2f')]) %% IC LSR [nICneurons c]=size(ICoutput); nLSRneurons=round(nICneurons/nTaus); ICLSRspikes=ICoutput(1:nLSRneurons,:); - PSTH=UTIL_PSTHmaker(ICLSRspikes, ANdt, localPSTHbinwidth); - ICLSRsaturated(levelNo)=mean(PSTH(round(length(PSTH)/2):end))/localPSTHbinwidth; + PSTH=UTIL_PSTHmaker(ICLSRspikes, dtSpikes, localPSTHbinwidth); +% ICLSRsaturated(levelNo)=mean(PSTH(round(length(PSTH)/2):end))/localPSTHbinwidth; + ICLSRsaturated(levelNo)=mean(PSTH)/localPSTHbinwidth; %IC HSR MacGregorMultiHSRspikes=... ICoutput(end-nLSRneurons+1:end,:); - PSTH=UTIL_PSTHmaker(MacGregorMultiHSRspikes, ANdt, localPSTHbinwidth); + PSTH=UTIL_PSTHmaker(MacGregorMultiHSRspikes, dtSpikes, localPSTHbinwidth); ICHSRsaturated(levelNo)= (sum(PSTH)/nLSRneurons)/toneDuration; AR(levelNo)=min(ARattenuation); @@ -190,7 +201,7 @@ title(' MOC'), ylabel('dB attenuation') ylim([-30 0]) - +% x=input('prompt') end % level %% plot with levels on x-axis @@ -219,8 +230,12 @@ % AN rate - level ONSET functions subplot(nRows,nCols,1) plot(levels,AN_LSRonset,'ro'), hold on -plot(levels,AN_HSRonset,'ko'), hold off -ylim([0 1000]), xlim([min(levels) max(levels)]) +plot(levels,AN_HSRonset,'ko', 'MarkerEdgeColor','k', 'markerFaceColor','k'), hold off +ylim([0 1000]) +if length(levels)>1 + xlim([min(levels) max(levels)]) +end + ttl=['tauCa= ' num2str(IHCpreSynapseParams.tauCa)]; title( ttl) xlabel('level dB SPL'), ylabel('peak rate (sp/s)'), grid on @@ -229,10 +244,12 @@ % AN rate - level ADAPTED function subplot(nRows,nCols,2) plot(levels,AN_LSRsaturated, 'ro'), hold on -plot(levels,AN_HSRsaturated, 'ko'), hold off +plot(levels,AN_HSRsaturated, 'ko', 'MarkerEdgeColor','k', 'markerFaceColor','k'), hold off ylim([0 400]) set(gca,'ytick',0:50:300) +if length(levels)>1 xlim([min(levels) max(levels)]) +end set(gca,'xtick',[levels(1):20:levels(end)]) % grid on ttl=[ 'spont=' num2str(mean(AN_HSRsaturated(1,:)),'%4.0f')... @@ -241,13 +258,15 @@ xlabel('level dB SPL'), ylabel ('adapted rate (sp/s)') text(0, 340, 'AN adapted', 'fontsize', 14), grid on -% CN rate - level ADAPTED function +% CN rate - level function subplot(nRows,nCols,3) plot(levels,CNLSRrate, 'ro'), hold on -plot(levels,CNHSRsaturated, 'ko'), hold off +plot(levels,CNHSRsaturated, 'ko', 'MarkerEdgeColor','k', 'markerFaceColor','k'), hold off ylim([0 400]) set(gca,'ytick',0:50:300) +if length(levels)>1 xlim([min(levels) max(levels)]) +end set(gca,'xtick',[levels(1):20:levels(end)]) % grid on ttl=[ 'spont=' num2str(mean(CNHSRsaturated(1,:)),'%4.0f') ' sat=' ... @@ -256,13 +275,15 @@ xlabel('level dB SPL'), ylabel ('adapted rate (sp/s)') text(0, 350, 'CN', 'fontsize', 14), grid on -% IC rate - level ADAPTED function +% IC rate - level function subplot(nRows,nCols,4) plot(levels,ICLSRsaturated, 'ro'), hold on -plot(levels,ICHSRsaturated, 'ko'), hold off +plot(levels,ICHSRsaturated, 'ko', 'MarkerEdgeColor','k', 'markerFaceColor','k'), hold off ylim([0 400]) set(gca,'ytick',0:50:300) +if length(levels)>1 xlim([min(levels) max(levels)]) +end set(gca,'xtick',[levels(1):20:levels(end)]), grid on ttl=['spont=' num2str(mean(ICHSRsaturated(1,:)),'%4.0f') ... ' sat=' num2str(mean(ICHSRsaturated(end,1)),'%4.0f')];
--- a/testPrograms/testANprob.m Thu Oct 06 15:43:20 2011 +0100 +++ b/testPrograms/testANprob.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,6 +1,9 @@ function testANprob(targetFrequency,BFlist, levels, ... paramsName, paramChanges) - +% testANprob generates rate/level functions for AN and brainstem units. +% also other information like PSTHs, MOC efferent activity levels. +% A 'probability' model is used. +% e.g. % testANprob(1000,1000, -10:10:80, 'Normal') global IHC_VResp_VivoParams IHC_cilia_RPParams IHCpreSynapseParams @@ -151,7 +154,7 @@ % AN rate - level ONSET functions subplot(nRows,nCols,1) plot(levels,AN_LSRonset,'ro'), hold on -plot(levels,AN_HSRonset,'ko'), hold off +plot(levels,AN_HSRonset,'ko', 'MarkerEdgeColor','k', 'markerFaceColor','k'), hold off ylim([0 1000]), xlim([min(levels) max(levels)]) ttl=['tauCa= ' num2str(IHCpreSynapseParams.tauCa)]; title( ttl) @@ -161,7 +164,7 @@ % AN rate - level ADAPTED function subplot(nRows,nCols,2) plot(levels,AN_LSRsaturated, 'ro'), hold on -plot(levels,AN_HSRsaturated, 'ko'), hold off +plot(levels,AN_HSRsaturated, 'ko', 'MarkerEdgeColor','k', 'markerFaceColor','k'), hold off maxYlim=340; ylim([0 maxYlim]) set(gca,'ytick',0:50:300)
--- a/testPrograms/testBM.m Thu Oct 06 15:43:20 2011 +0100 +++ b/testPrograms/testBM.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,15 +1,20 @@ function testBM (BFlist, paramsName,... relativeFrequencies, AN_spikesOrProbability, paramChanges) % testBM generates input output functions for DRNL model for any number -% of locations. -% Computations are bast on a single channel model (channelBFs=BF) -% peak displacement (peakAmp) is measured. -% if DRNLParams.useMOC is chosen, the full model is run (slow) -% otherwise only DRNL is computed. -% Tuning curves are generated based on a range of frequencies reletove to -% the BF of the location. +% of locations (BFlist). +% Each BF is evaluated using a single channel model +% +% Peak displacement as a function of pure tone level(peakAmp) is displayed +% +% If relative Frequencies is set to a range of values, tuning curves will +% be computed using these stimulus frequencie. +% +% If AN_spikesOrProbability is set to 'spikes' the full model is run (slow) +% otherwise efferent activity is based on AN pspiking probabilities. % % testBM (1000, 'Normal', 1, 'probability', []) +% for tuning curves at 6 locations: +% testBM ([250 500 1000 2000 4000 8000], 'Normal', [.5 .75 .9 1 1.1 1.25 1.5], 'probability', []) global DRNLParams @@ -20,8 +25,9 @@ savePath=path; addpath (['..' filesep 'utilities'],['..' filesep 'MAP']) +tic -levels=-10:10:90; nLevels=length(levels); +levels=-10:10:100; nLevels=length(levels); % levels= 50; nLevels=length(levels); % refBMdisplacement is the displacement of the BM at threshold @@ -124,17 +130,21 @@ set(gca,'ytick',[-10 0 10 20 40]) grid on % legend({num2str(stimulusFrequencies')}, 'location', 'EastOutside') - UTIL_printTabTable([levels' peakAmpBMdB], ... + UTIL_printTabTable([levels' peakAmpBMdB peakAmpBM*1e9], ... num2str([0 stimulusFrequencies]','%5.0f'), '%5.0f') finalSummary=[finalSummary peakAmpBMdB]; % Tuning curve if length(relativeFrequencies)>2 figure(3), subplot(maxRows,nBFs, 2*nBFs+BFno) - % contour(stimulusFrequencies,levels,peakAmpBM,... - % [refBMdisplacement refBMdisplacement],'r') - contour(stimulusFrequencies,levels,peakAmpBM,... - refBMdisplacement.*[1 5 10 50 100]) + contour(stimulusFrequencies,levels,peakAmpBM,... + [refBMdisplacement refBMdisplacement],'r','linewidth',4) + ylim([-10 40]) + +% contour(stimulusFrequencies,levels,peakAmpBM,... +% refBMdisplacement.*[1 5 10 50 100]) +% ylim([-10 90]) + title(['tuning curve at ' num2str(refBMdisplacement) 'm']); ylabel('level (dB) at reference') xlim([100 10000]) @@ -148,7 +158,7 @@ figure(3) subplot(maxRows,nBFs,nBFs+BFno), cla plot(levels,20*log10(peakEfferent), 'linewidth',2) - ylabel('MOC (dB attenuation)'), xlabel('level') + ylabel('MOC (dB attenuation)'), xlabel('level (dB SPL)') title(['MOC: (' AN_spikesOrProbability ') duration= ' ... num2str(1000*toneDuration,'%5.0f') ' ms']) grid on @@ -161,6 +171,7 @@ end % best frequency + UTIL_showStructureSummary(DRNLParams, 'DRNLParams', 10) UTIL_printTabTable([levels' finalSummary], ... @@ -170,5 +181,5 @@ disp(paramChanges) end - +toc path(savePath);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/testDPOAE.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,105 @@ +function [frequencies fft_ampdB]= ... + testDPOAE (leveldBSPL, toneFrequencies) +% MAPdemo runs the MATLAB auditory periphery model +% +% The OAE is simulated by combining the output from all DRNL channels +% +% arguments leveldBSPL and toneFrequencies are optional +% defaults are 70 and [5000 6000] +% +% e.g. +% MAPdemoMultiChOAE (60, [3000 4000]) + + +global dt DRNLoutput +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'utilities'], ... + ['..' filesep 'parameterStore'], ['..' filesep 'wavFileStore'],... + ['..' filesep 'testPrograms']) + +% set parameter file here +paramsName='Normal'; +% choose probability because spikes not used to evaluate BM +AN_spikesOrProbability='probability'; +% add parameter changes here. paramchanges is a cell array of command +% strings +paramChanges={}; + +% DRNL channels +lowestBF=1000; highestBF= 8000; numChannels=41; +% includes BFs at 250 500 1000 2000 4000 8000 (for 11, 21, 31 BFs) +% the output from all these filters will be combined to form the OAE +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +if nargin<2 + toneFrequencies= 2000; % single pure tone test + toneFrequencies=[ 2000 3000]; % F1 F2 for DPOAEs + toneFrequencies=[ 5000 6000]; % F1 F2 +end +duration=0.05; % seconds +duration=0.05; % seconds +rampDuration=.005; + +if nargin<1 + leveldBSPL=70; % dB SPL +end +amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak level) + +% Create pure stimulus +sampleRate= 100000; +dt=1/sampleRate; +time=dt: dt: duration; +inputSignal=sum(sin(2*pi*toneFrequencies'*time), 1); +inputSignal=amp*inputSignal; + +% apply ramps +if rampDuration>0.5*duration, rampDuration=duration/2; end +rampTime=dt:dt:rampDuration; +ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; +inputSignal=inputSignal.*ramp; % at the beginning +ramp=fliplr(ramp); +inputSignal=inputSignal.*ramp; % and at the end + +% add 10 ms silence +silenceDuration=0.01; +silence= zeros(1,round(silenceDuration/dt)); +inputSignal= [silence inputSignal silence]; +time=dt: dt: dt*length(inputSignal); + +%% delare 'showMap' options to control graphical output +showMapOptions.printModelParameters=0; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=0; % tracks of AR and MOC +showMapOptions.surfAN=0; % 2D plot of HSR response +showMapOptions.surfSpikes=0; % 2D plot of spikes histogram +showMapOptions.ICrates=0; % IC rates by CNtauGk + + +MAP1_14(inputSignal, 1/dt, BFlist, ... + paramsName, AN_spikesOrProbability, paramChanges); + +UTIL_showMAP(showMapOptions) +pause(0.1) + +% use this to produce a comnplete record of model parameters +% UTIL_showAllMAPStructures + +OAE=sum(DRNLoutput); +figure(5),subplot(2,1,1) +plot(time,OAE) +title(['F=' num2str(toneFrequencies)]) +[fft_powerdB, fft_phase, frequencies, fft_ampdB]= UTIL_FFT(OAE, dt, 1e-15); +idx=find(frequencies<1e4); + +figure(5),subplot(2,1,2) +plot(frequencies(idx),fft_ampdB(idx)) +title ('FFT of OAE') +ylabel('dB') +ylim([0 100]) +grid on + +path(restorePath);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/testEfferent.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,118 @@ +function testEfferent(probeFrequency,BFlist, levels, ... + paramsName,paramChanges) +% generates rate/level functions for AAR and MOC +% +% e.g. +% testEfferent(1000,1000, -10:10:80,'Normal',[]); + +global dtSpikes MOCattenuation ANtauCas + +tic +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'utilities'], ... + ['..' filesep 'parameterStore'], ['..' filesep 'wavFileStore'],... + ['..' filesep 'testPrograms']) + +if nargin<5, paramChanges=[]; end +if nargin<4, paramsName='Normal'; end +if nargin<3, levels=-10:10:100; end +if nargin==0, + probeFrequency=1000; + probeFrequency=100:100:8000; + lowestBF=250; highestBF= 8000; numChannels=21; + % 21 chs (250-8k)includes BFs at 250 500 1000 2000 4000 8000 + BFlist=round(logspace(log10(lowestBF),log10(highestBF),numChannels)); + keyChannel=round(numChannels/2); +% BFlist=1000; +end +nLevels=length(levels); + +toneDuration=.2; rampDuration=0.002; silenceDuration=.02; +localPSTHbinwidth=0.001; + + +sampleRate=64000; dt=1/sampleRate; + +%% delare 'showMap' options to control graphical output +showMapOptions.printModelParameters=0; % prints all parameters +showMapOptions.showModelOutput=0; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=1; % tracks of AR and MOC +showMapOptions.surfAN=0; % 2D plot of HSR response +showMapOptions.surfSpikes=0; % 2D plot of spikes histogram +showMapOptions.ICrates=0; % IC rates by CNtauGk + + +%% pre-allocate storage +AN_HSRonset=zeros(nLevels,1); +AN_HSRsaturated=zeros(nLevels,1); +AN_LSRonset=zeros(nLevels,1); +AN_LSRsaturated=zeros(nLevels,1); +CNLSRrate=zeros(nLevels,1); +CNHSRsaturated=zeros(nLevels,1); +ICHSRsaturated=zeros(nLevels,1); +ICLSRsaturated=zeros(nLevels,1); +vectorStrength=zeros(nLevels,1); + +AR=zeros(nLevels,1); +MOC=zeros(nLevels,1); +maxMOC=[]; + +%% main computational loop (vary level) +levelNo=0; +for leveldB=levels + levelNo=levelNo+1; + amp=28e-6*10^(leveldB/20); + fprintf('%4.0f\t', leveldB) + + %% generate tone and silences + time=dt:dt:toneDuration; + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + ramp=ramp.*fliplr(ramp); + + silence=zeros(1,round(silenceDuration/dt)); + + inputSignal=amp*sin(2*pi*probeFrequency'*time); + inputSignal=sum(inputSignal); + inputSignal= ramp.*inputSignal; + inputSignal=[silence inputSignal]; + + %% run the model + AN_spikesOrProbability='spikes'; +% nExistingParamChanges=length(paramChanges); +% paramChanges{nExistingParamChanges+1}=... +% ['AN_IHCsynapseParams.spikesTargetSampleRate=' ... +% num2str(spikesSampleRate) ';']; + + MAP1_14(inputSignal, 1/dt, BFlist, ... + paramsName, AN_spikesOrProbability, paramChanges); + +maxMOC=[maxMOC min(MOCattenuation(keyChannel,:))]; + UTIL_showMAP(showMapOptions) + pause(0.1) + + %% Auditory nerve evaluate and display (Fig. 5) + %LSR (same as HSR if no LSR fibers present) + nTaus=length(ANtauCas); + + +end % level + +%% MOC atten/ level function +figure(21), subplot(2,1,2) +plot(levels, 20*log10(maxMOC), 'k'), hold off +title(' MOC dB attenuation'), ylabel('dB attenuation') +ylim([-30 0]) +figure(21), subplot(2,1,1) +plot(levels, maxMOC, 'k'), hold off +title(' MOC attenuation (scalar)'), ylabel('attenuation (scalar)') +ylim([0 1]) + +set(gcf,'name','MOC atten/level') + +path(restorePath) +toc
--- a/testPrograms/testFM.m Thu Oct 06 15:43:20 2011 +0100 +++ b/testPrograms/testFM.m Mon Nov 28 13:34:28 2011 +0000 @@ -12,7 +12,7 @@ global inputStimulusParams outerMiddleEarParams DRNLParams global IHC_VResp_VivoParams IHCpreSynapseParams AN_IHCsynapseParams -global ANprobRateOutput ANoutput ANtauCas ANdt +global ANprobRateOutput ANoutput ANtauCas dtSpikes dbstop if error restorePath=path; addpath (['..' filesep 'MAP'], ['..' filesep 'utilities'], ... @@ -26,7 +26,7 @@ paramChanges=[]; else if nargin<3 - paramChanges=[]; + paramChanges=[]; end end @@ -144,13 +144,13 @@ inputSignal=... [initialSilence maskerPa gap probe finalSilence]; -% time=dt:dt:length(inputSignal)*dt; -% figure(99), plot(time,inputSignal) + % time=dt:dt:length(inputSignal)*dt; + % figure(99), plot(time,inputSignal) % ********************************** run MAP model -% showPlotsAndDetails=0; -nChanges=length(paramChanges); -paramChanges{nChanges+1}='AN_IHCsynapseParams.numFibers= 500;'; + % showPlotsAndDetails=0; + nChanges=length(paramChanges); + paramChanges{nChanges+1}='AN_IHCsynapseParams.numFibers= 500;'; MAP1_14(inputSignal, 1/dt, targetFrequency, ... paramsName, AN_spikesOrProbability, paramChanges); @@ -158,7 +158,7 @@ [nFibers c]=size(ANprobRateOutput); nLSRfibers=nFibers/length(ANtauCas); ANresponse=ANprobRateOutput(end-nLSRfibers:end,:); - ANdt=dt; % no adjustment for spikes speedup + dtSpikes=dt; % no adjustment for spikes speedup else [nFibers c]=size(ANoutput); nLSRfibers=nFibers/length(ANtauCas); @@ -166,12 +166,12 @@ end ANresponse=sum(ANresponse); -% ANresponseTimes=ANdt:ANdt:length(ANresponse)*ANdt; -% figure(99), plot(ANresponseTimes,ANresponse) + % ANresponseTimes=dtSpikes:dtSpikes:length(ANresponse)*dtSpikes; + % figure(99), plot(ANresponseTimes,ANresponse) % analyse results probeStart=initialSilenceDuration+maskerDuration+gapDuration; - PSTHbinWidth=ANdt; + PSTHbinWidth=dtSpikes; responseDelay=0.005; probeTimes=probeStart+responseDelay:... PSTHbinWidth:probeStart+probeDuration+responseDelay; @@ -189,20 +189,20 @@ nDurations=length(gapDurations); figure(8) PSTHbinWidth=1e-3; - PSTH=UTIL_PSTHmaker(ANresponse, ANdt, PSTHbinWidth); - PSTH=PSTH*ANdt/PSTHbinWidth; + PSTH=UTIL_PSTHmaker(ANresponse, dtSpikes, PSTHbinWidth); + PSTH=PSTH*dtSpikes/PSTHbinWidth; PSTHplotCount=PSTHplotCount+1; subplot(nLevels,nDurations,PSTHplotCount) PSTHtime=PSTHbinWidth:PSTHbinWidth:... PSTHbinWidth*length(PSTH); if strcmp(AN_spikesOrProbability, 'spikes') - bar(PSTHtime, PSTH/PSTHbinWidth/nFibers) -% ylim([0 500]) + bar(PSTHtime, PSTH/PSTHbinWidth/nFibers) + % ylim([0 500]) else - bar(PSTHtime, PSTH) + bar(PSTHtime, PSTH) ylim([0 500]) end -% xlim([0 longestSignalDuration]) + xlim([0 longestSignalDuration]) grid on if PSTHplotCount< (nLevels-1) * nDurations+1 @@ -220,7 +220,7 @@ title([num2str(1000*gapDurations(PSTHplotCount)) 'ms']) end -% figure(99), bar(PSTHtime, PSTH) + % figure(99), bar(PSTHtime, PSTH) end % showPSTHs
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/testLibermanMOC_DPOAE.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,189 @@ +function testLibermanMOC_DPOAE + +% compares MOC response to LIberman's 1996 data for DPOAE reduction with +% contralateral tone stimulation. +% This program is used mainly as a check on the time constants involved. +% +% NB very different time constants are required for 'spikes' and +% 'probability' + +% global dt ANdt savedBFlist saveAN_spikesOrProbability saveMAPparamsName... +% savedInputSignal OMEextEarPressure TMoutput OMEoutput ARattenuation ... +% DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... +% IHCoutput ANprobRateOutput ANoutput savePavailable ANtauCas ... +% CNtauGk CNoutput ICoutput ICmembraneOutput ICfiberTypeRates ... +% MOCattenuation +global dt dtSpikes saveAN_spikesOrProbability ANprobRateOutput ICoutput + +global DRNLParams + +LibermanData=[ + 2 0.2; +2.1 0.19;2.2 0.18;2.3 0.18;2.4 0.16;2.5 0.15;2.6 0.15;2.7 0.15; +2.8 0.12;2.9 0.12;3 0.1;3.1 0.1;3.2 0.05;3.3 0.05;3.4 0;3.5 -0.1; +3.6 -0.4;3.7 -1.2;3.8 -1.6;3.9 -1.8;4 -1.85;4.1 -2;4.2 -2.05; +4.3 -2.05;4.4 -2.15;4.5 -2.2;4.6 -2.15;4.7 -2.1;4.8 -2.15;4.9 -2.2; +5 -2.1;5.1 -2.1;5.2 -2.25;5.3 -2.1;5.4 -2.15;5.5 -2.1;5.6 -2.15; +5.7 -2.1;5.8 -2.2;5.9 -2.05;6 -2.15;6.1 -2.05;6.2 -2;6.3 -2.2;6.4 -2.1; +6.5 -2.05;6.6 -2.05;6.7 -2.05;6.8 -2.2;6.9 -2.1;7 -2.05;7.1 -2.05;7.2 -0.7; +7.3 -0.1;7.4 0;7.5 0.1;7.6 0.2;7.7 0.35;7.8 0.2;7.9 0.15;8 0.2;8.1 0.15;8.2 0.15; +8.3 0.15;8.4 0.12;8.5 0.1;8.6 0.09;8.7 0.08;8.8 0.07;8.9 0.06;9 0.05; +]; + +% Backus2006Data: time bilateral contralateral ipsilateral +% all % max microPascals +Backus2006Data=[ + 100 20 15 10; + 200 36 24 14; + 300 44 30 18; + 400 46 32 20; + 500 48 34 22; + 1000 50 36 24; + 1500 52 37 25; + 2000 54 38 27 + ]; + +steadyMinimum=mean(LibermanData(LibermanData(:,1)>4 & LibermanData(:,1)<7,2)); + +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation +AN_spikesOrProbability='spikes'; +% or +AN_spikesOrProbability='probability'; + + +%% #3 pure tone, harmonic sequence or speech file input +signalType= 'tones'; +sampleRate= 50000; +rampDuration=.005; % raised cosine ramp (seconds) +toneFrequency= 1000; % or a pure tone (Hz) +duration=3.6; % Lieberman test +beginSilence=1; % 1 for Lieberman +endSilence=1; % 1 for Lieberman + +%% #4 rms level +% signal details +leveldBSPL= 80; % dB SPL (80 for Lieberman) + + +%% #5 number of channels in the model + +numChannels=1; +BFlist=toneFrequency; + + +%% #6 change model parameters +paramChanges={}; + +%% delare 'showMap' options to control graphical output +showMapOptions.printModelParameters=1; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=1; % tracks of AR and MOC +showMapOptions.surfAN=1; % 2D plot of HSR response +showMapOptions.surfSpikes=0; % 2D plot of spikes histogram +showMapOptions.ICrates=0; % IC rates by CNtauGk + +%% Generate stimuli + + % Create pure tone stimulus + dt=1/sampleRate; % seconds + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + % catch rampTime error + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + +%% run the model +tic +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) +disp('Computing ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +%% the model run is now complete. Now display the results +UTIL_showMAP(showMapOptions) + +if strcmp(signalType,'tones') + disp(['duration=' num2str(duration)]) + disp(['level=' num2str(leveldBSPL)]) + disp(['toneFrequency=' num2str(toneFrequency)]) + disp(['attenuation factor =' ... + num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) + disp(['attenuation factor (probability)=' ... + num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) + disp(AN_spikesOrProbability) +end +disp(paramChanges) + + + +%% superimpose Lieberman (1996) data + +global MOCattenuation +MOCdB=20*log10(MOCattenuation); +MOCtime=dt:dt:dt*length(MOCdB); + +% scale up DPOAE results to the running maximum MOC dB +steadyMOCminimum=mean(MOCdB(MOCtime>2 & MOCtime<4.5)); +scalar=steadyMOCminimum/steadyMinimum; + +figure(90), clf +plot(MOCtime,MOCdB), hold on +plot(LibermanData(:,1)-2.5,scalar*LibermanData(:,2),'r:','linewidth',4) +legend({'MAP', 'DPOAE'},'location', 'east') +title('Compare Liberman (1996) DPOAE data with MAP MOC') +xlabel('time (s)'), ylabel('MOC attenuation/ DPOAE reduction') +if strcmp(saveAN_spikesOrProbability,'probability') +text(0,2,['MOCtau= ' num2str(DRNLParams.MOCtauProb)]) +else +text(0,2,['MOCtau= ' num2str(DRNLParams.MOCtau)]) +end +set(gcf, 'name', 'Liberman compare') + +PSTHbinwidth=0.001; + +% show the source of the MOC activity +figure(89) +if strcmp(saveAN_spikesOrProbability,'probability') + % brainstem activity + PSTH=UTIL_PSTHmaker... + (ANprobRateOutput(2,:), dt, PSTHbinwidth)*dt/PSTHbinwidth; +else + % AN probability + PSTH=UTIL_PSTHmaker(ICoutput(2,:), dtSpikes, PSTHbinwidth)*dt/PSTHbinwidth; +end + +time=PSTHbinwidth:PSTHbinwidth:PSTHbinwidth*length(PSTH); +plot(time, PSTH) +set(gcf,'name', 'Lieberman') +title(saveAN_spikesOrProbability) + +toc +path(restorePath) + +% figure(88), plot(MOCattenuation)
--- a/testPrograms/testOME.m Thu Oct 06 15:43:20 2011 +0100 +++ b/testPrograms/testOME.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,16 +1,24 @@ function testOME(paramsName, paramChanges) -% testOME compute the stapes response at a number of frequencies -% and compares the stapes displacement with in vivo data +% testOME compute the external resonance and +% stapes response at a number of frequencies +% It compares the stapes displacement against human in vivo data % collected by Huber et al.2001. -% e.g. +% paramsName: name of file in parameterStore containing model parameters +% paramchanges: string array of changes to parameters. +% this can be omitted or {} is acceptable argument +% % testOME('Normal',{}) savePath=path; addpath (['..' filesep 'utilities'],['..' filesep 'MAP']) +% default arguments if nargin<2 paramChanges=[]; end +if nargin<1 + paramsName='Normal'; +end sampleRate=50000;
--- a/testPrograms/testPhaseLocking.m Thu Oct 06 15:43:20 2011 +0100 +++ b/testPrograms/testPhaseLocking.m Mon Nov 28 13:34:28 2011 +0000 @@ -4,13 +4,20 @@ paramChanges=[]; end +if nargin<1 + paramsName='Normal'; +end + testFrequencies=[250 500 1000 2000 4000 8000]; -levels=50:10:80; +levels=0:10:100; + figure(14), clf set(gcf,'position', [980 36 383 321]) set(gcf,'name', 'phase locking') + allStrengths=zeros(length(testFrequencies), length(levels)); peakVectorStrength=zeros(1,length(testFrequencies)); + freqCount=0; for targetFrequency=testFrequencies; %single test @@ -24,7 +31,7 @@ %% plot results figure(14) subplot(2,1,2) -plot(levels,allStrengths) +plot(levels,allStrengths, '+') xlabel('levels') ylabel('vector strength') legend (num2str(testFrequencies'),'location','eastOutside')
--- a/testPrograms/testPhysiology.m Thu Oct 06 15:43:20 2011 +0100 +++ b/testPrograms/testPhysiology.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,22 +1,40 @@ function testPhysiology(BF,paramsName, paramChanges) % e.g. -% testPhysiology(1000,'Normal', []) +% testPhysiology(1000,'Normal', {}) restorePath=path; addpath (['..' filesep 'MAP']) -if nargin<3 - paramChanges=[]; -end +if nargin<3, paramChanges=[]; end +if nargin<2, paramsName='Normal'; end + +if nargin<1, BF=1000; end + +disp('testPhysiology...........computing') +disp('testOME...........computing') testOME(paramsName,paramChanges) + relativeFrequencies=[0.25 .5 .75 1 1.25 1.5 2]; +disp('testBM...........computing') testBM (BF, paramsName,relativeFrequencies,'spikes', paramChanges) -testRP(BF,paramsName,paramChanges) -testSynapse(BF,paramsName,paramChanges) + +disp('testRP...........computing') +testRP2(paramsName,paramChanges) + +disp('testSynapse...........computing') +testSynapse(BF,paramsName, 'spikes', paramChanges) + +disp('testFM...........computing') testFM(BF,paramsName,'spikes', paramChanges) + +disp('testPhaseLocking...........computing') testPhaseLocking(paramsName,paramChanges) + +disp('testAN...........computing') testAN(BF,BF, -10:10:80,paramsName,paramChanges); + +% put these figures on top figure(14) figure(15)
--- a/testPrograms/testPhysiologyProb.m Thu Oct 06 15:43:20 2011 +0100 +++ b/testPrograms/testPhysiologyProb.m Mon Nov 28 13:34:28 2011 +0000 @@ -9,12 +9,30 @@ paramChanges=[]; end +if nargin==0 + error('testPhysiologyProb must be called from the command line') +end + + +disp('testPhysiologyProb...........computing') + +disp('testOME...........computing') testOME(paramsName, paramChanges) + +disp('testBM...........computing') relativeFrequencies=[0.25 .5 .75 1 1.25 1.5 2]; testBM (BF, paramsName,relativeFrequencies,'probability', paramChanges) -testRP(BF,paramsName, paramChanges) -testSynapse(BF,paramsName, paramChanges) + +disp('testRP...........computing') +testRP2(paramsName,paramChanges) + +disp('testSynapse...........computing') +testSynapse(BF,paramsName, 'probability', paramChanges) + +disp('testFM...........computing') testFM(BF,paramsName,'probability', paramChanges) + +disp('testANprob...........computing') testANprob(BF,BF, -10:10:80,paramsName, paramChanges); figure(4)
--- a/testPrograms/testRF.m Thu Oct 06 15:43:20 2011 +0100 +++ b/testPrograms/testRF.m Mon Nov 28 13:34:28 2011 +0000 @@ -113,7 +113,7 @@ moduleSequence=[1:8]; - global ANdt ARAttenuation TMoutput OMEoutput ... + global dtSpikes ARAttenuation TMoutput OMEoutput ... DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... IHCoutput ANprobRateOutput ANoutput savePavailable ANtauCas ... CNoutput ICoutput ICmembraneOutput ICfiberTypeRates MOCattenuation
--- a/testPrograms/testRP.m Thu Oct 06 15:43:20 2011 +0100 +++ b/testPrograms/testRP.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,5 +1,5 @@ function testRP(BFs,MAPparamsName,paramChanges) -% testIHC used for IHC I/O function +% testIHC evaluates IHC I/O function % multiple BFs can be used but only one is easier to interpret. % e.g. testRP(1000,'Normal',{}); @@ -18,6 +18,12 @@ if nargin<3 paramChanges=[]; end +if nargin<2 + MAPparamsName='Normal'; +end +if nargin<3 + BFs=800; +end levels=-20:10:100; nLevels=length(levels); @@ -133,10 +139,10 @@ subplot(2,2,2) restingIHC_cilia=IHCrestingCiliaCond; plot(levels, IHC_cilia_peak,'k', 'linewidth',2), hold on - plot(levels, IHC_cilia_min,'r', 'linewidth',2) - hold on, - plot([min(levels) max(levels)], ... - [restingIHC_cilia restingIHC_cilia], 'g') +% plot(levels, IHC_cilia_min,'r', 'linewidth',2) +% hold on, +% plot([min(levels) max(levels)], ... +% [restingIHC_cilia restingIHC_cilia], 'g') title(' IHC apical cond.') ylabel ('IHCcilia(conductance)'), xlabel('dB SPL') xlim([min(levels) max(levels)])
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/testRP2.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,295 @@ +function testRP2(MAPparamsName,paramChanges) +% testIHC evaluates IHC I/O function +% multiple BF can be used but only one is easier to interpret. +% e.g. testRP(1000,'Normal',{}); + +global experiment method inputStimulusParams +global stimulusParameters IHC_VResp_VivoParams IHC_cilia_RPParams +savePath=path; +addpath (['..' filesep 'utilities'],['..' filesep 'MAP']) +dbstop if error + +figure(4), clf, +set (gcf, 'name', ['IHC']) +set(gcf,'position',[613 354 360 322]) +drawColors='rgbkmcy'; +drawnow + +if nargin<3 + paramChanges=[]; +end +if nargin<2 + MAPparamsName='Normal'; +end +if nargin<3 + BF=800; +end + +levels=-20:10:100; +nLevels=length(levels); +toneDuration=.05; +silenceDuration=.01; +sampleRate=50000; +dt=1/sampleRate; + +allIHC_RP_peak=[]; +allIHC_RP_dc=[]; + +%% Ruggero +%%Ruggero data +RuggeroData=[ + 0 2.00E-10; + 10 5.00E-10; + 20 1.50E-09; + 30 2.50E-09; + 40 5.30E-09; + 50 1.00E-08; + 60 1.70E-08; + 70 2.50E-08; + 80 4.00E-08; + 90 6.00E-08; + 100 1.50E-07; + 110 3.00E-07; + ]; + + +BF=10000; +targetFrequency=BF; + +IHC_RP_peak=zeros(nLevels,1); +IHC_RP_min=zeros(nLevels,1); +IHC_RP_dc=zeros(nLevels,1); + +time=dt:dt:toneDuration; + +rampDuration=0.004; +rampTime=dt:dt:rampDuration; +ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; +ramp=ramp.*fliplr(ramp); + +silence=zeros(1,round(silenceDuration/dt)); + +toneStartptr=length(silence)+1; +toneMidptr=toneStartptr+round(toneDuration/(2*dt)) -1; +toneEndptr=toneStartptr+round(toneDuration/dt) -1; + +levelNo=0; +for leveldB=levels + levelNo=levelNo+1; + % replicate at all levels + amp=28e-6*10^(leveldB/20); + + %% create signal (leveldB/ frequency) + inputSignal=amp*sin(2*pi*targetFrequency'*time); + inputSignal= ramp.*inputSignal; + inputSignal=[silence inputSignal silence]; + inputStimulusParams.sampleRate=1/dt; + % global IHC_ciliaParams + + %% disable efferent for fast processing + + %% run the model + global DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... + IHCoutput + AN_spikesOrProbability='probability'; + + MAP1_14(inputSignal, sampleRate, BF, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + % DRNL + DRNLoutput=DRNLoutput; + DRNL_peak(levelNo,1)=max(DRNLoutput(toneMidptr:toneEndptr)); + DRNL_min(levelNo,1)=min(DRNLoutput(toneMidptr:toneEndptr)); + DRNL_dc(levelNo,1)=mean(DRNLoutput(toneMidptr:toneEndptr)); + +end +%% plot DRNL +subplot(2,2,1) +referenceDisp=10e-9; +semilogy(levels,DRNL_peak, 'linewidth',2), hold on +semilogy(RuggeroData(:,1),RuggeroData(:,2),'o') +title(['BM: Ruggero ' num2str(BF) ' Hz']) +ylabel ('displacement(m)'), xlabel('dB SPL') +xlim([min(levels) max(levels)]), ylim([1e-10 1e-7]) +grid on + + +%% Dallos +BF=800; +targetFrequency=BF; + +IHC_RP_peak=zeros(nLevels,1); +IHC_RP_min=zeros(nLevels,1); +IHC_RP_dc=zeros(nLevels,1); + +time=dt:dt:toneDuration; + +rampDuration=0.004; +rampTime=dt:dt:rampDuration; +ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; +ramp=ramp.*fliplr(ramp); + +silence=zeros(1,round(silenceDuration/dt)); + +toneStartptr=length(silence)+1; +toneMidptr=toneStartptr+round(toneDuration/(2*dt)) -1; +toneEndptr=toneStartptr+round(toneDuration/dt) -1; + +levelNo=0; +for leveldB=levels + levelNo=levelNo+1; + % replicate at all levels + amp=28e-6*10^(leveldB/20); + + %% create signal (leveldB/ frequency) + inputSignal=amp*sin(2*pi*targetFrequency'*time); + inputSignal= ramp.*inputSignal; + inputSignal=[silence inputSignal silence]; + inputStimulusParams.sampleRate=1/dt; + % global IHC_ciliaParams + + %% disable efferent for fast processing + method.DRNLSave=1; + method.IHC_cilia_RPSave=1; + method.IHCpreSynapseSave=1; + method.IHC_cilia_RPSave=1; + method.segmentDuration=-1; + moduleSequence=1:4; + + %% run the model + global DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... + IHCoutput + AN_spikesOrProbability='probability'; + + MAP1_14(inputSignal, sampleRate, BF, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + % DRNL + DRNLoutput=DRNLoutput; + DRNL_peak(levelNo,1)=max(DRNLoutput(toneMidptr:toneEndptr)); + DRNL_min(levelNo,1)=min(DRNLoutput(toneMidptr:toneEndptr)); + DRNL_dc(levelNo,1)=mean(DRNLoutput(toneMidptr:toneEndptr)); + + % cilia + IHC_ciliaData=IHC_cilia_output; + IHC_ciliaData=IHC_ciliaData; + IHC_cilia_peak(levelNo,1)=... + max(IHC_ciliaData(toneMidptr:toneEndptr)); + IHC_cilia_min(levelNo,1)=... + min(IHC_ciliaData(toneMidptr:toneEndptr)); + IHC_cilia_dc(levelNo,1)=... + mean(IHC_ciliaData(toneMidptr:toneEndptr)); + + % RP + IHC_RPData=IHCoutput; + IHC_RPData=IHC_RPData; + IHC_RP_peak(levelNo,1)=... + max(IHC_RPData(toneMidptr:toneEndptr)); + IHC_RP_min(levelNo,1)=... + min(IHC_RPData(toneMidptr:toneEndptr)); + IHC_RP_dc(levelNo,1)=... + mean(IHC_RPData(toneMidptr:toneEndptr)); + +end + +%% Dallos and Harris data +%% plot receptor potentials +figure(4) +subplot(2,2,3) +% RP I/O function min and max +restingRP=IHC_RP_peak(1); +toPlot= [fliplr(IHC_RP_min(:,1)') IHC_RP_peak(:,1)']; +microPa= 28e-6*10.^(levels/20); +microPa=[-fliplr(microPa) microPa]; +plot(microPa,toPlot, 'linewidth',2) + +dallosx=[-0.9 -0.1 -0.001 0.001 0.01 0.9]; +dallosy=[-8 -7.8 -6.5 11 16.5 22]/1000 + restingRP; +subplot(2,2,3) +hold on, plot(dallosx,dallosy, 'o') +plot([-1 1], [restingRP restingRP], 'r') +title(' Dallos(86) data at 800 Hz') +ylabel ('receptor potential(V)'), xlabel('Pa') +ylim([-0.08 -0.02]), xlim([-1 1]) +grid on + + +%% Patuzzi +BF=7000; +targetFrequency=BF; + +IHC_RP_peak=zeros(nLevels,1); +IHC_RP_min=zeros(nLevels,1); +IHC_RP_dc=zeros(nLevels,1); + +time=dt:dt:toneDuration; + +rampDuration=0.004; +rampTime=dt:dt:rampDuration; +ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; +ramp=ramp.*fliplr(ramp); + +silence=zeros(1,round(silenceDuration/dt)); + +toneStartptr=length(silence)+1; +toneMidptr=toneStartptr+round(toneDuration/(2*dt)) -1; +toneEndptr=toneStartptr+round(toneDuration/dt) -1; + +levelNo=0; +for leveldB=levels + levelNo=levelNo+1; + % replicate at all levels + amp=28e-6*10^(leveldB/20); + + %% create signal (leveldB/ frequency) + inputSignal=amp*sin(2*pi*targetFrequency'*time); + inputSignal= ramp.*inputSignal; + inputSignal=[silence inputSignal silence]; + inputStimulusParams.sampleRate=1/dt; + % global IHC_ciliaParams + + %% run the model + global DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... + IHCoutput + AN_spikesOrProbability='probability'; + + MAP1_14(inputSignal, sampleRate, BF, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + % RP + IHC_RPData=IHCoutput; + IHC_RP_peak(levelNo,1)=... + max(IHC_RPData(toneMidptr:toneEndptr)); + IHC_RP_min(levelNo,1)=... + min(IHC_RPData(toneMidptr:toneEndptr)); + IHC_RP_dc(levelNo,1)=... + mean(IHC_RPData(toneMidptr:toneEndptr)); +end + + +%% RP I/O function min and max +figure(4) +subplot(2,2,4) +restingRP=IHC_RP_peak(1); +peakRP=max(IHC_RP_peak); +plot(levels, IHC_RP_peak, 'linewidth',2) +hold on +plot(levels, IHC_RP_dc, ':', 'linewidth',2) +plot([min(levels) max(levels)], [restingRP restingRP], 'r') +xlim([min(levels) max(levels)]) +% animal data +sndLevel=[5 15 25 35 45 55 65 75]; +RPanimal=restingRP+[0.5 2 4.6 5.8 6.4 7.2 8 10.2]/1000; +% could be misleading when restingRP changes +RPanimal=-0.060+[0.5 2 4.6 5.8 6.4 7.2 8 10.2]/1000; +hold on, plot(sndLevel,RPanimal,'o') + +grid on +title(' 7 kHz Patuzzi') +ylabel ('RP(V) peak and DC'), xlabel('dB SPL') +ylim([-0.07 -0.04]) +path(savePath); +disp(paramChanges)
--- a/testPrograms/testSynapse.m Thu Oct 06 15:43:20 2011 +0100 +++ b/testPrograms/testSynapse.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,12 +1,14 @@ -function testSynapse(BFlist,paramsName, paramChanges) +function testSynapse(BF,paramsName, AN_spikesOrProbability, paramChanges) % testSynapse tracks the quantity of available transmitter vesicles % the computations are single channel using the first frequency % in the targetFrequency box of the expGUI. -% For, speed this function uses only probability and HSR fibers. -% This cannot be changed because of the way AN_IHCsynapse is coded.This). +% This function uses only probability and HSR fibers. +% testSynapse(1000,'Normal',[]) -global experiment method IHCpreSynapseParams +global experiment IHCpreSynapseParams global AN_IHCsynapseParams stimulusParameters +global savePavailable saveNavailable + savePath=path; addpath (['..' filesep 'utilities'],['..' filesep 'MAP']) @@ -14,15 +16,27 @@ paramChanges=[]; end +if length(BF)>1 + error('Only one value allowed for BF') +end +% AN_spikesOrProbability='probability'; +% AN_spikesOrProbability='spikes'; +% showPlotsAndDetails=0; + figure(6),clf plotColors='rbgkrbgkrbgkrbgkrbgkrbgk'; set(gcf,'position',[5 32 264 243]) sampleRate=5e4; dt=1/sampleRate; -maskerLevels=-0:10:100; +switch AN_spikesOrProbability + case 'probability' + maskerLevels=-0:20:100; + case 'spikes' + maskerLevels=80; +end -targetFrequency=BFlist; +targetFrequency=BF; silenceDuration=0.015; maskerDuration=0.1; @@ -51,17 +65,16 @@ amp=28e-6*10^(leveldB/20); inputSignal=amp*signal; - AN_spikesOrProbability='probability'; - showPlotsAndDetails=0; - - global savePavailable - - MAP1_14(inputSignal, 1/dt, targetFrequency, ... - paramsName, AN_spikesOrProbability, paramChanges); + MAP1_14(inputSignal, 1/dt, targetFrequency, ... + paramsName, AN_spikesOrProbability, paramChanges); % ignore LSR channels (if any) at the top of the matrix - qt=savePavailable(end, :); - + switch AN_spikesOrProbability + case 'probability' + qt=savePavailable(end, :); + case 'spikes' + qt=saveNavailable; + end synapsedt=dt; time=synapsedt:synapsedt:synapsedt*length(qt); @@ -71,17 +84,22 @@ hold on xlim([0 max(time)]) ylim([0 AN_IHCsynapseParams.M]) + xlabel ('time') end set(gcf,'name','pre-synaptic available transmitter') -title(['q - available vesicles:' num2str(BFlist) ' Hz']) +title(['pre-synaptic transmitter:' num2str(BF) ' Hz']) +ylabel(['q - available vesicles']) legend(strvcat(num2str(maskerLevels')),'location','southeast') legend boxoff grid on -figure(88), [c,H]=contour(time, maskerLevels,qtMatrix,1:12); clabel(c, H); -set(gcf,'position',[ 276 31 328 246]) -xlabel('time'), ylabel('maskerLevels') -title('contour plot of available transmitter') -grid on +switch AN_spikesOrProbability + case 'probability' + figure(88), [c,H]=contour(time, maskerLevels,qtMatrix,1:12); clabel(c, H); + set(gcf,'position',[ 276 31 328 246]) + xlabel('time'), ylabel('maskerLevels') + title('contour plot of available transmitter') + grid on +end path(savePath);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/testPrograms/test_DRNL_Ruggero97.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,172 @@ +function test_DRNL_Ruggero97 +% test_DRNL_Ruggero97 attempts to match Ruggero's (1992 and 1997) +% iso-intensity data by fiddling with the parameters + +% # BF is the BF of the filter to be assessed +BF=9000; + +% # test frequencies. check that BF is one of them +% copy Ruggero's test tones as fara as possible +numFs=6; lowestF=4000; highestF= 11030; +toneFrequencyList=round(logspace(log10(lowestF), log10(highestF), numFs)); + +% # parameter file name. this is the base set of parameters +MAPparamsName='Normal'; + +% # probability representation (not directly relevant here as only +% the DRNL output is used +AN_spikesOrProbability='probability'; + +% # tone characteristics +sampleRate= 100000; +duration=0.0200; % Ruggero uses 5, 10, 25 ms tones +rampDuration=0.0015; % raised cosine ramp (seconds) +beginSilence=0.050; +endSilence=0.020; + +% # levels +levels=[3 10:10:80]; + +%% # change model parameters +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read + +paramChanges={}; + +paramChanges={... + 'DRNLParams.ctBMdB = -20;'... + 'DRNLParams.g=1000;'... + }; + +% delare 'showMap' options to control graphical output +% (not needed but might be useful +% showMapOptions.printModelParameters=0; % prints all parameters +% showMapOptions.showModelOutput=1; % plot of all stages +% showMapOptions.printFiringRates=0; % prints stage activity levels +% showMapOptions.showACF=0; % shows SACF (probability only) +% showMapOptions.showEfferent=0; % tracks of AR and MOC +% showMapOptions.surfAN=0; % 2D plot of HSR response +% showMapOptions.surfSpikes=0; % 2D plot of spikes histogram +% showMapOptions.ICrates=0; % IC rates by CNtauGk +% showMapOptions.fileName=[]; + +% run the program +global dt DRNLoutput DRNLParams TMoutput +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) +figure(2), clf,figure(3), clf,figure(4), clf, + +peakOutputDisp=zeros(length(levels),length(toneFrequencyList)); +peakStapesDisp=zeros(length(levels),length(toneFrequencyList)); + +%% now vary level and frequency while measuring the response +levelNo=0; +for leveldBSPL=levels + levelNo=levelNo+1; + disp(['level: ' num2str(leveldBSPL)]) + + freqNo=0; + for toneFrequency=toneFrequencyList + freqNo=freqNo+1; + + %% Generate stimuli + dt=1/sampleRate; + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + %% run the model + + MAP1_14(inputSignal, sampleRate, BF, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + peakOutputDisp(levelNo,freqNo)=max(DRNLoutput); + peakStapesDisp(levelNo,freqNo)=max(TMoutput); + + % the model run is complete. Now display the results if debugging + % UTIL_showMAP(showMapOptions) + end % probe frequencies + + % monitor progress + figure(2), loglog(toneFrequencyList, peakOutputDisp), hold on + xlabel('frequency') + ylabel('peak DRNL displacement (m)') + title ('DRNL uncorrected displacement') +end % levels +figure(2),legend(num2str(toneFrequencyList'),'location','northwest') + +% convert from model BM displacement to Ruggero's velocity +DRNLvelocity= peakOutputDisp ... + .*repmat(2*pi*toneFrequencyList,length(levels),1); +% convert from model stapes displacement to Ruggero's velocity +stapesVelocity= peakStapesDisp ... + .*repmat(2*pi*toneFrequencyList,length(levels),1); +% velocity gain is increased velocity attributable to the DRNL +DRNLvelocityGain = DRNLvelocity./stapesVelocity; +DRNLvelocityGain_dB= 20*log10(DRNLvelocityGain ); +% iso-intensity equates all functions at the same outpu for the lowest +% velocity tested +isoIntensityDRNLvel_dB= DRNLvelocityGain_dB- ... + repmat(DRNLvelocityGain_dB(:,1),1,length(toneFrequencyList)); + +% displays +% iso-velocity function (nrmalised by stapes velocity) +figure(3), clf, semilogx(toneFrequencyList, isoIntensityDRNLvel_dB) +ylim([-10 50]) +xlim([3000 20000]) +xlabel('frequency (Hz)') +set(gca,'Xtick', [1000 ],'xticklabel',{'1000'}) + +ylabel('peak DRNL velocity gain (dB)') +title(['CF= ' num2str(BF) ' Hz']) +legend(num2str(levels'),'location','northwest') + +% velocity I/O function +figure(4), clf, semilogy(levels, (DRNLvelocity*1e6)'), hold on +ylim([5e0 1.2e4]) +xlim([0 80]) +xlabel('level') +ylabel(' velocity (microm/ s)') +title(['CF= ' num2str(BF) ' Hz']) +legend(num2str(toneFrequencyList'),'location','northwest') + +% command window reports +disp(''), disp('***') +disp(AN_spikesOrProbability) + +% DRNL parameter set +UTIL_showStructureSummary(DRNLParams, 'DRNLParams', 10) + +% stimulus characteristics +disp(['CF=' num2str(BF)]) +disp(['tone Duration=' num2str(rampDuration)]) +disp(['ramp Duration=' num2str(duration)]) + +% particular parameter changes used on this run +for i=1:length(paramChanges) + disp(paramChanges{i}) +end + +% if required dump one or more matrices in tab spaced format +% (suitable for pasting directly into EXCEL. +% UTIL_printTabTable([toneFrequencyList' peakOutputDisp']) + +% leave the path as you found it +path(restorePath) + +
--- a/testPrograms/test_MAP1_14.m Thu Oct 06 15:43:20 2011 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,190 +0,0 @@ -function test_MAP1_14 -% test_MAP1_14 is a general purpose test routine that can be adjusted to -% test a number of different applications of MAP1_14 -% -% A range of options are supplied in the early part of the program -% -% One use of the function is to create demonstrations; filenames <demoxx> -% to illustrate particular features -% -% #1 -% Identify the file (in 'MAPparamsName') containing the model parameters -% -% #2 -% Identify the kind of model required (in 'AN_spikesOrProbability'). -% A full brainstem model (spikes) can be computed or a shorter model -% (probability) that computes only so far as the auditory nerve -% -% #3 -% Choose between a tone signal or file input (in 'signalType') -% -% #4 -% Set the signal rms level (in leveldBSPL) -% -% #5 -% Identify the channels in terms of their best frequencies in the vector -% BFlist. -% -% Last minute changes to the parameters fetched earlier can be made using -% the cell array of strings 'paramChanges'. -% Each string must have the same format as the corresponding line in the -% file identified in 'MAPparamsName' -% -% When the demonstration is satisfactory, freeze it by renaming it <demoxx> - -dbstop if error -restorePath=path; -addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... - ['..' filesep 'utilities']) - -%% #1 parameter file name -MAPparamsName='Normal'; - - -%% #2 probability (fast) or spikes (slow) representation -AN_spikesOrProbability='spikes'; -% or -AN_spikesOrProbability='probability'; - - -%% #3 pure tone, harmonic sequence or speech file input -signalType= 'tones'; -sampleRate= 50000; -duration=0.500; % seconds -rampDuration=.005; % raised cosine ramp (seconds) -beginSilence=0.250; -endSilence=0.250; -toneFrequency= 1000; % or a pure tone (Hz) - -% or -% harmonic sequence (Hz) -% F0=210; -% toneFrequency= F0:F0:8000; - -% or -signalType= 'file'; -fileName='twister_44kHz'; - - - -%% #4 rms level -% signal details -leveldBSPL= 70; % dB SPL (80 for Lieberman) - - -%% #5 number of channels in the model -% 21-channel model (log spacing) -numChannels=21; -lowestBF=250; highestBF= 6000; -BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); - -% or specify your own channel BFs -% numChannels=1; -% BFlist=toneFrequency; - - -%% #6 change model parameters - -paramChanges={}; - -% Parameter changes can be used to change one or more model parameters -% *after* the MAPparams file has been read -% This example declares only one fiber type with a calcium clearance time -% constant of 80e-6 s (HSR fiber) when the probability option is selected. -paramChanges={'AN_IHCsynapseParams.ANspeedUpFactor=5;', ... - 'IHCpreSynapseParams.tauCa=86e-6; '}; - - - -%% delare 'showMap' options to control graphical output -showMapOptions.printModelParameters=1; % prints all parameters -showMapOptions.showModelOutput=1; % plot of all stages -showMapOptions.printFiringRates=1; % prints stage activity levels -showMapOptions.showACF=0; % shows SACF (probability only) -showMapOptions.showEfferent=1; % tracks of AR and MOC -showMapOptions.surfProbability=1; % 2D plot of HSR response -showMapOptions.surfSpikes=1; % 2D plot of spikes histogram -showMapOptions.ICrates=0; % IC rates by CNtauGk - -% disable certain silly options -if strcmp(AN_spikesOrProbability, 'spikes') - % avoid nonsensical options - showMapOptions.surfProbability=0; - showMapOptions.showACF=0; -end - -if strcmp(signalType, 'file') - % needed for labeling plot - showMapOptions.fileName=fileName; -else - showMapOptions.fileName=[]; -end - -%% Generate stimuli - -switch signalType - case 'tones' - % Create pure tone stimulus - dt=1/sampleRate; % seconds - time=dt: dt: duration; - inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); - amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) - inputSignal=amp*inputSignal; - % apply ramps - % catch rampTime error - if rampDuration>0.5*duration, rampDuration=duration/2; end - rampTime=dt:dt:rampDuration; - ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... - ones(1,length(time)-length(rampTime))]; - inputSignal=inputSignal.*ramp; - ramp=fliplr(ramp); - inputSignal=inputSignal.*ramp; - % add silence - intialSilence= zeros(1,round(beginSilence/dt)); - finalSilence= zeros(1,round(endSilence/dt)); - inputSignal= [intialSilence inputSignal finalSilence]; - - case 'file' - %% file input simple or mixed - [inputSignal sampleRate]=wavread(fileName); - dt=1/sampleRate; - inputSignal=inputSignal(:,1); - targetRMS=20e-6*10^(leveldBSPL/20); - rms=(mean(inputSignal.^2))^0.5; - amp=targetRMS/rms; - inputSignal=inputSignal*amp; - intialSilence= zeros(1,round(0.1/dt)); - finalSilence= zeros(1,round(0.2/dt)); - inputSignal= [intialSilence inputSignal' finalSilence]; -end - - -%% run the model -tic -fprintf('\n') -disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) -disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) -disp('Computing ...') - -MAP1_14(inputSignal, sampleRate, BFlist, ... - MAPparamsName, AN_spikesOrProbability, paramChanges); - - -%% the model run is now complete. Now display the results -UTIL_showMAP(showMapOptions, paramChanges) - -if strcmp(signalType,'tones') - disp(['duration=' num2str(duration)]) - disp(['level=' num2str(leveldBSPL)]) - disp(['toneFrequency=' num2str(toneFrequency)]) - global DRNLParams - disp(['attenuation factor =' ... - num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) - disp(['attenuation factor (probability)=' ... - num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) - disp(AN_spikesOrProbability) -end -disp(paramChanges) -toc -path(restorePath) -
--- a/testPrograms/test_speechInNoise.m Thu Oct 06 15:43:20 2011 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,215 +0,0 @@ -function test_speechInNoise -% test_MAP1_14 is a general purpose test routine that can be adjusted to -% test a number of different applications of MAP1_14 -% -% A range of options are supplied in the early part of the program -% -% One use of the function is to create demonstrations; filenames <demoxx> -% to illustrate particular features -% -% #1 -% Identify the file (in 'MAPparamsName') containing the model parameters -% -% #2 -% Identify the kind of model required (in 'AN_spikesOrProbability'). -% A full brainstem model (spikes) can be computed or a shorter model -% (probability) that computes only so far as the auditory nerve -% -% #3 -% Choose between a tone signal or file input (in 'signalType') -% -% #4 -% Set the signal rms level (in leveldBSPL) -% -% #5 -% Identify the channels in terms of their best frequencies in the vector -% BFlist. -% -% Last minute changes to the parameters fetched earlier can be made using -% the cell array of strings 'paramChanges'. -% Each string must have the same format as the corresponding line in the -% file identified in 'MAPparamsName' -% -% When the demonstration is satisfactory, freeze it by renaming it <demoxx> - -dbstop if error -restorePath=path; -addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... - ['..' filesep 'utilities']) - -%% #1 parameter file name -MAPparamsName='Normal'; - - -%% #2 probability (fast) or spikes (slow) representation -AN_spikesOrProbability='spikes'; -% or -% AN_spikesOrProbability='probability'; - - -%% #3 pure tone, harmonic sequence or speech file input -% signalType= 'tones'; -% sampleRate= 50000; -% toneFrequency= 1000; % or a pure tone (Hz8 -% duration=.5; % seconds -% beginSilence=.2; -% endSilence=0.5; - -% F0=210; -% toneFrequency= F0:F0:8000; % harmonic sequence (Hz) - -rampDuration=.005; % raised cosine ramp (seconds) - -% or -signalType= 'file'; -fileName='twister_44kHz'; -beginSilence=1; -endSilence=0.5; - -%% #4 rms level -% signal details -leveldBSPL= 60; % dB SPL - -% leveldBSPLNoise=leveldBSPL; -leveldBSPLNoise=0; - -%% #5 number of channels in the model -% 21-channel model (log spacing) -numChannels=21; -lowestBF=300; highestBF= 6000; -BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); - -% or specify your own channel BFs -% numChannels=1; -% BFlist=toneFrequency; - - -%% #6 change model parameters -% Parameter changes can be used to change one or more model parameters -% *after* the MAPparams file has been read -% This example declares only one fiber type with a calcium clearance time -% constant of 80e-6 s (HSR fiber) when the probability option is selected. -% paramChanges={'AN_IHCsynapseParams.ANspeedUpFactor=5;', ... -% 'IHCpreSynapseParams.tauCa=86e-6;'}; - -% paramChanges={}; - -paramChanges{1}='DRNLParams.MOCtau =.3;'; -paramChanges{2}='IHCpreSynapseParams.tauCa= IHCpreSynapseParams.tauCa(2);'; -paramChanges{3}='DRNLParams.rateToAttenuationFactorProb = 0.05;'; -paramChanges{3}='DRNLParams.rateToAttenuationFactorProb = 0;'; - -% paramChanges={... -% 'DRNLParams.MOCtau =.055;DRNLParams.rateToAttenuationFactor = 002;'}; -% paramChanges=... -% {'DRNLParams.MOCtau =.3; DRNLParams.rateToAttenuationFactor=0.0123;'}; - -%% delare 'showMap' options to control graphical output -showMapOptions.printModelParameters=1; % prints all parameters -showMapOptions.showModelOutput=1; % plot of all stages -showMapOptions.printFiringRates=1; % prints stage activity levels -showMapOptions.showACF=0; % shows SACF (probability only) -showMapOptions.showEfferent=1; % tracks of AR and MOC -showMapOptions.surfProbability=0; % 2D plot of HSR response -showMapOptions.surfSpikes=0; % 2D plot of spikes histogram -showMapOptions.ICrates=0; % IC rates by CNtauGk - -% disable certain silly options -if strcmp(AN_spikesOrProbability, 'spikes') - % avoid nonsensical options - showMapOptions.surfProbability=0; - showMapOptions.showACF=0; -else - showMapOptions.surfSpikes=0; -end -if strcmp(signalType, 'file') - % needed for labeling plot - showMapOptions.fileName=fileName; -else - showMapOptions.fileName=[]; -end - -%% Generate stimuli - -switch signalType - case 'tones' - % inputSignal=createMultiTone(sampleRate, toneFrequency, ... - % leveldBSPL, duration, rampDuration); - % Create pure tone stimulus - dt=1/sampleRate; % seconds - time=dt: dt: duration; - inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); - amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) - inputSignal=amp*inputSignal; - - % apply ramps - % catch rampTime error - if rampDuration>0.5*duration, rampDuration=duration/2; end - rampTime=dt:dt:rampDuration; - ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... - ones(1,length(time)-length(rampTime))]; - inputSignal=inputSignal.*ramp; - ramp=fliplr(ramp); - inputSignal=inputSignal.*ramp; - - % add silences - intialSilence= zeros(1,round(beginSilence/dt)); - finalSilence= zeros(1,round(endSilence/dt)); - inputSignal= [intialSilence inputSignal finalSilence]; - - case 'file' - %% file input simple or mixed - [inputSignal sampleRate]=wavread(fileName); - dt=1/sampleRate; - inputSignal=inputSignal(:,1); - targetRMS=20e-6*10^(leveldBSPL/20); - rms=(mean(inputSignal.^2))^0.5; - amp=targetRMS/rms; - inputSignal=inputSignal*amp; - - % add silences - intialSilence= zeros(1,round(beginSilence*sampleRate)); - finalSilence= zeros(1,round(endSilence*sampleRate)); - inputSignal= [intialSilence inputSignal' finalSilence]; - - [inputNoise sampleRateN]=wavread('babble'); - inputNoise=inputNoise(1:length(inputSignal)); - inputNoise=inputNoise(:,1); - targetRMS=20e-6*10^(leveldBSPLNoise/20); - rms=(mean(inputNoise.^2))^0.5; - amp=targetRMS/rms; - inputNoise=inputNoise*amp; - inputSignal=inputSignal+inputNoise'; - -end - - -%% run the model -tic - -fprintf('\n') -disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) -disp([num2str(numChannels) ' channel model']) -disp('Computing ...') - -MAP1_14(inputSignal, sampleRate, BFlist, ... - MAPparamsName, AN_spikesOrProbability, paramChanges); - - -%% the model run is now complete. Now display the results -UTIL_showMAP(showMapOptions, paramChanges) -disp(['duration=' num2str(duration)]) -disp(['level=' num2str(leveldBSPL)]) -disp(['noise level=' num2str(leveldBSPLNoise)]) - -disp(['toneFrequency=' num2str(toneFrequency)]) -global DRNLParams -disp(['attenuation factor =' ... - num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) -disp(['attenuation factor (probability)=' ... - num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) -disp(AN_spikesOrProbability) -disp(paramChanges) -toc -path(restorePath) -
--- a/userPrograms/Pavel_MAP1_14.m Thu Oct 06 15:43:20 2011 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,189 +0,0 @@ -function Pavel_MAP1_14 -% test_MAP1_14 is a general purpose test routine that can be adjusted to -% test a number of different applications of MAP1_14 -% -% A range of options are supplied in the early part of the program -% -% One use of the function is to create demonstrations; filenames <demoxx> -% to illustrate particular features -% -% #1 -% Identify the file (in 'MAPparamsName') containing the model parameters -% -% #2 -% Identify the kind of model required (in 'AN_spikesOrProbability'). -% A full brainstem model (spikes) can be computed or a shorter model -% (probability) that computes only so far as the auditory nerve -% -% #3 -% Choose between a tone signal or file input (in 'signalType') -% -% #4 -% Set the signal rms level (in leveldBSPL) -% -% #5 -% Identify the channels in terms of their best frequencies in the vector -% BFlist. -% -% Last minute changes to the parameters fetched earlier can be made using -% the cell array of strings 'paramChanges'. -% Each string must have the same format as the corresponding line in the -% file identified in 'MAPparamsName' -% -% When the demonstration is satisfactory, freeze it by renaming it <demoxx> - -restorePath=path; -addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... - ['..' filesep 'utilities']) - -%% #1 parameter file name -MAPparamsName='Normal'; - - -%% #2 probability (fast) or spikes (slow) representation -AN_spikesOrProbability='spikes'; - -% or -% AN_spikesOrProbability='probability'; -% NB probabilities are not corrected for refractory effects - - -%% #3 pure tone, harmonic sequence or speech file input -signalType= 'tones'; -sampleRate= 100000; -duration=0.1; % seconds -% toneFrequency= 250:250:8000; % harmonic sequence (Hz) -toneFrequency= 1000; % or a pure tone (Hz8 -rampDuration=.005; % seconds - -% or -% signalType= 'file'; -% fileName='twister_44kHz'; - - -%% #4 rms level -% signal details -leveldBSPL= 30; % dB SPL - - -%% #5 number of channels in the model -% 21-channel model (log spacing) -% numChannels=21; -% lowestBF=250; highestBF= 8000; -% BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); - -% or specify your own channel BFs -numChannels=1; -BFlist=toneFrequency; - - -%% #6 change model parameters -paramChanges=[]; - -% or -% Parameter changes can be used to change one or more model parameters -% *after* the MAPparams file has been read -% This example declares only one fiber type with a calcium clearance time -% constant of 80e-6 s (HSR fiber) when the probability option is selected. -% It also removes the speed up that normally takes place for AN spikes -% It also increases the number of AN fibers computed to 500. -paramChanges={... - 'AN_IHCsynapseParams.ANspeedUpFactor=1;', ... - 'IHCpreSynapseParams.tauCa=86e-6;',... - 'AN_IHCsynapseParams.numFibers= 500;' }; - - -%% delare 'showMap' options to control graphical output -global showMapOptions - -% or (example: show everything including an smoothed SACF output -showMapOptions.printModelParameters=1; % prints all parameters -showMapOptions.showModelOutput=1; % plot of all stages -showMapOptions.printFiringRates=1; % prints stage activity levels -showMapOptions.showACF=0; % shows SACF (probability only) -showMapOptions.showEfferent=0; % tracks of AR and MOC -showMapOptions.surfProbability=0; % 2D plot of HSR response -if strcmp(AN_spikesOrProbability, 'spikes') - % avoid nonsensical options - showMapOptions.surfProbability=0; - showMapOptions.showACF=0; -end -if strcmp(signalType, 'file') - % needed for labeling plot - showMapOptions.fileName=fileName; -else - showMapOptions.fileName=[]; -end - -%% Generate stimuli - -dbstop if error -restorePath=path; -addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore']) -switch signalType - case 'tones' - inputSignal=createMultiTone(sampleRate, toneFrequency, ... - leveldBSPL, duration, rampDuration); - - case 'file' - %% file input simple or mixed - [inputSignal sampleRate]=wavread(fileName); - dt=1/sampleRate; - inputSignal=inputSignal(:,1); - targetRMS=20e-6*10^(leveldBSPL/20); - rms=(mean(inputSignal.^2))^0.5; - amp=targetRMS/rms; - inputSignal=inputSignal*amp; - silence= zeros(1,round(0.1/dt)); - inputSignal= [silence inputSignal' silence]; -end - - -%% run the model -tic - -fprintf('\n') -disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) -disp([num2str(numChannels) ' channel model']) -disp('Computing ...') - - -MAP1_14(inputSignal, sampleRate, BFlist, ... - MAPparamsName, AN_spikesOrProbability, paramChanges); -toc - -% the model run is now complete. Now display the results -% the model run is now complete. Now display the results -disp(' param changes to list of parameters below') -for i=1:length(paramChanges) - disp(paramChanges{i}) -end -UTIL_showMAP(showMapOptions) - -toc -path(restorePath) - - -function inputSignal=createMultiTone(sampleRate, toneFrequency, ... - leveldBSPL, duration, rampDuration) -% Create pure tone stimulus -dt=1/sampleRate; % seconds -time=dt: dt: duration; -inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); -amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) -inputSignal=amp*inputSignal; - -% apply ramps -% catch rampTime error -if rampDuration>0.5*duration, rampDuration=duration/2; end -rampTime=dt:dt:rampDuration; -ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... - ones(1,length(time)-length(rampTime))]; -inputSignal=inputSignal.*ramp; -ramp=fliplr(ramp); -inputSignal=inputSignal.*ramp; - -% add 10 ms silence -silence= zeros(1,round(0.03/dt)); -inputSignal= [silence inputSignal silence]; -
--- a/userPrograms/pitchModel_RM.m Thu Oct 06 15:43:20 2011 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,242 +0,0 @@ -function pitchModel_RM -% Modification of testMAP_14 to replicate the pitch model published -% in JASA 2006. -% -% test_MAP1_14 is a general purpose test routine that can be adjusted to -% test a number of different applications of MAP1_14 -% -% A range of options are supplied in the early part of the program -% -% One use of the function is to create demonstrations; filenames <demoxx> -% to illustrate particular features -% -% #1 -% Identify the file (in 'MAPparamsName') containing the model parameters -% -% #2 -% Identify the kind of model required (in 'AN_spikesOrProbability'). -% A full brainstem model (spikes) can be computed or a shorter model -% (probability) that computes only so far as the auditory nerve -% -% #3 -% Choose between a tone signal or file input (in 'signalType') -% -% #4 -% Set the signal rms level (in leveldBSPL) -% -% #5 -% Identify the channels in terms of their best frequencies in the vector -% BFlist. -% -% Last minute changes to the parameters fetched earlier can be made using -% the cell array of strings 'paramChanges'. -% Each string must have the same format as the corresponding line in the -% file identified in 'MAPparamsName' -% -% When the demonstration is satisfactory, freeze it by renaming it <demoxx> - -dbstop if error -restorePath=path; -addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... - ['..' filesep 'utilities']) - -% Pitch model modification here -global ICrate % used to collect rate profile from showMAP temporary -rates=[]; F0count=0; - -% F0s=[150 200 250]; % fundamental frequency -% harmonics= 3:5; - -% F0s=[3000]; % fundamental frequency -F0s=50:5:1000; -harmonics= 1; -% F0s=150; -for F0=F0s - F0count=F0count+1; - - -%% #1 parameter file name -MAPparamsName='Normal'; - - -%% #2 probability (fast) or spikes (slow) representation -AN_spikesOrProbability='spikes'; - -% or -% NB probabilities are not corrected for refractory effects -% AN_spikesOrProbability='probability'; - - -%% #3 pure tone, harmonic sequence or speech file input -signalType= 'tones'; -sampleRate= 50000; -duration=0.50; % seconds -% toneFrequency= 1000; % or a pure tone (Hz8 - -% F0=210; -toneFrequency= F0*harmonics; % harmonic sequence (Hz) - -rampDuration=.005; % raised cosine ramp (seconds) - -% or - -% signalType= 'file'; -% fileName='twister_44kHz'; - - -%% #4 rms level -% signal details -leveldBSPL= 50; % dB SPL - - -%% #5 number of channels in the model -% 21-channel model (log spacing) -numChannels=21; -lowestBF=250; highestBF= 8000; -BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); - -% or specify your own channel BFs -% numChannels=1; -BFlist=toneFrequency; -% BFlist=500; - - -%% #6 change model parameters -paramChanges=[]; - -% or -% Parameter changes can be used to change one or more model parameters -% *after* the MAPparams file has been read -% This example declares only one fiber type with a calcium clearance time -% constant of 80e-6 s (HSR fiber) when the probability option is selected. - -% paramChanges={'AN_IHCsynapseParams.ANspeedUpFactor=5;', ... -% 'IHCpreSynapseParams.tauCa=86e-6;'}; - -% paramChanges={'DRNLParams.rateToAttenuationFactorProb = 0;'}; - -% paramChanges={'IHCpreSynapseParams.tauCa=86e-6;', -% 'AN_IHCsynapseParams.numFibers= 1000;'}; - -% fixed MOC attenuation(using negative factor) -% paramChanges={'DRNLParams.rateToAttenuationFactorProb=-0.005;'}; - -% slow the CN chopping rate -% paramChanges={'IHCpreSynapseParams.tauCa= 70e-6;'...' -% 'MacGregorMultiParams.tauGk= [0.75e-3:.0001 : 3e-3];'... -% ' MacGregorParams.dendriteLPfreq=4000;'... -% 'MacGregorParams.tauGk= 1e-4;'... -% 'MacGregorParams.currentPerSpike=220e-8;'... -% }; -paramChanges={... - 'MacGregorMultiParams.currentPerSpike=25e-9;'... - 'MacGregorMultiParams.tauGk= [0.1e-3:.00005 : 1e-3];'... -'MacGregorParams.currentPerSpike=40e-9;'... -}; - -%% delare 'showMap' options to control graphical output - -showMapOptions.printModelParameters=0; % prints all parameters -showMapOptions.showModelOutput=1; % plot of all stages -showMapOptions.printFiringRates=1; % prints stage activity levels -showMapOptions.showACF=0; % shows SACF (probability only) -showMapOptions.showEfferent=0; % tracks of AR and MOC -showMapOptions.surfProbability=0; % 2D plot of HSR response -showMapOptions.surfSpikes=0; % 2D plot of spikes histogram -showMapOptions.ICrates=1; % IC rates by CNtauGk - -% disable certain silly options -if strcmp(AN_spikesOrProbability, 'spikes') - % avoid nonsensical options - showMapOptions.surfProbability=0; - showMapOptions.showACF=0; -else - showMapOptions.surfSpikes=0; -end -if strcmp(signalType, 'file') - % needed for labeling plot - showMapOptions.fileName=fileName; -else - showMapOptions.fileName=[]; -end - -%% Generate stimuli - -switch signalType - case 'tones' - inputSignal=createMultiTone(sampleRate, toneFrequency, ... - leveldBSPL, duration, rampDuration); - - case 'file' - %% file input simple or mixed - [inputSignal sampleRate]=wavread(fileName); - dt=1/sampleRate; - inputSignal=inputSignal(:,1); - targetRMS=20e-6*10^(leveldBSPL/20); - rms=(mean(inputSignal.^2))^0.5; - amp=targetRMS/rms; - inputSignal=inputSignal*amp; - silence= zeros(1,round(0.1/dt)); - inputSignal= [silence inputSignal' silence]; -end - - -%% run the model -tic - -fprintf('\n') -disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) -disp([num2str(numChannels) ' channel model']) -disp([num2str(F0) ' F0']) -disp('Computing ...') - -MAP1_14(inputSignal, sampleRate, BFlist, ... - MAPparamsName, AN_spikesOrProbability, paramChanges); - - -% the model run is now complete. Now display the results -UTIL_showMAP(showMapOptions, paramChanges) - -%% pitch model Collect and analyse data -% ICrate is global and computed in showMAP -% a vector of 'stage4' rates; one value for each tauCNGk -rates=[rates; ICrate]; -figure(92), imagesc(rates) -ylabel ('F0 no'), xlabel('tauGk') -% figure(92), plot(rates), ylim([0 inf]) - -h=figure(99); CNmovie(F0count)=getframe(h); -figure(91), plot(rates'),ylim([0 inf]) -pause (0.1) -path(restorePath) - -end -%% show results -toc -figure(91), plot(F0s,rates'), xlabel('F0'), ylabel('rate'),ylim([0 inf]) -% figure(99),clf,movie(CNmovie,1,4) - - -function inputSignal=createMultiTone(sampleRate, toneFrequency, ... - leveldBSPL, duration, rampDuration) -% Create pure tone stimulus -dt=1/sampleRate; % seconds -time=dt: dt: duration; -inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); -amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) -inputSignal=amp*inputSignal; - -% apply ramps -% catch rampTime error -if rampDuration>0.5*duration, rampDuration=duration/2; end -rampTime=dt:dt:rampDuration; -ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... - ones(1,length(time)-length(rampTime))]; -inputSignal=inputSignal.*ramp; -ramp=fliplr(ramp); -inputSignal=inputSignal.*ramp; - -% add 10 ms silence -silence= zeros(1,round(0.005/dt)); -inputSignal= [silence inputSignal silence]; -
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userPrograms/runMAP1_14.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,181 @@ +function runMAP1_14 +% runMAP1_14 is a general purpose test routine that can be adjusted to +% test a number of different applications of MAP1_14 +% +% A range of options are supplied in the early part of the program +% +% #1 +% Identify the file (in 'MAPparamsName') containing the model parameters +% +% #2 +% Identify the kind of model required (in 'AN_spikesOrProbability'). +% A full brainstem model ('spikes') can be computed or a shorter model +% ('probability') that computes only so far as the auditory nerve +% +% #3 +% Choose between a tone signal or file input (in 'signalType') +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the channels in terms of their best frequencies in the vector +% BFlist. +% +% Last minute changes to the parameters can be made using +% the cell array of strings 'paramChanges'. +% Each string must have the same format as the corresponding line in the +% file identified in 'MAPparamsName' + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation: select one +% AN_spikesOrProbability='spikes'; +% or +AN_spikesOrProbability='probability'; + + +%% #3 A. pure tone, B. harmonic sequence or C. speech file input +% comment out unwanted code + +% A. tone +sampleRate= 441000; +signalType= 'tones'; +toneFrequency= 5000; % or a pure tone (Hz) +duration=0.500; % seconds +beginSilence=0.050; +endSilence=0.050; +rampDuration=.005; % raised cosine ramp (seconds) + +% or +% B. harmonic tone (Hz) - useful to demonstrate a broadband sound +% sampleRate= 44100; +% signalType= 'tones'; +% toneFrequency= F0:F0:8000; +% duration=0.500; % seconds +% beginSilence=0.250; +% endSilence=0.250; +% F0=210; +% rampDuration=.005; % raised cosine ramp (seconds) + +% or +% C. signalType= 'file'; +% fileName='twister_44kHz'; + +%% #4 rms level +% signal details +leveldBSPL= 70; % dB SPL (80 for Lieberman) + +%% #5 number of channels in the model +% 21-channel model (log spacing) +numChannels=21; +lowestBF=250; highestBF= 6000; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +% or specify your own channel BFs +% numChannels=1; +% BFlist=toneFrequency; + + +%% #6 change model parameters + +paramChanges={}; + +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read +% This example declares only one fiber type with a calcium clearance time +% constant of 80e-6 s (HSR fiber) when the probability option is selected. +% paramChanges={'AN_IHCsynapseParams.ANspeedUpFactor=5;', ... +% 'IHCpreSynapseParams.tauCa=86e-6; '}; + + + +%% delare 'showMap' options to control graphical output +% see UTIL_showMAP for more options +showMapOptions.printModelParameters=1; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showEfferent=1; % tracks of AR and MOC +showMapOptions.surfProbability=1; % 2D plot of HSR response + +if strcmp(signalType, 'file') + % needed for labeling plot + showMapOptions.fileName=fileName; +else + showMapOptions.fileName=[]; +end + +%% Generate stimuli +switch signalType + case 'tones' + % Create pure tone stimulus + dt=1/sampleRate; % seconds + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + % catch rampTime error + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + intialSilence= zeros(1,round(0.1/dt)); + finalSilence= zeros(1,round(0.2/dt)); + inputSignal= [intialSilence inputSignal' finalSilence]; +end + + +%% run the model +tic +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) +disp('Computing ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +%% the model run is now complete. Now display the results +UTIL_showMAP(showMapOptions, paramChanges) + +if strcmp(signalType,'tones') + disp(['duration=' num2str(duration)]) + disp(['level=' num2str(leveldBSPL)]) + disp(['toneFrequency=' num2str(toneFrequency)]) + global DRNLParams + disp(['attenuation factor =' ... + num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) + disp(['attenuation factor (probability)=' ... + num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) + disp(AN_spikesOrProbability) +end +disp(paramChanges) +toc +path(restorePath) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/Exp_Ray_1.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,167 @@ +function Exp_Ray_1(isMasterNode) + +% Some description of the experiment goes here + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Set up the basic folders +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +expName = 'scrap6_with_eff'; +dataFolderPrefix = 'featR'; +if isunix + expFolderPrefix = '/scratch/nrclark/exps/'; +else + expFolderPrefix = 'D:\Exps'; +end + +expFolder = fullfile(expFolderPrefix,expName); +hmmFolder = fullfile(expFolder,'hmm'); + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Sort out the training (LEARNING) condition +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +learnFolder = fullfile(expFolder,'featL'); + +xL = cJob('L', learnFolder); + +xL.participant = 'Normal'; +xL.MAPparamChanges= {'DRNLParams.rateToAttenuationFactorProb=0;', 'OMEParams.rateToAttenuationFactorProb=0;' }; +xL.MAPparamChanges= {}; + +xL.noiseLevToUse = -200; +xL.noiseLevToUse = 20; +xL.speechLevToUse = 50; + +xL.MAPopHSR = 1; +xL.MAPopMSR = 0; +xL.MAPopLSR = 0; + + +xL.numCoeff = 14; +xL.removeEnergyStatic = 0; + +%%%%% Group of params that will influence simulation run time %%%%%%% +xL.numWavs = 512; %MAX=8440 +testWavs = 48; %MAX = 358 +nzLevel = [-200 40 60]; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +xL.noisePreDur = 1; +xL.noisePostDur = 0.1; +xL.truncateDur = xL.noisePreDur-0.1; + +xL.noiseName = '20TalkerBabble'; + +if isMasterNode && ~isdir(xL.opFolder) + mkdir(xL.opFolder); + xL = xL.assignFiles; + xL.storeSelf; % This is a call to a member function and is not a pointless line of code! +end + + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Sort out the testing (RECOGNITION) conditions +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +recConditions = numel(nzLevel); + +tmpIdx=0; +for nn = 0*recConditions+1:1*recConditions + tmpIdx=tmpIdx+1; + xR{nn} = xL; %simply copy the "Learn" object and change it a bit below + recFolder = fullfile(expFolder,[dataFolderPrefix num2str(nn)]); + xR{nn}.opFolder = recFolder; + + %These are the interesting differences between training and testing + xR{nn}.numWavs = testWavs; %MAX = 358 + xR{nn}.noiseLevToUse = nzLevel(tmpIdx); + %xR{nn}.MAPparamChanges= {'DRNLParams.rateToAttenuationFactorProb=0;'}; + + %Now just to wrap it up ready for processing + if isMasterNode && ~isdir(xR{nn}.opFolder) + mkdir(xR{nn}.opFolder); + xR{nn} = xR{nn}.assignWavPaths('R'); + xR{nn} = xR{nn}.assignFiles; + xR{nn}.storeSelf; + end +end + +% tmpIdx=0; +% for nn = 1*recConditions+1:2*recConditions +% tmpIdx=tmpIdx+1; +% xR{nn} = xL; %simply copy the "Learn" object and change it a bit below +% recFolder = fullfile(expFolder,[dataFolderPrefix num2str(nn)]); +% xR{nn}.opFolder = recFolder; +% +% %These are the interesting differences between training and testing +% xR{nn}.numWavs = testWavs; %MAX = 358 +% xR{nn}.noiseLevToUse = nzLevel(tmpIdx); +% xR{nn}.MAPparamChanges= {'DRNLParams.rateToAttenuationFactorProb=-10^(-10/20);'}; +% +% +% %Now just to wrap it up ready for processing +% if isMasterNode && ~isdir(xR{nn}.opFolder) +% mkdir(xR{nn}.opFolder); +% xR{nn} = xR{nn}.assignWavPaths('R'); +% xR{nn} = xR{nn}.assignFiles; +% xR{nn}.storeSelf; +% end +% end + + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ** Generate features ** +% This is the time consuming, processing intensive portion of the program. +% Nodes that are not the master node are only interested in the opFolder +% member of the jobjects. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +worker(xL.opFolder); +maxConds = nn; +if ~isMasterNode %dont bother wasting master node effort on generating testing features (for now) + for nn = 1:maxConds + worker(xR{nn}.opFolder); + end +end + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Train and test the recogniser - a job for the master node only +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +if isMasterNode + while(~all(xL.todoStatus==2)) + disp('Waiting on straggler nodes to complete their jobs before HMM is trained . . .') + pause(30); %Wait for 30 seconds before looking again + xL.lockJobList; + xL = xL.loadSelf; %Reload incase changed + xL.unlockJobList; + end + y = cHMM(hmmFolder); + y.numCoeff = (xL.numCoeff-logical(xL.removeEnergyStatic)) * 3; + y.createSCP(xL.opFolder) + y.createMLF(xL.opFolder) + y.train(xL.opFolder) %This node can be busy training, even if other jobs are being processed for testing + + % ALLOW MASTER NODE TO MUCK IN WITH GENERATING TESTING FEATURES ONCE + % HMM HAS BEEN TRAINED + for nn = 1:maxConds + worker(xR{nn}.opFolder); + end + + xR{end}.lockJobList; + xR{end} = xR{end}.loadSelf; %Reload changes + xR{end}.unlockJobList; + while(~all(xR{end}.todoStatus==2)) + disp('Waiting on straggler nodes to complete their jobs before HMM is tested . . .') + pause(30); %Wait for 30 seconds before looking again + xR{end}.lockJobList; + xR{end} = xR{end}.loadSelf; %Reload incase changed + xR{end}.unlockJobList; + end + + for nn = 1:maxConds + y.createSCP(xR{nn}.opFolder); + y.test(xR{nn}.opFolder); + end + + %Show all of the scores in the command window at the end + for nn = 1:maxConds + y.score(xR{nn}.opFolder); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/Exp_Tutorial_1.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,165 @@ +function Exp_Tutorial_1(isMasterNode) + +% Some description of the experiment goes here + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Set up the basic folders +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +expName = 'Tutorial'; +dataFolderPrefix = 'featR'; +if isunix + expFolderPrefix = '/scratch/nrclark/exps/'; +else + expFolderPrefix = 'D:\Exps'; +end + +expFolder = fullfile(expFolderPrefix,expName); +hmmFolder = fullfile(expFolder,'hmm'); + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Sort out the training (LEARNING) condition +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +learnFolder = fullfile(expFolder,'featL'); + +xL = cJob('L', learnFolder); + +xL.participant = 'Normal'; +xL.MAPparamChanges= {'DRNLParams.rateToAttenuationFactorProb=0;', 'OMEParams.rateToAttenuationFactorProb=0;' }; + +xL.noiseLevToUse = -200; +xL.speechLevToUse = 60; + +xL.MAPopHSR = 1; +xL.MAPopMSR = 0; +xL.MAPopLSR = 0; + + +xL.numCoeff = 14; +xL.removeEnergyStatic = 0; + +%%%%% Group of params that will influence simulation run time %%%%%%% +xL.numWavs = 12; %MAX=8440 +testWavs = 6; %MAX = 358 +nzLevel = [-200 40:10:70]; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +xL.noisePreDur = 1; +xL.noisePostDur = 0.1; +xL.truncateDur = xL.noisePreDur-0.1; + +xL.noiseName = 'pink_demo'; + +if isMasterNode && ~isdir(xL.opFolder) + mkdir(xL.opFolder); + xL = xL.assignFiles; + xL.storeSelf; % This is a call to a member function and is not a pointless line of code! +end + + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Sort out the testing (RECOGNITION) conditions +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +recConditions = numel(nzLevel); + +tmpIdx=0; +for nn = 0*recConditions+1:1*recConditions + tmpIdx=tmpIdx+1; + xR{nn} = xL; %simply copy the "Learn" object and change it a bit below + recFolder = fullfile(expFolder,[dataFolderPrefix num2str(nn)]); + xR{nn}.opFolder = recFolder; + + %These are the interesting differences between training and testing + xR{nn}.numWavs = testWavs; %MAX = 358 + xR{nn}.noiseLevToUse = nzLevel(tmpIdx); + xR{nn}.MAPparamChanges= {'DRNLParams.rateToAttenuationFactorProb=0;'}; + + %Now just to wrap it up ready for processing + if isMasterNode && ~isdir(xR{nn}.opFolder) + mkdir(xR{nn}.opFolder); + xR{nn} = xR{nn}.assignWavPaths('R'); + xR{nn} = xR{nn}.assignFiles; + xR{nn}.storeSelf; + end +end + +tmpIdx=0; +for nn = 1*recConditions+1:2*recConditions + tmpIdx=tmpIdx+1; + xR{nn} = xL; %simply copy the "Learn" object and change it a bit below + recFolder = fullfile(expFolder,[dataFolderPrefix num2str(nn)]); + xR{nn}.opFolder = recFolder; + + %These are the interesting differences between training and testing + xR{nn}.numWavs = testWavs; %MAX = 358 + xR{nn}.noiseLevToUse = nzLevel(tmpIdx); + xR{nn}.MAPparamChanges= {'DRNLParams.rateToAttenuationFactorProb=-10^(-10/20);'}; + + + %Now just to wrap it up ready for processing + if isMasterNode && ~isdir(xR{nn}.opFolder) + mkdir(xR{nn}.opFolder); + xR{nn} = xR{nn}.assignWavPaths('R'); + xR{nn} = xR{nn}.assignFiles; + xR{nn}.storeSelf; + end +end + + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ** Generate features ** +% This is the time consuming, processing intensive portion of the program. +% Nodes that are not the master node are only interested in the opFolder +% member of the jobjects. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +worker(xL.opFolder); +maxConds = nn; +if ~isMasterNode %dont bother wasting master node effort on generating testing features (for now) + for nn = 1:maxConds + worker(xR{nn}.opFolder); + end +end + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Train and test the recogniser - a job for the master node only +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +if isMasterNode + while(~all(xL.todoStatus==2)) + disp('Waiting on straggler nodes to complete their jobs before HMM is trained . . .') + pause(30); %Wait for 30 seconds before looking again + xL.lockJobList; + xL = xL.loadSelf; %Reload incase changed + xL.unlockJobList; + end + y = cHMM(hmmFolder); + y.numCoeff = (xL.numCoeff-logical(xL.removeEnergyStatic)) * 3; + y.createSCP(xL.opFolder) + y.createMLF(xL.opFolder) + y.train(xL.opFolder) %This node can be busy training, even if other jobs are being processed for testing + + % ALLOW MASTER NODE TO MUCK IN WITH GENERATING TESTING FEATURES ONCE + % HMM HAS BEEN TRAINED + for nn = 1:maxConds + worker(xR{nn}.opFolder); + end + + xR{end}.lockJobList; + xR{end} = xR{end}.loadSelf; %Reload changes + xR{end}.unlockJobList; + while(~all(xR{end}.todoStatus==2)) + disp('Waiting on straggler nodes to complete their jobs before HMM is tested . . .') + pause(30); %Wait for 30 seconds before looking again + xR{end}.lockJobList; + xR{end} = xR{end}.loadSelf; %Reload incase changed + xR{end}.unlockJobList; + end + + for nn = 1:maxConds + y.createSCP(xR{nn}.opFolder); + y.test(xR{nn}.opFolder); + end + + %Show all of the scores in the command window at the end + for nn = 1:maxConds + y.score(xR{nn}.opFolder); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/Exp_Tutorial_2.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,173 @@ +function Exp_Tutorial_2(isMasterNode) + +% This tutorial recycles a HMM + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Set up the basic experiment parameters +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +expName = 'Tutorial'; +dataFolderPrefix = 'recycle_featR'; +if isunix + expFolderPrefix = '/scratch/nrclark/exps/'; +else + expFolderPrefix = 'D:\Exps'; +end + +% expFolderPrefix = pwd; +expFolder = fullfile(expFolderPrefix,expName); +hmmFolder = fullfile(expFolder,'hmm'); + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Sort out the training (LEARNING) condition +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +learnFolder = fullfile(expFolder,'featL'); + +xL = cJob('L', learnFolder); + +xL.participant = 'Normal'; +xL.MAPparamChanges= {'DRNLParams.rateToAttenuationFactorProb=0;', 'OMEParams.rateToAttenuationFactorProb=0;' }; + +xL.noiseLevToUse = -200; +xL.speechLevToUse = 60; + +xL.MAPopHSR = 1; +xL.MAPopMSR = 0; +xL.MAPopLSR = 0; + + +xL.numCoeff = 14; +xL.removeEnergyStatic = 0; + +%%%%% Group of params that will influence simulation run time %%%%%%% +xL.numWavs = 12; %MAX=8440 +testWavs = 6; %MAX = 358 +nzLevel = [-200 40:10:70]; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +xL.noisePreDur = 1; +xL.noisePostDur = 0.1; +xL.truncateDur = xL.noisePreDur-0.1; +xL.noiseName = 'pink_demo'; + + +% if isMasterNode && ~isdir(xL.opFolder) +% mkdir(xL.opFolder); +% xL = xL.assignFiles; +% xL.storeSelf; +% end + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Sort out the testing (RECOGNITION) conditions +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +recConditions = numel(nzLevel); + +tmpIdx=0; +for nn = 0*recConditions+1:1*recConditions + tmpIdx=tmpIdx+1; + xR{nn} = xL; %simply copy the "Learn" object and change it a bit below + recFolder = fullfile(expFolder,[dataFolderPrefix num2str(nn)]); + xR{nn}.opFolder = recFolder; + + %These are the interesting differences between training and testing + xR{nn}.numWavs = testWavs; %MAX = 358 + xR{nn}.noiseLevToUse = nzLevel(tmpIdx); + xR{nn}.MAPparamChanges= {'DRNLParams.a=400;'}; + + + %Now just to wrap it up ready for processing + if isMasterNode && ~isdir(xR{nn}.opFolder) + mkdir(xR{nn}.opFolder); + xR{nn} = xR{nn}.assignWavPaths('R'); + xR{nn} = xR{nn}.assignFiles; + xR{nn}.storeSelf; + end +end + +tmpIdx=0; +for nn = 1*recConditions+1:2*recConditions + tmpIdx=tmpIdx+1; + xR{nn} = xL; %simply copy the "Learn" object and change it a bit below + recFolder = fullfile(expFolder,[dataFolderPrefix num2str(nn)]); + xR{nn}.opFolder = recFolder; + + %These are the interesting differences between training and testing + xR{nn}.numWavs = testWavs; %MAX = 358 + xR{nn}.noiseLevToUse = nzLevel(tmpIdx); + xR{nn}.MAPparamChanges= {'DRNLParams.a=400;'}; + + xR{nn}.mainGain = [27.2013; 26.0797; 26.0939; 26.7997; 26.0520]; + xR{nn}.TCdBO = [37; 37; 37; 37; 37]; %Compression thresholds (in dB OUTPUT from 2nd filt) + xR{nn}.TMdBO = [20; 20; 20; 20; 20]; %MOC thresholds (in dB OUTPUT from 2nd filt) + xR{nn}.ARthresholddB = 85; % dB SPL (input signal level) =>200 to disable + xR{nn}.MOCtau = 1; + xR{nn}.useAid = 1; + + %Now just to wrap it up ready for processing + if isMasterNode && ~isdir(xR{nn}.opFolder) + mkdir(xR{nn}.opFolder); + xR{nn} = xR{nn}.assignWavPaths('R'); + xR{nn} = xR{nn}.assignFiles; + xR{nn}.storeSelf; + end +end + + + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ** Generate features ** +% This is the time consuming, processing intensive portion of the program. +% Nodes that are not the master node are only interested in the opFolder +% member of the jobjects. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% worker(xL.opFolder); +maxConds = nn; +if ~isMasterNode %dont bother wasting master node effort on generating testing features (for now) + for nn = 1:maxConds + worker(xR{nn}.opFolder); + end +end + +%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Train and test the recogniser - a job for the master node only +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +if isMasterNode +% while(~all(xL.todoStatus==2)) +% disp('Waiting on straggler nodes to complete their jobs before HMM is trained . . .') +% pause(30); %Wait for 30 seconds before looking again +% xL.lockJobList; +% xL = xL.loadSelf; %Reload incase changed +% xL.unlockJobList; +% end + y = cHMM(hmmFolder); + y.numCoeff = 14*3; +% y.createSCP(xL.opFolder) +% y.createMLF(xL.opFolder) +% y.train(xL.opFolder) %This node can be busy training, even if other jobs are being processed for testing + + % ALLOW MASTER NODE TO MUCK IN WITH GENERATING TESTING FEATURES ONCE + % HMM HAS BEEN TRAINED + for nn = 1:maxConds + worker(xR{nn}.opFolder); + end + + xR{end}.lockJobList; + xR{end} = xR{end}.loadSelf; %Reload changes + xR{end}.unlockJobList; + while(~all(xR{end}.todoStatus==2)) + disp('Waiting on straggler nodes to complete their jobs before HMM is tested . . .') + pause(30); %Wait for 30 seconds before looking again + xR{end}.lockJobList; + xR{end} = xR{end}.loadSelf; %Reload incase changed + xR{end}.unlockJobList; + end + + for nn = 1:maxConds + y.createSCP(xR{nn}.opFolder); + y.test(xR{nn}.opFolder); + end + + %Show all of the scores in the command window at the end + for nn = 1:maxConds + y.score(xR{nn}.opFolder); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/MAPwrap.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,14 @@ +% This function wraps up whatever version of MAP I want to call. It is +% implemented partly because I want to avoid messing with jobject too much +% and mostly because I dont want to declare globals in my class. + +function [myANprobRateOutput, mydt, myBF] = MAPwrap(stimulus, sampleRate, BFlist, participant, AN_spikesOrProbability, paramChanges) + + +global ANprobRateOutput dt BFlist +% disp(20*log10(sqrt(mean(stimulus.^2))/20e-6)) +MAP1_14(stimulus, sampleRate, BFlist, participant, AN_spikesOrProbability, paramChanges); +% disp(20*log10(sqrt(mean(stimulus.^2))/20e-6)) +myANprobRateOutput = ANprobRateOutput; +mydt = dt; +myBF = BFlist;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/cEssexAid.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,651 @@ +classdef cEssexAid + %ESSEXAID_WRAPCLASS Wrapper for the EssexAid - Nick Clark July 2011 + % This class wraps up the EssexAid algorithm function that processes + % each block of samples. This wrapper closely emulates the GUI used + % in the lab and runs stimuli through the exact same algorithm used + % in the lab. It even includes a helper function to generate C code + % from the algorithm for use in a real-time framework. + + + %% ********************************************************* + % properties _ _ + % | | (_) + % _ __ _ __ ___ _ __ ___ _ __| |_ _ ___ ___ + % | '_ \| '__/ _ \| '_ \ / _ \ '__| __| |/ _ \/ __| + % | |_) | | | (_) | |_) | __/ | | |_| | __/\__ \ + % | .__/|_| \___/| .__/ \___|_| \__|_|\___||___/ + % | | | | + % |_| |_| + %************************************************************ + + %% ********************************************************** + % Public properties - can be set by user + %************************************************************ + properties(Access = public) + sr = 48e3; + numSamples = 1024; %MAX=6912, LAB_USE=48 + stimulusUSER + + %------------------------------------------------------------------ + % Params for audiometric freqs 250, 500, 1000, 2000, 4000, 8000 Hz + %------------------------------------------------------------------ + audiometry_dB= [ 0; 0; 0; 0; 0; 0]; %Pure tone threshold in dB SPL + mainGain_dB = [ 0; 0; 0; 0; 0; 0]; %Gain applied at audiometric frequencies + TC_dBHL = [40; 40; 40; 40; 40; 40]; %Compression thresholds (in dB HL from 2nd filt) + TM_dBHL = [10; 10; 10; 10; 10; 10]; %MOC thresholds (in dB OUTPUT from 2nd filt) + DRNLc = [ 0.2; 0.2; 0.2; 0.2; 0.2; 0.2]; %Compression exponent at audiometric frequencies + + %------------------------------------------------------------------ + % Dynamic compression properties + %------------------------------------------------------------------ + ARtau = 60e-3; %decay time constant + ARthreshold_dB = 85; %dB SPL (input signal level) =>200 to disable + MOCtau = 450e-3; %Time constant in Seconds + MOCfactor = 0.5; %dB attenuation applied to the input per dB exceeding output threshold + + %------------------------------------------------------------------ + % Band filtering properties + %------------------------------------------------------------------ + bwOct = 1/2; %1/1, 1/2, 1/3, 1/4, 1/5 + filterOrder = 2 %BUTTER=2, GTF=3 + useGTF = false; %If false, revert to butterworth + end + + %% ********************************************************** + % Read only properties that are not dependent + %************************************************************ + properties(SetAccess = private) + MOCrecord + end + + %% ********************************************************** + % Constant properties + %************************************************************ + properties(Constant = true, Hidden = true) + numAudiometricFreqs = 6; + end + + %% ********************************************************** + % Dependent visable properties - calculated at runtime + %************************************************************ + properties(Dependent = true, Hidden = false) + channelBFs %= 250 * 2.^((0:fNmax)'*params.bwOct); + numChannels %= numel(channelBFs); + aidOPnice %aid output reformatted to be exactly the same dimensions as the input stimulus + end + + %% ********************************************************** + % Dependent invisable properties - calculated at runtime + %************************************************************ + properties(Dependent = true, Hidden = true) + TC_dBO_INTERP % Compression threshold in terms of 2nd filter o/p in dB SPL + TM_dBO_INTERP % MOC threshold in terms of 2nd filter o/p in dB SPL + bwOct_INTERP + DRNLb_INTERP %= ( 2e-5 .* 10.^(TCdBO/20)) .^ (1-DRNLc) ; + DRNLc_INTERP + mainGain_INTERP %Interp'd and in linear units + + ARthresholdPa %= 20e-6*10^(ARthreshold_dB/20);% Pa thresh for triggering AR + stimulusINTERNAL %input stimulus in correct format for the Aid algo + end + + %% ********************************************************** + % Protected properties - The user never needs to set + %************************************************************ + properties(Access = protected) + aidOP + emlc_z + + %-------------------------------------------------------------- + % ENUMERATIONS USED IN THE FRAME PROCESSOR + %-------------------------------------------------------------- + enumC_ARb = 0; + enumC_ARa = 2; + enumC_MOCb = 4; + enumC_MOCa = 6; + + % enumC_BPb1 = 8; + % enumC_BPa1 = 13; + % enumC_BPb2 = 18; + % enumC_BPa2 = 23; + % enumC_BPb3 = 28; + % enumC_BPa3 = 33; + % enumC_BPb4 = 38; + % enumC_BPa4 = 43; + + enumS_AR = 0; + + % enumS_MOC1 = 1; + % enumS_BPin_1_1 = 2; + % enumS_BPin_2_1 = 6; + % enumS_BPout_1_1 = 10; + % enumS_BPout_2_1 = 14; + % + % enumS_MOC2 = 18; + % enumS_BPin_1_2 = 19; + % enumS_BPin_2_2 = 23; + % enumS_BPout_1_2 = 27; + % enumS_BPout_2_2 = 31; + % ... + end + + %% ********************************************************** + % methods _ _ _ + % | | | | | | + % _ __ ___ ___| |_| |__ ___ __| |___ + %| '_ ` _ \ / _ \ __| '_ \ / _ \ / _` / __| + %| | | | | | __/ |_| | | | (_) | (_| \__ \ + %|_| |_| |_|\___|\__|_| |_|\___/ \__,_|___/ + %************************************************************ + + methods + %% ********************************************************** + % Constructor + %************************************************************ + function obj = EssexAid_WrapClass(sr, stimulus) + + if nargin > 0 + obj.sr = sr; + end + + if nargin > 1 + obj.stimulusUSER = stimulus; + else + obj.stimulusUSER = obj.pipSequence(obj.sr); + end + end + + %% ********************************************************** + % Get method for channelBFs + %************************************************************ + function value = get.channelBFs(obj) + fNmax = 5/obj.bwOct; + value = 250 * 2.^((0:fNmax)'*obj.bwOct); + end + + %% ********************************************************** + % Get method for numChannels + %************************************************************ + function value = get.numChannels(obj) + value = numel(obj.channelBFs); + end + + %% ********************************************************** + % Get method for ARthresholdPa + %************************************************************ + function value = get.ARthresholdPa(obj) + value = 20e-6*10^(obj.ARthreshold_dB/20);% Pa thresh for triggering AR + end + + %% ********************************************************** + % Get method for TC_dBO_INTERP + %************************************************************ + function value = get.TC_dBO_INTERP(obj) + TC_dBO = obj.audiometry_dB - obj.mainGain_dB + obj.TC_dBHL; + value = obj.interpPars(TC_dBO, obj.numChannels); + end + + %% ********************************************************** + % Get method for TM_dBO_INTERP + %************************************************************ + function value = get.TM_dBO_INTERP(obj) + TM_dBO = obj.audiometry_dB - obj.mainGain_dB + obj.TM_dBHL; + value = obj.interpPars(TM_dBO, obj.numChannels); + end + + %% ********************************************************** + % Get method for bwOct_INTERP + %************************************************************ + function value = get.bwOct_INTERP(obj) + value = repmat(obj.bwOct, 1, obj.numChannels); + end + + %% ********************************************************** + % Get method for DRNLb_INTERP + %************************************************************ + function value = get.DRNLb_INTERP(obj) + value = ( 2e-5 .* 10.^(obj.TC_dBO_INTERP/20)) .^ (1-obj.DRNLc_INTERP); + end + + %% ********************************************************** + % Get method for DRNLc_INTERP + %************************************************************ + function value = get.DRNLc_INTERP(obj) + value = obj.interpPars(obj.DRNLc, obj.numChannels); + end + + %% ********************************************************** + % Get method for mainGain_INTERP + %************************************************************ + function value = get.mainGain_INTERP(obj) + mainGainLin = 10.^(obj.mainGain_dB/20); %lin units + value = obj.interpPars(mainGainLin, obj.numChannels); + end + + %% *********************************************************** + % Get method for stimulus + % ----------------------- + % The hearing aid expects a stereo signal, as the MOC control is + % linked for left and right channels. It would be more efficient to + % use a mono version of the aid for simulation in Matlab. However, + % I always want to use the exact same code for the hardware in the + % lab and current simulations. This code will make a mono signal + % stereo if needs be and/or rotate to 2xN array. + %************************************************************* + function value = get.stimulusINTERNAL(obj) + [nRows, nCols] = size(obj.stimulusUSER); + + % Assume that the stimulus duration is greater than 2 samples. + % Therefore the number of channels is the min dim. + [nChans, I] = min([nRows nCols]); + + if nChans == 2 + if I == 2 + value = obj.stimulusUSER; + else + value = obj.stimulusUSER'; + end + elseif nChans == 1 %Just to be explicit + if I == 2 + value = [obj.stimulusUSER obj.stimulusUSER]; + else + value = [obj.stimulusUSER; obj.stimulusUSER]'; + end + end + end + + %% *********************************************************** + % Get method for aid output + % ----------------------- + % This get method is linked to the above internal stimulus method + % and allows the user to extract the hearing aid output in exactly + % the same shape and size as the original input stimulus. This is + % very useful for the speech recognition work and presumably + % for multithreshold also. + %************************************************************* + function value = get.aidOPnice(obj) + if ~isempty(obj.aidOP) + [nRows, nCols] = size(obj.stimulusUSER); + + % Assume that the stimulus duration is greater than 2 samples. + % Therefore the number of channels is the min dim. + [nChans, I] = min([nRows nCols]); + + %** The aid output will ALWAYS be a 2xN array ** + %The fist job is to remove trailing zeros that may have been + %introduced by the framing process + aidOPtruncated = obj.aidOP(:, 1:max([nRows nCols])); + + %The next task is to arrange the op like the ip + if nChans == 2 + if I == 1 + value = aidOPtruncated; + else + value = aidOPtruncated'; + end + elseif nChans == 1 %Just to be explicit + if I == 1 + value = aidOPtruncated(1,:); + else + value = aidOPtruncated(1,:)'; + end + end + else % ---- of if isempty statement + value = []; + end + end + + %% *********************************************************** + % *** Set methods *** + % ----------------------- + % This is a bunch of unexciting error hunting functions. They also + % flush the aid output if any parameters change. Therefore, + % processStim will have to be called explicity by the user once + % again. + %************************************************************* + function obj = set.stimulusUSER(obj,value) + [nRows, nCols] = size(value); + + % Assume that the stimulus duration is greater than 2 samples. + % Therefore the number of channels is the min dim. + nChans = min([nRows nCols]); + assert(nChans<3 && nChans, 'Number of stimulus channels must be 1 or 2') + + obj = obj.flushAidData; %flush any previous hearing aid data if the input stimulus changes + obj.stimulusUSER = value; + end + function obj = set.sr(obj,value) + assert(value>=20e3 && value<=192e3, 'sr must be between 20 and 192 kHz') + obj = obj.flushAidData; + obj.sr = value; + end + function obj = set.numSamples(obj,value) + assert(value>=48 && value<=6912, 'must be between 48 and 6912 samples') + obj = obj.flushAidData; + obj.numSamples = value; + end + function obj = set.audiometry_dB(obj,value) + [nRows,nCols] = size(value); + assert(nRows==obj.numAudiometricFreqs && nCols==1, 'must be 6x1 column vector') %#ok<MCSUP> + obj = obj.flushAidData; + obj.audiometry_dB = value; + end + function obj = set.mainGain_dB(obj,value) + [nRows,nCols] = size(value); + assert(nRows==obj.numAudiometricFreqs && nCols==1, 'must be 6x1 column vector') %#ok<MCSUP> + obj = obj.flushAidData; + obj.mainGain_dB = value; + end + function obj = set.TC_dBHL(obj,value) + [nRows,nCols] = size(value); + assert(nRows==obj.numAudiometricFreqs && nCols==1, 'must be 6x1 column vector') %#ok<MCSUP> + obj = obj.flushAidData; + obj.TC_dBHL = value; + end + function obj = set.TM_dBHL(obj,value) + [nRows,nCols] = size(value); + assert(nRows==obj.numAudiometricFreqs && nCols==1, 'must be 6x1 column vector') %#ok<MCSUP> + obj = obj.flushAidData; + obj.TM_dBHL = value; + end + function obj = set.DRNLc(obj,value) + [nRows,nCols] = size(value); + assert(nRows==obj.numAudiometricFreqs && nCols==1, 'must be 6x1 column vector') %#ok<MCSUP> + assert(all(value)>=0 && all(value)<=1, 'all DRNLc values must be between 0 and 1') + obj = obj.flushAidData; + obj.DRNLc = value; + end + function obj = set.ARtau(obj,value) + assert(value>=1e-3 && value<=1, 'must be between 1e-3 and 1s') + obj = obj.flushAidData; + obj.ARtau = value; + end + function obj = set.ARthreshold_dB(obj,value) + assert(value>0, 'set AR to a high value to disable it') + obj = obj.flushAidData; + obj.ARthreshold_dB = value; + end + function obj = set.MOCtau(obj,value) + assert(value>=1e-3 && value<=2, 'must be between 1e-3 and 2s') + obj = obj.flushAidData; + obj.MOCtau = value; + end + function obj = set.MOCfactor(obj,value) + assert(value>=0 && value<=1, 'must be between 0 and 1') + obj = obj.flushAidData; + obj.MOCfactor = value; + end + function obj = set.bwOct(obj,value) + assert(value==1/1 || value==1/2 || value==1/3 || value==1/4 || value==1/5, 'must be one of 1./(1:5)') + obj = obj.flushAidData; + obj.bwOct = value; + end + function obj = set.filterOrder(obj,value) + assert(value>0 && value<5, 'must be one of 1:4') + obj = obj.flushAidData; + obj.filterOrder = value; + end + function obj = set.useGTF(obj,value) + obj = obj.flushAidData; + obj.useGTF = value; + end + + %% ********************************************************** + % flushAidData + % This second function is a workaround allowing a set method to + % change another property value. + %************************************************************ + function obj = flushAidData(obj) + obj.aidOP = []; + obj.MOCrecord = []; + end + + + %% ********************************************************** + % OVERLOADED plot method + %************************************************************ + function plot(obj) + clf + sig2dBSPL = @(sig)20*log10(abs(sig/20e-6)+(1/(2^32))); + dt = 1/obj.sr; + tAxis = dt:dt:dt*size(obj.stimulusINTERNAL,1); + + subplot(2,1,1) + plot(tAxis(1:length(obj.stimulusUSER)), sig2dBSPL(obj.stimulusUSER), 'k') + if ~isempty(obj.aidOPnice) + hold on + plot(tAxis(1:length(obj.stimulusUSER)), sig2dBSPL(obj.aidOPnice), 'r') + end + ylim([0 100]) + xlim([0 tAxis(length(obj.stimulusUSER))]) + title('Level response') + xlabel('Time in seconds') + ylabel('Level in dB SPL') + + subplot(2,1,2) + if ~isempty(obj.MOCrecord) + imagesc(tAxis, 1:obj.numChannels, flipud(-20*log10(obj.MOCrecord))) + colorbar + end + title('MOC attenuation') + xlabel('Time in seconds') + ylabel('Band frequency in Hz') + numSpacers = 1 + (obj.numChannels-numel(obj.DRNLc)) / (numel(obj.DRNLc)-1); + set(gca, 'YTick', 1:numSpacers:obj.numChannels); + set(gca, 'YTickLabel', num2str(flipud([250; 500; 1000; 2000; 4000; 8000]))); + end% ------ OVERLOADED plot method + + %% ********************************************************** + % OVERLOADED soundsc method + %************************************************************ + function soundsc(obj) + soundsc(obj.aidOPnice, obj.sr) + end + + %% ********************************************************** + % processStim + %************************************************************ + function obj = processStim(obj) + %-------------------------------------------------------------- + % EMULATION OF THE GUI PARAMETER CONVERSIONS + %-------------------------------------------------------------- + biggestNumSamples = obj.numSamples; + + filterStatesL = (zeros(3000,1)); + filterStatesR = filterStatesL; + filterCoeffs = (zeros(5000,1)); + + %filter coefficients + ARcutOff=1/(2*pi*obj.ARtau); + [b,a] = butter(1,ARcutOff/(obj.sr/2)); + filterCoeffs(obj.enumC_ARb+1:obj.enumC_ARb+2) = b; + filterCoeffs(obj.enumC_ARa+1:obj.enumC_ARa+2) = a; + + MOCcutOff=1/(2*pi*obj.MOCtau); + [bMOC,aMOC] = butter(1,MOCcutOff/(obj.sr/2)); + filterCoeffs(obj.enumC_MOCb+1:obj.enumC_MOCb+2) = bMOC; + filterCoeffs(obj.enumC_MOCa+1:obj.enumC_MOCa+2) = aMOC; + + + for filterCount = 1:obj.numChannels + %----------------------------------- + % nonlinear path - filter bws + %----------------------------------- + lowerCutOff=obj.channelBFs(filterCount)*2^(-obj.bwOct_INTERP(filterCount)/2); + upperCutOff=obj.channelBFs(filterCount)*2^( obj.bwOct_INTERP(filterCount)/2); + + if obj.useGTF + bwHz = upperCutOff - lowerCutOff; + [b_DRNL,a_DRNL] = obj.gammatone(bwHz, obj.channelBFs(filterCount), 1/obj.sr); + filterCoeffs(10*(filterCount-1)+9 :10*(filterCount-1)+10) = b_DRNL; + filterCoeffs(10*(filterCount-1)+14:10*(filterCount-1)+16) = a_DRNL; + else + [b_DRNL,a_DRNL] = butter(2,[lowerCutOff upperCutOff]/(obj.sr/2)); + filterCoeffs(10*(filterCount-1)+9 :10*(filterCount-1)+13) = b_DRNL; + filterCoeffs(10*(filterCount-1)+14:10*(filterCount-1)+18) = a_DRNL; + end + end + + %-------------------------------------------------------------- + % EMULATION OF THE IO CALLBACK THREAD + %-------------------------------------------------------------- + frameBufferL = buffer(obj.stimulusINTERNAL(:,1), obj.numSamples); + frameBufferR = buffer(obj.stimulusINTERNAL(:,2), obj.numSamples); + nFrames = size(frameBufferL,2); + + pad = zeros(1,biggestNumSamples-obj.numSamples); + ARampL=ones(1,biggestNumSamples); + ARampR = ARampL; + MOCcontrol = ones(obj.numChannels, biggestNumSamples); + + peakIPL = zeros(5,1); + peakOPL = peakIPL; + rmsIPL = peakIPL; + rmsOPL = peakIPL; + + peakIPR = peakIPL; + peakOPR = peakIPL; + rmsIPR = peakIPL; + rmsOPR = peakIPL; + + MOCend = zeros(obj.numChannels,1); + + op = []; + moc= []; + for nn = 1:nFrames + frameBufferPadL = [frameBufferL(:,nn)' pad]; + frameBufferPadR = [frameBufferR(:,nn)' pad]; + + [ outBufferL, outBufferR, filterStatesL, filterStatesR, ARampL, ARampR, MOCend, peakIPL, peakOPL, rmsIPL, rmsOPL, peakIPR, peakOPR, rmsIPR, rmsOPR, MOCcontrol ] =... + EssexAidProcessVFrameSwitchable( ... + frameBufferPadL,... + frameBufferPadR,... + filterStatesL,... + filterStatesR,... + filterCoeffs,... + obj.numChannels,... + obj.numSamples,... + ARampL,... + ARampR,... + obj.ARthresholdPa,... + obj.filterOrder,... + obj.DRNLb_INTERP,... + obj.DRNLc_INTERP,... + obj.TM_dBO_INTERP,... + obj.MOCfactor,... + peakIPL,... + peakOPL,... + rmsIPL,... + rmsOPL,... + peakIPR,... + peakOPR,... + rmsIPR,... + rmsOPR,... + MOCend,... + MOCcontrol,... + obj.mainGain_INTERP,... + obj.useGTF); + + + outBuffer = ( [outBufferL(:, 1:obj.numSamples); outBufferR(:, 1:obj.numSamples)] ); + op = [op outBuffer]; %#ok<AGROW> + moc= [moc MOCcontrol]; %#ok<AGROW> + + end %End of frame processing emulation loop + obj.aidOP = op; + obj.MOCrecord=moc; + + + end %End of process stim method + + end %End of methods block + + %% ********************************************************* + % _ _ _ _ _ _ + % | | | | (_) | | | | | | + % ___| |_ __ _| |_ _ ___ _ __ ___ ___| |_| |__ ___ __| |___ + % / __| __/ _` | __| |/ __| | '_ ` _ \ / _ \ __| '_ \ / _ \ / _` / __| + % \__ \ || (_| | |_| | (__ | | | | | | __/ |_| | | | (_) | (_| \__ \ + % |___/\__\__,_|\__|_|\___| |_| |_| |_|\___|\__|_| |_|\___/ \__,_|___/ + %************************************************************ + + methods(Static) + %% ******************************************************** + % pipOut - sequence of tone pips at various levels + %********************************************************** + function pipOut = pipSequence(sampleRate, freq, dBlevs, pulseDur, silDur) + if nargin < 5 + silDur = 0.3; + end + if nargin < 4 + pulseDur = 0.1; + end + if nargin < 3 + dBlevs = 20:20:100; + end + if nargin < 2 + freq = 500; + end + if nargin < 1 + sampleRate = 48e3; + end + + dt = 1/sampleRate; + tAxis = dt:dt:pulseDur; + sPulse = sin(2*pi*freq*tAxis); + sPulse = sPulse./sqrt(mean(sPulse.^2)); + rms2dBspl = @(dBspl)20e-6*10^(dBspl/20); %sneaky short-hand function by (ab)using function handles + zPad = zeros(1,ceil(sampleRate*silDur)); + + pipOut = []; + for nn = 1:numel(dBlevs) + pipOut = [ pipOut sPulse*rms2dBspl(dBlevs(nn)) zPad]; %#ok<AGROW> + end + + end% ------ OF pipSequence + + %% ******************************************************** + % interpPars - Linear interpolation of given parameter to mimic GUI + % fitting functionality. + %********************************************************** + function fullArray = interpPars(shortArray, numBands) + nGUIbands = numel(shortArray); + if numBands == nGUIbands + fullArray = shortArray; + else + numSpacers = (numBands-nGUIbands) / (nGUIbands-1); + fullArray = shortArray(1); + for nn = 2:nGUIbands + fullArray = [fullArray,... + repmat(mean([shortArray(nn) shortArray(nn-1)]),1,numSpacers),... + shortArray(nn)]; %#ok<AGROW> + end + end + end% ----- OF interpPars + + %% ******************************************************** + % gammatone - get filter coefficients + %********************************************************** + function [b,a] = gammatone(bw, cf, dt) + phi = 2 * pi * bw * dt; + theta = 2 * pi * cf * dt; + cos_theta = cos(theta); + sin_theta = sin(theta); + alpha = -exp(-phi) * cos_theta; + b0 = 1.0; + b1 = 2 * alpha; + b2 = exp(-2 * phi); + z1 = (1 + alpha * cos_theta) - (alpha * sin_theta) * 1i; + z2 = (1 + b1 * cos_theta) - (b1 * sin_theta) * 1i; + z3 = (b2 * cos(2 * theta)) - (b2 * sin(2 * theta)) * 1i; + tf = (z2 + z3) / z1; + a0 = abs(tf); + a1 = alpha * a0; + + a = [b0, b1, b2]; + b = [a0, a1]; + end% ------ OF gammatone + end% ------ OF static methods + +end %End of classdef +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/cHMM.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,718 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You can obtain a copy of the GNU General Public License from +% http://www.gnu.org/copyleft/gpl.html or by writing to +% Free Software Foundation, Inc.,675 Mass Ave, Cambridge, MA 02139, USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +classdef cHMM + %HMMCLASS Handles all of the HTK related gubbins + % Please see the documentation located in a separate file for further + % information. + + %% ********************************************************* + % properties _ _ + % | | (_) + % _ __ _ __ ___ _ __ ___ _ __| |_ _ ___ ___ + % | '_ \| '__/ _ \| '_ \ / _ \ '__| __| |/ _ \/ __| + % | |_) | | | (_) | |_) | __/ | | |_| | __/\__ \ + % | .__/|_| \___/| .__/ \___|_| \__|_|\___||___/ + % | | | | + % |_| |_| + %************************************************************ + + %% ********************************************************** + % Public properties - can be set by user + %************************************************************ + properties(Access = public) + hmmFolder + paramType = 'USER_D_A'; %DELTAS and ACCELERATIONS + numCoeff = 27; %9*3 THIS IS FOR PROBABILITY MODEL (not high spont+low spont which would be 18*3=54) + + HERestDataPath = fullfile(pwd, 'def', 'HERest_digit'); + binPath = fullfile(pwd, 'def', 'bin'); + configFile = fullfile(pwd, 'def', 'config_STANDARD'); + trainWordListFile = fullfile(pwd, 'def', 'Grammar_digit', 'words3'); + testWordListFile = fullfile(pwd, 'def', 'Grammar_digit', 'wordsNoSevenZero'); + wordNetFile = fullfile(pwd, 'def', 'Grammar_digit', 'wdnetNoSP.slf'); + dictFile = fullfile(pwd, 'def', 'Grammar_digit', 'noSevenZeroDict'); + end + + %% ********************************************************** + % Dependent - never set by user. Only calculated when needed + %************************************************************ + properties(Dependent = true) + protoFile % = fullfile(pwd, 'def', 'proto_RobANonly_9'); %probability only + end + + %% ********************************************************* + % methods _ _ _ + % | | | | | | + % _ __ ___ ___| |_| |__ ___ __| |___ + %| '_ ` _ \ / _ \ __| '_ \ / _ \ / _` / __| + %| | | | | | __/ |_| | | | (_) | (_| \__ \ + %|_| |_| |_|\___|\__|_| |_|\___/ \__,_|___/ + %************************************************************ + + methods + %% ********************************************************** + % Constructor + %************************************************************ + function obj = cHMM(hmmFolder) + if nargin > 0 + obj.hmmFolder = hmmFolder; + end + end % ------ OF CONSTRUCTOR + + %% ********************************************************** + % genProto - generate task specific prototype + %************************************************************ + function genProto(obj) + % models_1mixsil.exe - takes input (hmmdef) and copies it making a + % basis of one, two three etc. etc. + obj.models_1mixsilMat(fullfile(obj.hmmFolder,'hmm0','hmmdef'), fullfile(obj.hmmFolder,'hmm0','models')); + end % ------ OF GENPROTO + + %% ********************************************************** + % istrained + %************************************************************ + function boolans = istrained(obj) + boolans = numel(dir(fullfile(obj.hmmFolder,'hmm36','models'))); + end + + %% ********************************************************** + % Train + %************************************************************ + function train(obj, trainFeatureFolder) + + % Most of the following code block can be replaced by doing a + % find and replace across the code below. I didn't + % want to mess with the working code too much, so I just + % copied the object properties needed into the variable names + % expected. + + ED_CMDFILE1 = fullfile(obj.HERestDataPath, 'sil1.hed'); + ED_CMDFILE2 = fullfile(obj.HERestDataPath, 'mix2_16.hed'); + ED_CMDFILE3 = fullfile(obj.HERestDataPath, 'mix3_16.hed'); + ED_CMDFILE4 = fullfile(obj.HERestDataPath, 'mix5_16.hed'); + ED_CMDFILE5 = fullfile(obj.HERestDataPath, 'mix7_16.hed'); + + NUM_COEF = obj.numCoeff; + PAR_TYPE = obj.paramType; + LIST_FILE = fullfile(obj.hmmFolder, 'tmp.list'); + + word_list = obj.trainWordListFile;%fullfile(obj.grammarPath, 'words'); + word_listSP = word_list; % for use in hmm4 onwards - UGLY HACK NOW SP ABANDONED + proto = obj.protoFile;%Does not exist on disk just yet probably - see a few lines down + config = obj.configFile; + train_list = fullfile(trainFeatureFolder, 'list.scp'); + labels = fullfile(trainFeatureFolder, 'labels.mlf'); + labelssp = labels; % for use in hmm4 onwards - UGLY HACK NOW SP ABANDONED + hmm_dir = obj.hmmFolder; + + FEAT_ROOT = trainFeatureFolder; + + % Now for the actual HMM training code + mkdir(hmm_dir) + for I = 0:36 + h = fullfile(hmm_dir,['hmm' num2str(I)]); + mkdir(h); + end + obj.makeProtoHmm(proto, obj.paramType, obj.numCoeff, 18); + + fid = fopen(train_list,'r'); + disp(train_list) + S = textscan(fid,'%s','Delimiter','\n'); + fclose(fid); + + fid = fopen(LIST_FILE,'w'); + + for I = 1:size(S{1},1) + str = fullfile(FEAT_ROOT,S{1}{I}); + fprintf(fid,'%s\n',str); + end; + fclose(fid); + + % HCompV just gets the vfloor stuff out so we can begin approximating + cmd = ['"HCompV" -T 2 -D -C "' config '" -o hmmdef -f 0.01 -m -S "' LIST_FILE '" -M "' hmm_dir filesep 'hmm0" "' proto '"']; + system(cmd); + + %cmd = [BINDIR filesep 'macro' binExt ' ' num2str(NUM_COEF) ' ' PAR_TYPE ' ' '"' hmm_dir filesep 'hmm0' filesep 'vFloors' '" "' hmm_dir filesep 'hmm0' filesep 'macros' '"']; + %system(cmd); + obj.macroMat(NUM_COEF,PAR_TYPE, fullfile(hmm_dir, 'hmm0', 'vFloors'), fullfile(hmm_dir, 'hmm0', 'macros')); + + %MAKE THE INITIAL MODEL PROTOTYPE + genProto(obj); + + disp('Seed HMM successfully Produced.....'); + + %Training + for I = 1:3 + disp(I) + j = I-1; + cmd = ['HERest -D -C ' config ' -I ' labels ' -t 250.0 150.0 1000.0 -S ' LIST_FILE ' -H ' hmm_dir filesep 'hmm' num2str(j) filesep 'macros -H ' hmm_dir filesep 'hmm' num2str(j) filesep 'models -M ' hmm_dir filesep 'hmm' num2str(I) ' ' word_list]; + % disp(cmd) + system(cmd); + end + + disp('3 iterations complete'); + + rmdir ([hmm_dir filesep 'hmm4'],'s') + copyfile ([hmm_dir filesep 'hmm3'], [hmm_dir filesep 'hmm4']) + + % The following command takes state 3 from the silence model + % and appends it to the end of the model as state 2 of the + % short pause model. + % Original: + % cmd = [BINDIR filesep 'spmodel_gen' binExt ' ' hmm_dir filesep 'hmm3' filesep 'models ' hmm_dir filesep 'hmm4' filesep 'models']; + % system(cmd); + % New: + obj.spmodel_genMat(fullfile(hmm_dir,'hmm3','models'), fullfile(hmm_dir,'hmm4','models')); + + cmd = ['HHEd -T 2 -H ' hmm_dir filesep 'hmm4' filesep 'macros -H ' hmm_dir filesep 'hmm4' filesep 'models -M ' hmm_dir filesep 'hmm5 ' ED_CMDFILE1 ' ' word_listSP ]; + system(cmd); + disp ('SP model fixed') + + % after the spmodel_gen command - the word_list is changed to + % word_listSP. The sp model is just ignored currently + + for I = 6:8 + disp(I) + j = I-1; + cmd = ['HERest -C ' config ' -I ' labelssp ' -S ' LIST_FILE ' -H ' hmm_dir filesep 'hmm' num2str(j) filesep 'macros -H ' hmm_dir filesep 'hmm' num2str(j) filesep 'models -M ' hmm_dir filesep 'hmm' num2str(I) ' ' word_listSP]; + system(cmd); + end + + disp('6 iterations complete'); + + cmd = ['HHEd -T 2 -H ' hmm_dir filesep 'hmm8' filesep 'macros -H ' hmm_dir filesep 'hmm8' filesep 'models -M ' hmm_dir filesep 'hmm9 ' ED_CMDFILE2 ' ' word_listSP ]; + system(cmd); + disp ('2 gaussians per mixture') + + for I = 10:12 + disp(I) + j = I-1; + cmd = ['HERest -C ' config ' -I ' labelssp ' -S ' LIST_FILE ' -H ' hmm_dir filesep 'hmm' num2str(j) filesep 'macros -H ' hmm_dir filesep 'hmm' num2str(j) filesep 'models -M ' hmm_dir filesep 'hmm' num2str(I) ' ' word_listSP]; + system(cmd); + end + + disp ('9 iterations completed') + + cmd = ['HHEd -T 2 -H ' hmm_dir filesep 'hmm12' filesep 'macros -H ' hmm_dir filesep 'hmm12' filesep 'models -M ' hmm_dir filesep 'hmm13 ' ED_CMDFILE3 ' ' word_listSP ]; + system(cmd); + disp ('3 gaussians per mixture') + + for I = 14:20 + disp(I) + j = I-1; + cmd = ['HERest -C ' config ' -I ' labelssp ' -S ' LIST_FILE ' -H ' hmm_dir filesep 'hmm' num2str(j) filesep 'macros -H ' hmm_dir filesep 'hmm' num2str(j) filesep 'models -M ' hmm_dir filesep 'hmm' num2str(I) ' ' word_listSP]; + system(cmd); + end + + disp ('16 iterations completed') + + cmd = ['HHEd -T 2 -H ' hmm_dir filesep 'hmm20' filesep 'macros -H ' hmm_dir filesep 'hmm20' filesep 'models -M ' hmm_dir filesep 'hmm21 ' ED_CMDFILE4 ' ' word_listSP ]; + system(cmd); + disp ('5 gaussians per mixture') + + for I = 22:28 + disp(I) + j = I-1; + cmd = ['HERest -C ' config ' -I ' labelssp ' -S ' LIST_FILE ' -H ' hmm_dir filesep 'hmm' num2str(j) filesep 'macros -H ' hmm_dir filesep 'hmm' num2str(j) filesep 'models -M ' hmm_dir filesep 'hmm' num2str(I) ' ' word_listSP]; + system(cmd); + end + + disp ('23 iterations completed') + + cmd = ['HHEd -T 2 -H ' hmm_dir filesep 'hmm28' filesep 'macros -H ' hmm_dir filesep 'hmm28' filesep 'models -M ' hmm_dir filesep 'hmm29 ' ED_CMDFILE5 ' ' word_listSP ]; + system(cmd); + disp ('7 gaussians per mixture') + + for I = 30:36 + disp(I) + j = I-1; + cmd = ['HERest -C ' config ' -I ' labelssp ' -S ' LIST_FILE ' -H ' hmm_dir filesep 'hmm' num2str(j) filesep 'macros -H ' hmm_dir filesep 'hmm' num2str(j) filesep 'models -M ' hmm_dir filesep 'hmm' num2str(I) ' ' word_listSP]; + system(cmd); + end + + disp ('30 iterations completed') + + end % ------ OF TRAIN + + %% ********************************************************** + % Test + %************************************************************ + function test(obj, testFeatureFolder) + flags = '-p 0.0 -s 0.0'; + test_word_list = obj.testWordListFile; + + net = obj.wordNetFile; + dict = obj.dictFile; + + LIST_FILE = fullfile(testFeatureFolder, 'tmp.list'); + config = obj.configFile; + + disp ('Now testing with 7 mixture HMMs') + mod_file = fullfile(obj.hmmFolder, 'hmm36', 'models'); + mac_file = fullfile(obj.hmmFolder, 'hmm36', 'macros'); + + + RESULTS_DIR = testFeatureFolder; + TEST_FEAT_ROOT = testFeatureFolder; + + N1list = fullfile(testFeatureFolder, 'list.scp'); + + ftest = fopen(N1list,'r'); + S = textscan(ftest,'%s','Delimiter','\n'); + fclose(ftest); + + flist = fopen(LIST_FILE,'w'); + for I = 1:size(S{1},1) + str = fullfile(TEST_FEAT_ROOT,S{1}{I}); + fprintf(flist,'%s\n',str); + end; + fclose(flist); + + cmd = ['HVite -D -H ' mac_file ' -H ' mod_file ' -S ' LIST_FILE ' -C ' config ' -w ' net ' -l ''*'' -i ' RESULTS_DIR filesep 'result.mlf ' flags ' ' dict ' ' test_word_list]; + system(cmd); + end % ------ OF TEST + + %% ********************************************************** + % Get methods determining feature vector related gubbins + %************************************************************ + function value = get.protoFile(obj) + value = fullfile(obj.hmmFolder, 'proto_AutoGen'); + end + + end % ------ OF METHODS + + %% ********************************************************* + % _ _ _ _ _ _ + % | | | | (_) | | | | | | + % ___| |_ __ _| |_ _ ___ _ __ ___ ___| |_| |__ ___ __| |___ + % / __| __/ _` | __| |/ __| | '_ ` _ \ / _ \ __| '_ \ / _ \ / _` / __| + % \__ \ || (_| | |_| | (__ | | | | | | __/ |_| | | | (_) | (_| \__ \ + % |___/\__\__,_|\__|_|\___| |_| |_| |_|\___|\__|_| |_|\___/ \__,_|___/ + %************************************************************ + + methods(Static) + %% ********************************************************** + % macroMat (matlab port of macro C code) + % ported by NC - Nov 2011 + %************************************************************ + function macroMat(VECSIZE, PARAMETER_TYPE, infile, outfile) + % This function takes the vFloors file that is created after + % invoking HCompV and generates a Macro file required for further + % HMM training + + ofp = fopen(outfile,'w'); + + fprintf(ofp, '~o\n<STREAMINFO> 1 %d\n', VECSIZE); + fprintf(ofp, '<VECSIZE> %d\n', VECSIZE); + fprintf(ofp, '<NULLD>\n<%s>\n', PARAMETER_TYPE); + + fp = fopen(infile); + tline = fgets(fp); + while ischar(tline) + fprintf(ofp,tline); + tline = fgets(fp); + end + fclose(fp); + fclose(ofp); + end %---- of MACROMAT + + %% ********************************************************** + % spmodel_genMat (matlab port of spmodel_gen C code) + % ported by NC - Nov 2011 + %************************************************************ + function spmodel_genMat(infile, outfile) + % This function copies the middle state (3) from the silence model + % and makes a sp model out of it by copying it to state 2 of the sp model. + + % From: http://www.voxforge.org/home/dev/acousticmodels/linux/create/htkjulius/tutorial/monophones/step-7 + % In the last step you created HMM models that did not include an "sp" + % (short pause) silence model - which refers to the types of short pauses + % that occur between words in normal speech. However, you did create a + % "sil" silence model - sil silence models are typically of longer + % duration, and refer to the pauses occur at the end of a sentence. + % + % The HTK book says that the sp model needs to have its "emitting state + % tied to the centre state of the silence model". What this means is that + % you need to create a new sp model in your hmmdefs, that it will use the + % centre state of sil, and then they both need to be 'tied' together. For + % a bit of background on HMMs and states, see this example. + % + % This can be done by copying the centre state from the sil model in your + % hmmdefs file and adding it to the sp model, and then running a special + % tool called HHED to 'tie' the sp model to the sil model so that they + % share the same centre state. The HTK book provides some background on + % what this means, but you need an understanding of the basics of Hidden + % Markov Modelling before tackling the HTK Book explanations + + ofp = fopen(outfile,'a+'); % we append this time + + fprintf(ofp,'~h "sp"\n'); + fprintf(ofp,'<BEGINHMM>\n<NUMSTATES> 3\n<STATE> 2\n'); + + %-- This block gets the hmmdef file to the MODEL + lNow = []; + fp = fopen(infile); + while ~(strcmpi(lNow, '~h "sil"')) + lNow = fgetl(fp); + end + %------------------------------ + + %-- This block gets the hmmdef file to the STATE + lNow = []; + fp = fopen(infile); + while ~(strcmpi(lNow, ['<STATE> ' num2str(3)])) + lNow = fgetl(fp); + end + %------------------------------ + + %%% This block puts a copy of the hmmdef file in from the + %%% correct line as found above + tline = fgetl(fp); + while ~(strcmpi(tline, ['<STATE> ' num2str(4)])) + fprintf(ofp,'%s\n', tline); + tline = fgetl(fp); + end + fclose(fp); + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + fprintf(ofp,'<TRANSP> 3\n0.000000e+00 1.000000e+00 0.000000e+00\n'); + fprintf(ofp,'0.000000e+00 5.000000e-01 5.000000e-01\n'); + fprintf(ofp,'0.000000e+00 0.000000e+00 0.000000e+00\n<ENDHMM>\n'); + + fclose(ofp); + end %---- of spmodel_genMat + + %% ********************************************************** + % models_1mixsilMat (matlab port of models_1mixsil C code) + % ported by NC - Nov 2011 + %************************************************************ + function models_1mixsilMat(infile, outfile) + % This function takes the hmmdef file and + % generates a HMM Model file + + ofp = fopen(outfile,'w'); + for ii = 1:11 + + %%% This block gets the hmmdef file to the correct line + lNow = []; + fp = fopen(infile); + while ~(strcmpi(lNow, '~h "hmmdef"')) + lNow = fgetl(fp); + end + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + %%% This block puts a numbered header + if ii==1; fprintf(ofp,'~h "one"\n'); end + if ii==2; fprintf(ofp,'~h "two"\n'); end + if ii==3; fprintf(ofp,'~h "three"\n'); end + if ii==4; fprintf(ofp,'~h "four"\n'); end + if ii==5; fprintf(ofp,'~h "five"\n'); end + if ii==6; fprintf(ofp,'~h "six"\n'); end + if ii==7; fprintf(ofp,'~h "seven"\n'); end + if ii==8; fprintf(ofp,'~h "eight"\n'); end + if ii==9; fprintf(ofp,'~h "nine"\n'); end + if ii==10; fprintf(ofp,'~h "oh"\n'); end + if ii==11; fprintf(ofp,'~h "zero"\n'); end + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + %%% This block puts a copy of the hmmdef file in from the + %%% correct line as found above + tline = fgets(fp); + while ischar(tline) + fprintf(ofp,tline); + tline = fgets(fp); + end + fclose(fp); %close it as we reached EOF + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + end + + %%% THIS IS THE SILENCE MODEL @ THE END + fprintf(ofp,'~h "sil"\n'); + fprintf(ofp,'<BEGINHMM>\n<NUMSTATES> 5\n'); + for kk = 2:4 + fprintf(ofp,'<STATE> %d\n<NUMMIXES> 1\n', kk); + %-- This block gets the hmmdef file to the correct line + lNow = []; + fp = fopen(infile); + while ~(strcmpi(lNow, ['<STATE> ' num2str(kk)])) + lNow = fgetl(fp); + end + %------------------------------ + + %%% This block puts a copy of the hmmdef file in from the + %%% correct line as found above + tline = fgetl(fp); + while ~(strcmpi(tline, ['<STATE> ' num2str(kk+1)])) + fprintf(ofp,'%s\n', tline); + tline = fgetl(fp); + end + fclose(fp); + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + end + + fprintf(ofp,'<TRANSP> 5\n0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00\n'); + fprintf(ofp,'0.000000e+00 6.000000e-01 4.000000e-01 0.000000e+00 0.000000e+00\n'); + fprintf(ofp,'0.000000e+00 0.000000e+00 6.000000e-01 4.000000e-01 0.000000e+00\n'); + fprintf(ofp,'0.000000e+00 0.000000e+00 0.000000e+00 7.000000e-01 3.000000e-01\n'); + fprintf(ofp,'0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00\n<ENDHMM>\n'); + + fclose(ofp); + end %---- of models_1mixsilMat + + %% ********************************************************** + % makeProtoHmm (Make a prototype HMM) + %************************************************************ + function makeProtoHmm(filename,featureType,numFeatures,numStates) + % filename is obvious + % featureType is usually USER_D_A + % numFeatures is number of features (including differences if used) + % numStates is usually 18 + + ofp = fopen(filename,'w'); + + fprintf(ofp,'<BeginHMM>\n'); + fprintf(ofp,' <NumStates> %d <VecSize> %d <%s> <nullD> <diagC>\n',numStates,numFeatures,featureType'); + fprintf(ofp,' <StreamInfo> 1 %d\n',numFeatures); + for state=2:numStates-1, + fprintf(ofp,' <State> %d <NumMixes> 1\n',state); + fprintf(ofp,' <Stream> 1\n'); + fprintf(ofp,' <Mixture> 1 1.0\n'); + fprintf(ofp,' <Mean> %d\n',numFeatures); + fprintf(ofp,' '); + fprintf(ofp,'%1.1f ',zeros(1,numFeatures)); + fprintf(ofp,'\n'); + fprintf(ofp,' <Variance> %d\n',numFeatures); + fprintf(ofp,' '); + fprintf(ofp,'%1.1f ',ones(1,numFeatures)); + fprintf(ofp,'\n'); + end + fprintf(ofp,'<TransP> %d\n',numStates); + transp = zeros(numFeatures); + transp(1,2)=1; + for state=2:numStates-2, + transp(state,state)=0.6; + transp(state,state+1)=0.4; + end + transp(numStates-1,numStates-1)=0.9; + transp(numStates-1,numStates)=0.1; + for state=1:numStates, + fprintf(ofp,'%1.3e ',transp(state,1:numStates)); + fprintf(ofp,'\n'); + end + fprintf(ofp,'<EndHMM>\n'); + fclose(ofp); + end %---- of MAKEPROTOHMM + + %% ********************************************************** + % createMLF - master label file - belongs with hmm class + %************************************************************ + function createMLF(mapFileFolder) + mlfFileName = 'labels'; + d = dir(fullfile(mapFileFolder, '*.map')); + fid = fopen([fullfile(mapFileFolder,mlfFileName) '.mlf'],'w'); + fprintf(fid,'#!MLF!#\n'); + + for I = 1:size(d,1) + fprintf(fid,['"*/' d(I).name(1:end-3) 'lab"\n']); + fprintf(fid,'sil\n'); + labels = d(I).name(5:end-5); + for J = 1:length(labels); + switch labels(J) + case 'O' + S = 'oh'; + case '1' + S = 'one'; + case '2' + S = 'two'; + case '3' + S = 'three'; + case '4' + S = 'four'; + case '5' + S = 'five'; + case '6' + S = 'six'; + case '7' + S = 'seven'; + case '8' + S = 'eight'; + case '9' + S = 'nine'; + case 'Z' + S = 'zero'; + end + fprintf(fid,S); + fprintf(fid,'\n'); + end; + fprintf(fid,'sil\n.\n'); + end; + fclose(fid); + end % ------ OF CREATEMLF + + %% ********************************************************** + % createSCP - Just a file list + %************************************************************ + function createSCP(mapFileFolder) + scpFileName = 'list'; + d = dir(fullfile(mapFileFolder, '*.map')); + fid = fopen([fullfile(mapFileFolder,scpFileName) '.scp'],'w'); + + for I = 1:size(d,1) + fprintf(fid,[d(I).name(1:end-3) 'map\n']); + end; + + fclose(fid); + end% ------ OF createSCP + + %% ********************************************************** + % score - Simple non-dynamic scoring + %************************************************************ + function score(testMLFpath) + % First get the test result files and store the file locations + testIdx = 1; + temp = dir(testMLFpath); + for nn = 1:numel(temp) + if length(temp(nn).name) > 3 + if strcmp(temp(nn).name(end-3:end), '.mlf') + testMLFfiles{testIdx} = temp(nn).name; + testIdx = testIdx+1; + end + end + end + + for fileIdx = 1:numel(testMLFfiles) + % Extract file name and regonised words as strings -> store in recResults. + % This code block is Matt Robertson's + fmlf = fopen(fullfile(testMLFpath , testMLFfiles{fileIdx}),'r'); + + MLF = textscan(fmlf,'%s','delimiter','\n'); + replaceDownPattern = '[0-9\-\.\s]'; + + for I = 1:size(MLF{1},1); + if strfind(MLF{1}{I},'.rec') >0 + M{I} = MLF{1}{I}; + else + if strfind(MLF{1}{I},'MLF') >0; + M{I} = MLF{1}{I}; + else + M{I} = regexprep(MLF{1}{I},replaceDownPattern,''); + end + end; + end; + + fclose(fmlf); + + A = 1; + for I = 1:size(M,2) + if not(strcmpi(M{I},'sp')) && not(strcmpi(M{I},'sil')) && not(strcmpi(M{I},'')); + M2{A} = M{I}; + A = A + 1; + end; + end; + + recResults = []; + X=0; + for I = 1:length(M2) + if strfind(M2{I},'MLF') + else + if strfind(M2{I},'.rec') + X = X+1; + + % found FileName + recResults(X).fileName = M2{I}; + recResults(X).utterance = {}; + else + recResults(X).utterance = [recResults(X).utterance M2{I}]; + end; + end; + end; + + % Make separate 2D arrays of input and output digits + inputDigits = zeros(numel(recResults),3); + outputDigits = zeros(numel(recResults),3); + for nn = 1:numel(recResults) + ipStr = recResults(nn).fileName(end-8:end-6); + + for kk = 1:3 + inputDigits(nn,kk) = cHMM.htk_str2num(ipStr(kk)); %see local function @ bottom of script + opStr = recResults(nn).utterance(kk); + outputDigits(nn,kk) = cHMM.htk_str2num(opStr{:}); %bit of a hack to do cell2str as it were + end + end + + % Now do the scoring (simple whan data in the right format) + % :D + scoreArray = (inputDigits == outputDigits); + pcSent(fileIdx) = 100*sum(all(scoreArray,2)) / numel(recResults); + pcWord(fileIdx) = 100*sum(scoreArray(:)) / numel(scoreArray); + end + + %will output table to console if used on *nix + xlsdataFull = [{'-- File Name --', '%Sent', '%Word'}; testMLFfiles' num2cell(pcSent') num2cell(pcWord')]; + disp(xlsdataFull) + fid = fopen(fullfile(testMLFpath,['score__' num2str(pcWord(1), '%0.1f') '__.txt']),'w'); + fclose(fid); + end% ------ OF SCORE + + %% ********************************************************** + % scoreWhole folder - make my life easier @ command line + %************************************************************ + function scoreWholeFolder(folderToScore, searchString) + if nargin < 2 + searchString = '*featR*'; + end + dirInfo = dir(fullfile(folderToScore, searchString)); + numFolders = numel(dirInfo); + for nn = 1:numFolders; + currentScoring = fullfile(folderToScore, dirInfo(nn).name); + disp(''); + disp(currentScoring); + cHMM.score(currentScoring); + end + end + + %************************************************************************** + % htk_str2num - Convert strings to integers + %************************************************************************** + function opNum = htk_str2num(ipString) + switch ipString + case {'oh' , 'O'} + opNum = 0; + case {'one' , '1'} + opNum = 1; + case {'two' , '2'} + opNum = 2; + case {'three' , '3'} + opNum = 3; + case {'four' , '4'} + opNum = 4; + case {'five' , '5'} + opNum = 5; + case {'six' , '6'} + opNum = 6; + case {'eight' , '8'} + opNum = 8; + case {'nine' , '9'} + opNum = 9; + otherwise + assert(0) % throw error + end + end % ------ OF htk_str2num + + end % ------ OF STATIC METHODS + +end % ------ OF CLASS
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/cJob.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,941 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You can obtain a copy of the GNU General Public License from +% http://www.gnu.org/copyleft/gpl.html or by writing to +% Free Software Foundation, Inc.,675 Mass Ave, Cambridge, MA 02139, USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +classdef cJob + %CJOB Responsible for conversion of wav files into recogniser features + % Please see the documentation located in a separate file for further + % information. + + %% ********************************************************* + % properties _ _ + % | | (_) + % _ __ _ __ ___ _ __ ___ _ __| |_ _ ___ ___ + % | '_ \| '__/ _ \| '_ \ / _ \ '__| __| |/ _ \/ __| + % | |_) | | | (_) | |_) | __/ | | |_| | __/\__ \ + % | .__/|_| \___/| .__/ \___|_| \__|_|\___||___/ + % | | | | + % |_| |_| + %************************************************************ + + %% ********************************************************** + % Public properties - can be set by user + %************************************************************ + properties(Access = public) + wavFolder + opFolder + noiseFolder + + wavList + todoStatus + + + participant = 'Normal';%'DEMO2_multiSpont'; + noiseName = '8TalkerBabble'; + numWavs = 5; + noiseLevToUse = -200; + speechLevToUse = 50; + + speechLevStd = 0; + noiseLevStd = 0; + freezeNoise = 0; + meanSNR = 20; + speechDist = 'None'; + noiseDist = 'None'; + + noisePreDur = 0.0; + noisePostDur = 0.0; + truncateDur = 0.0; %Dr. RF used 0.550 + + currentSpeechLevel + currentNoiseLevel + + + useSpectrogram = false; + numCoeff = 9; + + %************************************************************ + % plotting handles + %************************************************************ + probHaxes = []; + probHaxesSM = []; + sacfHaxes = []; + sacfHaxesSM = []; + featHaxes = []; + reconHaxes = []; + + %************************************************************ + % SACF params + %************************************************************ + useSACF = false; + SACFacfTau = 2; % > 1 = Wiegrebe mode + SACFnBins = 128; + SACFminLag = 1 / 4000; + SACFmaxLag = 1 / 50; + SACFlambda = 10e-3; + + %************************************************************ + % MAP params + %************************************************************ + MAProot = fullfile('..'); + MAPplotGraphs = 0; + MAPDRNLSave = 0; + + MAPopLSR = 0; + MAPopMSR = 0; + MAPopHSR = 1; + + MAPparamChanges = {}; + + %************************************************************ + % HTK stuff - writing to HTK recogniser format + %************************************************************ + frameshift = 10; % shift between frames (ms) + sampPeriodFromMsFactor = 10000; % appropriate for 10ms frame rate + paramKind = 9; % HTK USER format for user-defined features (p73 of HTK book) + + removeEnergyStatic = false; + doCMN = false; % Cepstral mean normalisation + + %************************************************************ + % Portable EssexAid params + %************************************************************ + bwOct = 1/1; + filterOrder = 2; + + mainGain = [ 1; 1; 1; 1; 1]; % gain in linear units + TCdBO = [40; 40; 40; 40; 40]; %Compression thresholds (in dB OUTPUT from 2nd filt) + TMdBO = [10; 10; 10; 10; 10]; %MOC thresholds (in dB OUTPUT from 2nd filt) + DRNLc = [ 0.2; 0.2; 0.2; 0.2; 0.2;] + + ARtau = 0.03; % decay time constant + ARthresholddB = 85; % dB SPL (input signal level) =>200 to disable + MOCtau = 0.3; + MOCfactor = 0.5; %dB per dB OUTPUT + + numSamples = 1024; %MAX=6912 + useAid = 0; + end + + %% ********************************************************* + % Protected properties - inter/ra class communication + %************************************************************ + properties(Access = protected) + jobLockFid % File identifier for mutex + + %Nick C comment on this: + %OK. The big-endian thing used to work because in the config file + %'config_tr_zcpa12' there was a flag called NATURALREADORDER that was set to + %FALSE and thus appeared to override x86 standard: + %little-endian. Endianess has **!nothing!** to do with win vs *nix + byteOrder = 'le'; % byte order is big endian + end + properties(Dependent = true) + jobLockTxtFile + end + + %% ********************************************************* + % methods _ _ _ + % | | | | | | + % _ __ ___ ___| |_| |__ ___ __| |___ + %| '_ ` _ \ / _ \ __| '_ \ / _ \ / _` / __| + %| | | | | | __/ |_| | | | (_) | (_| \__ \ + %|_| |_| |_|\___|\__|_| |_|\___/ \__,_|___/ + %************************************************************ + + methods + %% ********************************************************** + % Constructor + %************************************************************ + function obj = cJob(LearnOrRecWavs, jobFolder) + if nargin < 1 + warning('job:ambiguouscorpus',... + 'We need to know whether the (L)earning or (R)ecognition corpus is being used, assuming ''L'''); + LearnOrRecWavs = 'L'; + end + if nargin > 1 + obj.opFolder = jobFolder; + else + if isunix + if ismac + obj.opFolder = '~/ASR/exps/_foo'; + else + obj.opFolder = '/scratch/nrclark/exps/_foo'; + end + else + obj.opFolder = 'D:\exps\_foo'; + end + end + + obj = obj.assignWavPaths(LearnOrRecWavs); + obj = obj.initMAP; + + + end % ------ OF CONSTRUCTOR + + %% ********************************************************** + % Set Wav Paths + %************************************************************ + function obj = assignWavPaths(obj, LearnOrRecWavs) + if isunix + if ismac + lWAVpath = fullfile('demo_wavs', 'TrainingData-Clean'); + rWAVpath = fullfile('demo_wavs', 'TripletTestData'); + obj.noiseFolder = fullfile('demo_wavs', 'noises'); + else + lWAVpath = fullfile('demo_wavs', 'TrainingData-Clean'); + rWAVpath = fullfile('demo_wavs', 'TripletTestData'); + obj.noiseFolder = fullfile('demo_wavs', 'noises'); + end + else + lWAVpath = 'D:\AURORA digits (wav)\TrainingData-Clean'; + rWAVpath = 'D:\AURORA digits (wav)\TripletTestData'; + obj.noiseFolder = 'D:\AURORA digits (wav)\noises'; + end + + if strcmpi(LearnOrRecWavs, 'l') + obj.wavFolder = lWAVpath; + elseif strcmpi(LearnOrRecWavs, 'r') + obj.wavFolder = rWAVpath; + else + error('First argument to constructor must be ''L'' or ''R'''); + end + end + + %% ********************************************************** + % mutex related _ + % | | + % _ __ ___ _ _| |_ _____ __ + % | '_ ` _ \| | | | __/ _ \ \/ / + % | | | | | | |_| | || __/> < + % |_| |_| |_|\__,_|\__\___/_/\_\ + %************************************************************ + + %% ********************************************************** + % lockJobList - File mutex protecting from race conditions + %************************************************************ + function obj = lockJobList(obj) + lockON = false; + while (~lockON) + if numel(dir(obj.jobLockTxtFile)) %Check to see if lock already in place + wTime = randi(750)+250; %3,000-10,000 ms + disp(['File mutex in place. Retrying in ' num2str(wTime) ' ms']) + pause(wTime/1000); + else + obj.jobLockFid = fopen(obj.jobLockTxtFile,'w'); + disp('Job locked'); + pause(50/1000); + lockON = true; + end + end + fclose(obj.jobLockFid); + end% ------ OF LOCKJOBLIST + + %% ********************************************************** + % unlockJobList - unlocks for other workers + %************************************************************ + function obj = unlockJobList(obj) + lockON = logical(numel(dir(obj.jobLockTxtFile))); + while(lockON) + try + delete(obj.jobLockTxtFile); + disp('Job unlocked'); + pause(50/1000); + lockON = false; + catch %#ok<CTCH> + disp('Unjamming lock - retrying immediately') + pause(200/1000) + end + end + end% ------ OF UNLOCKJOBLIST + + %% ********************************************************** + % storeSelf - save a copy of this object in opFolder + %************************************************************ + function storeSelf(obj) + doneSaving = false; + while(~doneSaving) + try + save(fullfile(obj.opFolder, 'jobObject'), 'obj'); + doneSaving = true; + catch %#ok<CTCH> + wTime = randi(3800)+200; %200-4000 ms + disp(['Save collision (THIS IS VERY VERY BAD - have you not used the mutex?). Retrying in ' num2str(wTime) ' ms']); + pause(wTime/1000); + end + end + end % ------ OF STORESELF + + %% ********************************************************** + % loadSelf - load a copy of this object from opFolder + %************************************************************ + function obj = loadSelf(obj) + doneLoading = false; + while(~doneLoading) + try + load(fullfile(obj.opFolder,'jobObject')); + doneLoading = true; + catch %#ok<CTCH> + wTime = randi(3800)+200; %200-4000 ms + disp(['Load collision (THIS IS VERY VERY BAD - have you not used the mutex?). Retrying in ' num2str(wTime) ' ms']) + pause(wTime/1000); + end + end + end% ------ OF LOADSELF + + + %% ********************************************************* + % _ _ _ + % | | | | (_) + % | |__ ___ _ _ ___ ___| | _____ ___ _ __ _ _ __ __ _ + % | '_ \ / _ \| | | / __|/ _ \ |/ / _ \/ _ \ '_ \| | '_ \ / _` | + % | | | | (_) | |_| \__ \ __/ < __/ __/ |_) | | | | | (_| | + % |_| |_|\___/ \__,_|___/\___|_|\_\___|\___| .__/|_|_| |_|\__, | + % | | __/ | + % |_| |___/ + %************************************************************ + + %% ********************************************************** + % get method for dependtent var jobLockTxtFile + %************************************************************ + function value = get.jobLockTxtFile(obj) + %Name the MUTEX file here + value = [fullfile(obj.opFolder, 'jobLock') '.txt']; + end + + %% ********************************************************** + % checkStatus - see how much of the current job is complete + %************************************************************ + function checkStatus(obj) + NOWopen = sum(obj.todoStatus==0); %Starting from R2010b, Matlab supports enumerations. For now, we resort to integers for compatibility. + NOWpend = sum(obj.todoStatus==1); + NOWdone = sum(obj.todoStatus==2); + + zz = clock; + disp([num2str(zz(3)) '-' num2str(zz(2)) '-' num2str(zz(1)) ' ' num2str(zz(4)) ':' num2str(zz(5))]) + disp(['CURRENT JOB:' obj.opFolder]); + disp(' ') + disp(['open - ' num2str(NOWopen) ' || pending - ' num2str(NOWpend) ' || complete - ' num2str(NOWdone)]) + + % ---- PROGRESS BAR ---- + pcDone = 100*NOWdone/obj.numWavs; + progBarLength = 40; + charBars = repmat('=',1,floor(pcDone/100 * progBarLength)); + charWhiteSpace = repmat(' ',1, progBarLength - numel(charBars)); + disp(' ') + disp([' -[' charBars charWhiteSpace ']- ' num2str(pcDone, '%0.1f') '%']) + disp(' ') + disp(' ') + % -- END PROGRESS BAR --- + end% ------ OF CHECKSTATUS + + %% ********************************************************** + % initMAP - add MAP stuff to path + %************************************************************ + function obj = initMAP(obj) + addpath(...fullfile(obj.MAProot, 'modules'),... + fullfile(obj.MAProot, 'utilities'),... + fullfile(obj.MAProot, 'MAP'),... + fullfile(obj.MAProot, 'parameterStore')); + end % ------ OF INIT MAP + + %% ********************************************************** + % assign files to testing and training sets + %************************************************************ + function obj = assignFiles(obj) + speechWavs = dir(fullfile(obj.wavFolder, '*.wav')); + assert(obj.numWavs <= size(speechWavs, 1) ,... + 'not enough files available in the corpus'); % make sure we have enough wavs + + randomWavs = rand(1, size(speechWavs, 1)); + [~,b] = sort(randomWavs); + trainFileIdx = b(1:obj.numWavs); + + obj.wavList = speechWavs(trainFileIdx); %This is a record of all of the wavs that should be done + + %Starting from R2010b, Matlab should support enumerated types. For now we + %use integers for compatibility. + %0=open, 1=processing, 2=done + obj.todoStatus = zeros(numel(obj.wavList), 1); + + end % ------ OF ASSIGN FILES + + %% ********************************************************** + % generate feature + %************************************************************ + function obj = genFeat(obj, currentWav) + fprintf(1,'Processing: %s \n', currentWav); + if strcmpi(obj.speechDist,'Gaussian') + tempSpeechLev = obj.speechLevToUse + obj.speechLevStd*randn; + elseif strcmpi(obj.speechDist,'Uniform') + % for a uniform distribution, the standard deviation is + % range/sqrt(12) + % http://mathforum.org/library/drmath/view/52066.html + tempSpeechLev = obj.speechLevToUse - obj.speechLevStd*sqrt(12)/2 + obj.speechLevStd*sqrt(12)*rand; + elseif strcmpi(obj.speechDist,'None') + tempSpeechLev = obj.speechLevToUse; + end + + if strcmpi(obj.noiseDist,'Gaussian') + tempNoiseLev = speechLev - obj.meanSNR + obj.noiseLevStd*randn; + elseif strcmpi(obj.noiseDist,'Uniform') + tempNoiseLev = tempSpeechLev - obj.meanSNR - obj.noiseLevStd*sqrt(12)/2 + obj.noiseLevStd*sqrt(12)*rand; + elseif strcmpi(obj.noiseDist,'None') + tempNoiseLev = obj.noiseLevToUse; + end + + disp(['Current speech level = ' num2str(tempSpeechLev)]); + disp(['Current noise level = ' num2str(tempNoiseLev)]); + + obj.currentSpeechLevel = tempSpeechLev; + obj.currentNoiseLevel = tempNoiseLev; + [finalFeatures, ~] = processWavs(obj, currentWav); %discard the output from ANprobabilityResponse and method using ~ + opForHTK(obj, currentWav, finalFeatures); + end % ------ OF GENFEAT + + %% ********************************************************** + % write o/p in HTK friendly format + %************************************************************ + function obj = opForHTK(obj, currentWav, featureData) + + featureName = strrep(currentWav, '.wav','.map'); + targetFilename = fullfile(obj.opFolder, featureName); + + % write in a format HTK compliant for the recogniser to use + obj.writeHTK(... + targetFilename,... + featureData,... + size(featureData,2),... + obj.frameshift*obj.sampPeriodFromMsFactor,... + size(featureData,1)*4,... + obj.paramKind,... + obj.byteOrder); + end % ------ OF opForHTK + + + %% ********************************************************** + % _ _ _ + % (_) | | (_) + % ___ _ __ _ _ __ __ _| | _ __ _ __ ___ ___ ___ ___ ___ _ _ __ __ _ + % / __| |/ _` | '_ \ / _` | | | '_ \| '__/ _ \ / __/ _ \/ __/ __| | '_ \ / _` | + % \__ \ | (_| | | | | (_| | | | |_) | | | (_) | (_| __/\__ \__ \ | | | | (_| | + % |___/_|\__, |_| |_|\__,_|_| | .__/|_| \___/ \___\___||___/___/_|_| |_|\__, | + % __/ | | | __/ | + % |___/ |_| |___/ + %************************************************************ + + %% ********************************************************** + % getStimulus - what it says on the tin + %************************************************************ + function [stimulus, sampleRate] = getStimulus(obj, currentWav) + + % getStimulus.m - NC Aug 2010 + % Modified version of Rob's script to include: + % + % 1)Signal and noise samples with different sample rate. The component with + % lower sample rate is upsampled to the rate of that with the + % higher rate. + % 2) Clearer level setting + % 3) Parameter to change noise intro duration + % 4) Noise padding at end of stimulus + % + % ORIGINAL HEADER: + % getStimulus.m + % + % Robert T. Ferry + % 13th May 2009 + + % Set levels + [speech speechSampleRate] = wavread(fullfile(obj.wavFolder, currentWav )); + speech = speech./sqrt(mean(speech.^2)); %Normalize RMS to 1 + speech = speech * 20e-6 * 10^(obj.currentSpeechLevel/20); %Convert RMS to pascals at desired level + %disp(20*log10(sqrt(mean(speech.^2))/20e-6)) + + [noise noiseSampleRate] = wavread(fullfile(obj.noiseFolder, obj.noiseName )); + noise = noise./sqrt(mean(noise.^2)); %Normalize RMS to 1 + noise = noise * 20e-6*10^(obj.currentNoiseLevel/20); %Convert RMS to pascals at desired level + %disp(20*log10(sqrt(mean(noise.^2))/20e-6)) + + % Do sample rate conversion if needed + % Will always convert stimulus component with lower rate up to that with + % higher rate. + if speechSampleRate > noiseSampleRate + % disp('S>N') + [p,q] = rat(speechSampleRate/noiseSampleRate,0.0001); + noise = resample(noise, p, q); + noiseSampleRate = speechSampleRate; + elseif noiseSampleRate > speechSampleRate + % disp('N>S') + [p,q] = rat(noiseSampleRate/speechSampleRate,0.0001); + speech = resample(speech, p, q); + speechSampleRate = noiseSampleRate; %#ok<NASGU> + else + %DO NOTHING BUT ASSERT + assert(speechSampleRate == noiseSampleRate); + end + sampleRate = noiseSampleRate; + dt = 1/sampleRate; + + % mix stimuli + % Everything from here down (mostly) is RTF's original + silenceStart = floor(obj.noisePreDur*sampleRate); + silenceEnd = floor(obj.noisePostDur*sampleRate); + + silencePointsStart = zeros(silenceStart,1); + silencePointsEnd = zeros(silenceEnd,1); + + speech = [silencePointsStart; speech; silencePointsEnd]; + + stimLength = length(speech); + noiseLength = length(noise); + if obj.freezeNoise + idx = 1; + else + idx = ceil(rand*(noiseLength-stimLength)); + end + noise = noise(idx:idx+stimLength-1); + + stimulus = speech+noise; + + % add ramps to noise + stimInNoiseTime = dt:dt:dt*length(stimulus); + rampDuration = 0.100; + rampTime = dt : dt : rampDuration; + ramp = [0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ones(1,length(stimInNoiseTime)-length(rampTime))]; + stimulus = stimulus'.*ramp; + ramp = fliplr(ramp); + stimulus = stimulus.*ramp; + stimulus = stimulus'; + %disp(20*log10(sqrt(mean(stimulus.^2))/20e-6)) + + % add additional silent period to start of stimulus for model to 'settle down' + additionalSilenceLength = round(0.050*sampleRate); + additionalSilencePoints = zeros(additionalSilenceLength,1); + stimulus = [additionalSilencePoints; stimulus]'; %now rotated. + %disp(20*log10(sqrt(mean(stimulus.^2))/20e-6)) + end% ------ OF GETSTIMULUS + + + %% ********************************************************** + % processWavs - do all of the MAP signal processing + %************************************************************ + function [finalFeatures, ANprobabilityResponse] = processWavs(obj, currentWav) + + %********************************************************** + % FIRST GET THE STIMULUS + %********************************************************** + [stimulus, sampleRate] = obj.getStimulus(currentWav); + + %********************************************************** + % NOW TO LOAD IN THE HEARING AID + %********************************************************** + if obj.useAid + stimulus = [stimulus; stimulus]'; %EsxAid requires stereo stim + stimulus = EssexAid_guiEmulatorWrapper(stimulus, sampleRate, obj); + stimulus = stimulus(1,:); %convert back to mono + end + + AN_spikesOrProbability = 'probability'; + + if obj.useSpectrogram + lowestBF=100; highestBF= 4500; numChannels=30; + F=round(logspace(log10(lowestBF),log10(highestBF),numChannels)); + + nfft = 1024; + hopSamples = 64; + noverlap = nfft - hopSamples; + dt = hopSamples/sampleRate; + [~,~,~,P] = spectrogram(stimulus,nfft,noverlap,F,sampleRate); + + ANprobabilityResponse = 10*log10( abs(P) / ((20e-6)^2) ); %now correct [(a^2)/(b^2) = (a/b)^2] + + else + [ANprobabilityResponse, dt, myBFlist] = MAPwrap(stimulus, sampleRate, -1, obj.participant, AN_spikesOrProbability, obj.MAPparamChanges); + end + nChannels = numel(myBFlist); + + + time_ANresponse = dt:dt:dt*size(ANprobabilityResponse,2); + idx = time_ANresponse > obj.truncateDur; %RTF had this @ 0.550 + ANprobabilityResponse = ANprobabilityResponse(:, idx); + + + if ~obj.useSpectrogram + if obj.MAPopLSR && ~obj.MAPopHSR + ANprobabilityResponse = ANprobabilityResponse(1:nChannels, :); %use LSR + end + if ~obj.MAPopLSR && obj.MAPopHSR + ANprobabilityResponse = ANprobabilityResponse(end-nChannels+1:end, :); %or use HSR + end + if obj.MAPopMSR + assert(0,'Not working with MSR at the mo') + end + end + + % OPTIONAL PLOTTING + YTickIdx = 1:floor(numel(myBFlist)/6):numel(myBFlist); + YTickIdxRev = numel(myBFlist)+1-YTickIdx; + if ~isempty(obj.probHaxes) + axes(obj.probHaxes); %#ok<MAXES> + imagesc(ANprobabilityResponse); colorbar('peer', obj.probHaxes) + set(obj.probHaxes, 'YTick', YTickIdx); + set(obj.probHaxes, 'YTickLabel', num2str( myBFlist(YTickIdxRev)' )); + ylabel('cf in Hz') + end + + % OPTIONAL PLOTTING SMOOTHED + if ~isempty(obj.probHaxesSM) + axes(obj.probHaxesSM); %#ok<MAXES> + anSM=flipud(obj.makeANsmooth(ANprobabilityResponse, 1/dt)); + imagesc((1:size(anSM,2))./100,1:size(ANprobabilityResponse,1),anSM); + set(obj.probHaxesSM, 'YTick', YTickIdx); + set(obj.probHaxesSM, 'YTickLabel', num2str( myBFlist(YTickIdxRev)' )); + shading(obj.probHaxesSM, 'flat'); colorbar('peer', obj.probHaxesSM) + ylabel('cf (Hz)') + xlabel('Time (s)') + end + + + %********************************************************** + % optional SACF stage + %********************************************************** + if obj.useSACF + + % A slightly ugly copying is needed + SACFparams.lambda = obj.SACFlambda; + SACFparams.acfTau = obj.SACFacfTau; + SACFparams.lags = logspace(log10(obj.SACFminLag),log10(obj.SACFmaxLag),obj.SACFnBins); + SACFparams.lags = linspace(obj.SACFminLag, obj.SACFmaxLag,obj.SACFnBins ); + + SACFmethod.dt = dt; + SACFmethod.nonlinCF = myBFlist; + + %This is slightly misleading as the ANprob is now a SACF + [ANprobabilityResponse, ~, ~, ~] = filteredSACF(ANprobabilityResponse, SACFmethod, SACFparams); + + % OPTIONAL PLOTTING + YTickIdx = 1:floor(obj.SACFnBins/6):obj.SACFnBins; + %YTickIdxRev = obj.SACFnBins+1-YTickIdx; + if ~isempty(obj.sacfHaxes) + axes(obj.sacfHaxes); %#ok<MAXES> + imagesc(flipud(ANprobabilityResponse)); shading(obj.sacfHaxes, 'flat'); colorbar('peer', obj.sacfHaxes) + set(obj.sacfHaxes, 'YTick', YTickIdx); + set(obj.sacfHaxes, 'YTickLabel', num2str( 1./SACFparams.lags(YTickIdx)' ,'%0.1f' )); + ylabel('Pitch in Hz') + end + + % OPTIONAL PLOTTING SMOOTHED + if ~isempty(obj.sacfHaxesSM) + axes(obj.sacfHaxesSM); %#ok<MAXES> + imagesc(flipud(obj.makeANsmooth(ANprobabilityResponse, 1/dt))); shading(obj.sacfHaxesSM, 'flat'); colorbar('peer', obj.sacfHaxesSM) + set(obj.sacfHaxesSM, 'YTick', YTickIdx); + set(obj.sacfHaxesSM, 'YTickLabel', num2str( 1./SACFparams.lags(YTickIdx)' ,'%0.1f' )); + ylabel('Pitch in Hz') + end + + end + + + finalFeatures = obj.makeANfeatures( ... + obj.makeANsmooth(ANprobabilityResponse, 1/dt), obj.numCoeff ); + + if obj.removeEnergyStatic + finalFeatures = finalFeatures(2:end,:); + % disp(size(finalFeatures)) + end + + if obj.doCMN + m = mean(finalFeatures,2); + finalFeatures = finalFeatures - repmat(m,1,size(finalFeatures,2)); + end + + % OPTIONAL PLOTTING + if ~isempty(obj.featHaxes) + pcolor(obj.featHaxes, finalFeatures); shading(obj.featHaxes, 'flat'); colorbar('peer', obj.featHaxes) + end + if ~isempty(obj.reconHaxes) + reconsData = idct(finalFeatures,obj.SACFnBins); + axes(obj.reconHaxes); %#ok<MAXES> + imagesc(flipud( reconsData )); + end + + opForHTK(obj, currentWav, finalFeatures); + end % ------ OF PROCESSWAVS + + end % ------ OF METHODS + + %% ********************************************************* + % _ _ _ _ _ _ + % | | | | (_) | | | | | | + % ___| |_ __ _| |_ _ ___ _ __ ___ ___| |_| |__ ___ __| |___ + % / __| __/ _` | __| |/ __| | '_ ` _ \ / _ \ __| '_ \ / _ \ / _` / __| + % \__ \ || (_| | |_| | (__ | | | | | | __/ |_| | | | (_) | (_| \__ \ + % |___/\__\__,_|\__|_|\___| |_| |_| |_|\___|\__|_| |_|\___/ \__,_|___/ + %************************************************************ + + methods(Static) + %% ******************************************************** + % makeANsmooth - smooth the AN response into hanning windowed chunks + %********************************************************** + function ANsmooth = makeANsmooth(ANresponse, sampleRate, winSize, hopSize) + if nargin < 3 + winSize = 25; %default 25 ms window + end + if nargin < 4 + hopSize = 10; %default 10 ms jump between windows + end + + winSizeSamples = round(winSize*sampleRate/1000); + hopSizeSamples = round(hopSize*sampleRate/1000); + + % smooth + hann = cJob.NRC_hanning(winSizeSamples); + + ANsmooth = [];%Cannot pre-allocate a size as it is unknown until the enframing + for chan = 1:size(ANresponse,1) + f = cJob.enframe(ANresponse(chan,:), hann, hopSizeSamples); + ANsmooth(chan,:) = mean(f,2)'; %#ok<AGROW> see above comment + end + end% ------ OF makeANsmooth + + %% ******************************************************** + % makeANfeatures - dct wizardry + %********************************************************** + function ANfeatures = makeANfeatures(ANrate, numCoeff) + % make feature vectors + features = cJob.GJB_dct(ANrate); + ANfeatures = features(1:numCoeff,:); + end % ------ OF makeANfeatures + + %% ************************************************************************ + % enframe - AVOID SIGNAL PROCESSING TOOLBOX buffer function + %************************************************************************** + function f=enframe(x,win,inc) + %ENFRAME split signal up into (overlapping) frames: one per row. F=(X,WIN,INC) + % + % F = ENFRAME(X,LEN) splits the vector X(:) up into + % frames. Each frame is of length LEN and occupies + % one row of the output matrix. The last few frames of X + % will be ignored if its length is not divisible by LEN. + % It is an error if X is shorter than LEN. + % + % F = ENFRAME(X,LEN,INC) has frames beginning at increments of INC + % The centre of frame I is X((I-1)*INC+(LEN+1)/2) for I=1,2,... + % The number of frames is fix((length(X)-LEN+INC)/INC) + % + % F = ENFRAME(X,WINDOW) or ENFRAME(X,WINDOW,INC) multiplies + % each frame by WINDOW(:) + + % Copyright (C) Mike Brookes 1997 + % Version: $Id: enframe.m,v 1.4 2006/06/22 19:07:50 dmb Exp $ + % + % VOICEBOX is a MATLAB toolbox for speech processing. + % Home page: http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html + % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % This program is free software; you can redistribute it and/or modify + % it under the terms of the GNU General Public License as published by + % the Free Software Foundation; either version 2 of the License, or + % (at your option) any later version. + % + % This program is distributed in the hope that it will be useful, + % but WITHOUT ANY WARRANTY; without even the implied warranty of + % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + % GNU General Public License for more details. + % + % You can obtain a copy of the GNU General Public License from + % http://www.gnu.org/copyleft/gpl.html or by writing to + % Free Software Foundation, Inc.,675 Mass Ave, Cambridge, MA 02139, USA. + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + nx=length(x(:)); + nwin=length(win); + if (nwin == 1) + len = win; + else + len = nwin; + end + if (nargin < 3) + inc = len; + end + nf = fix((nx-len+inc)/inc); + f=zeros(nf,len); + indf= inc*(0:(nf-1)).'; + inds = (1:len); + f(:) = x(indf(:,ones(1,len))+inds(ones(nf,1),:)); + if (nwin > 1) + w = win(:)'; + f = f .* w(ones(nf,1),:); + end + end % ------ OF ENFRAME + + %% ************************************************************************ + % GJB_dct - AVOID SIGNAL PROCESSING TOOLBOX + %************************************************************************** + function b=GJB_dct(a,n) + + if nargin == 0, + error('Not enough input arguments.'); + end + + if isempty(a) + b = []; + return + end + + % If input is a vector, make it a column: + do_trans = (size(a,1) == 1); + if do_trans, a = a(:); end + + if nargin==1, + n = size(a,1); + end + m = size(a,2); + + % Pad or truncate input if necessary + if size(a,1)<n, + aa = zeros(n,m); + aa(1:size(a,1),:) = a; + else + aa = a(1:n,:); + end + + % Compute weights to multiply DFT coefficients + ww = (exp(-1i*(0:n-1)*pi/(2*n))/sqrt(2*n)).'; + ww(1) = ww(1) / sqrt(2); + + if rem(n,2)==1 || ~isreal(a), % odd case + % Form intermediate even-symmetric matrix + y = zeros(2*n,m); + y(1:n,:) = aa; + y(n+1:2*n,:) = flipud(aa); + + % Compute the FFT and keep the appropriate portion: + yy = fft(y); + yy = yy(1:n,:); + + else % even case + % Re-order the elements of the columns of x + y = [ aa(1:2:n,:); aa(n:-2:2,:) ]; + yy = fft(y); + ww = 2*ww; % Double the weights for even-length case + end + + % Multiply FFT by weights: + b = ww(:,ones(1,m)) .* yy; + + if isreal(a), b = real(b); end + if do_trans, b = b.'; end + end % ----- of GJB_DCT + + %% ************************************************************************ + % NRC_hanning - AVOID SIGNAL PROCESSING TOOLBOX + %************************************************************************** + function w=NRC_hanning(n) + calc_hanning = @(m,n)0.5*(1 - cos(2*pi*(1:m)'/(n+1))); %cheeky anonymous function - I <3 these + if ~rem(n,2) + % Even length window + half = n/2; + w = calc_hanning(half,n); + w = [w; w(end:-1:1)]; + else + % Odd length window + half = (n+1)/2; + w = calc_hanning(half,n); + w = [w; w(end-1:-1:1)]; + end + end % ------ of NRC_HANNING + + %% ************************************************************************ + % writeHTK - convert data to htk format -> by anonymous Sue (2001) + %************************************************************************** + function retcode = writeHTK(filename, htkdata, nFrames, sampPeriod, SampSize, ParamKind, byte_order) + % Write an HTK format file. + % + % Input parameters: + % filename HTK data file + % htkdata HTK data read: an m x n matrix with + % m = no. of channels + % n = no. of frames + % The following are from the HTK header (see HTK manual): + % nFrames no. of frames (samples) + % sampPeriod sample period (in 100 ns units?) + % SampSize sample size + % ParamKind parameter kind code + % + % byteorder 'be' for big-endian (typical for Unix) (default) + % 'le' for little-endian (typical for MSWindows) + % + % Output parameters: + % retcode 0 if successful + + % Written by Sue 17/12/01 + + retcode=-1; % initialise in case of error + if nargin < 6 + fprintf('Usage: %s(filename, htkdata, nFrames, sampPeriod, SampSize, ParamKind [, byte_order])', mfilename); + end; + + % Default to big-endian (HTK format) + if nargin < 7 + byte_order = 'be'; + end; + + fid = fopen (filename, 'w', sprintf('ieee-%s', byte_order)); + if fid < 1 + fprintf('%s: can''t open output file %s\n', mfilename, filename); + return + end + + % Write header + fwrite (fid, nFrames, 'int32'); %nSamples in HTK + fwrite (fid, sampPeriod, 'int32'); + fwrite (fid, SampSize, 'int16'); + fwrite (fid, ParamKind, 'int16'); + + % Write actual data + fwrite(fid, htkdata, 'float32'); + + fclose(fid); + + retcode=0; + end% ------ OF WRITEHTK + + %% ************************************************************************ + % readHTK - just incase you ever want to go backwards + %************************************************************************** + function [htkdata,nframes,sampPeriod,sampSize,paramKind] = readHTK(filename,byte_order) + + if nargin<2 + byte_order = 'be'; + end + + fid = fopen(filename,'r',sprintf('ieee-%s',byte_order)); + + nframes = fread(fid,1,'int32'); + sampPeriod = fread(fid,1,'int32'); + sampSize = fread(fid,1,'int16'); + paramKind = fread(fid,1,'int16'); + + % read the data + + htkdata = fread(fid,nframes*(sampSize/4),'float32'); + htkdata = reshape(htkdata,sampSize/4,nframes); + fclose(fid); + end % ------ OF READHTK + + end % ------ OF STATIC METHODS + +end % ------ OF CLASS
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/def/Grammar_digit/noSevenZeroDict Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,11 @@ +one one +two two +three three +four four +five five +six six +eight eight +nine nine +oh oh +sp sp +sil sil
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/def/Grammar_digit/wdnetNRCnoSP.txt Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,7 @@ + +$digit = one | two | three | four | five | six | eight | nine | oh; + +( + sil $digit $digit $digit sil +) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/def/Grammar_digit/wdnetNoSP.slf Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,93 @@ +VERSION=1.0 +N=34 L=57 +I=0 W=!NULL +I=1 W=!NULL +I=2 W=sil +I=3 W=one +I=4 W=!NULL +I=5 W=two +I=6 W=three +I=7 W=four +I=8 W=five +I=9 W=six +I=10 W=eight +I=11 W=nine +I=12 W=oh +I=13 W=one +I=14 W=!NULL +I=15 W=two +I=16 W=three +I=17 W=four +I=18 W=five +I=19 W=six +I=20 W=eight +I=21 W=nine +I=22 W=oh +I=23 W=one +I=24 W=!NULL +I=25 W=two +I=26 W=three +I=27 W=four +I=28 W=five +I=29 W=six +I=30 W=eight +I=31 W=nine +I=32 W=oh +I=33 W=sil +J=0 S=33 E=1 +J=1 S=0 E=2 +J=2 S=2 E=3 +J=3 S=3 E=4 +J=4 S=5 E=4 +J=5 S=6 E=4 +J=6 S=7 E=4 +J=7 S=8 E=4 +J=8 S=9 E=4 +J=9 S=10 E=4 +J=10 S=11 E=4 +J=11 S=12 E=4 +J=12 S=2 E=5 +J=13 S=2 E=6 +J=14 S=2 E=7 +J=15 S=2 E=8 +J=16 S=2 E=9 +J=17 S=2 E=10 +J=18 S=2 E=11 +J=19 S=2 E=12 +J=20 S=4 E=13 +J=21 S=13 E=14 +J=22 S=15 E=14 +J=23 S=16 E=14 +J=24 S=17 E=14 +J=25 S=18 E=14 +J=26 S=19 E=14 +J=27 S=20 E=14 +J=28 S=21 E=14 +J=29 S=22 E=14 +J=30 S=4 E=15 +J=31 S=4 E=16 +J=32 S=4 E=17 +J=33 S=4 E=18 +J=34 S=4 E=19 +J=35 S=4 E=20 +J=36 S=4 E=21 +J=37 S=4 E=22 +J=38 S=14 E=23 +J=39 S=23 E=24 +J=40 S=25 E=24 +J=41 S=26 E=24 +J=42 S=27 E=24 +J=43 S=28 E=24 +J=44 S=29 E=24 +J=45 S=30 E=24 +J=46 S=31 E=24 +J=47 S=32 E=24 +J=48 S=14 E=25 +J=49 S=14 E=26 +J=50 S=14 E=27 +J=51 S=14 E=28 +J=52 S=14 E=29 +J=53 S=14 E=30 +J=54 S=14 E=31 +J=55 S=14 E=32 +J=56 S=24 E=33
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/def/Grammar_digit/words3 Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,12 @@ +one +two +three +four +five +six +seven +eight +nine +zero +oh +sil
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/def/Grammar_digit/wordsNoSevenZero Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,10 @@ +one +two +three +four +five +six +eight +nine +oh +sil
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/def/HERest_digit/mix2_16.hed Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,15 @@ +MU 2 {one.state[2-17].mix} +MU 2 {two.state[2-17].mix} +MU 2 {three.state[2-17].mix} +MU 2 {four.state[2-17].mix} +MU 2 {five.state[2-17].mix} +MU 2 {six.state[2-17].mix} +MU 2 {seven.state[2-17].mix} +MU 2 {eight.state[2-17].mix} +MU 2 {nine.state[2-17].mix} +MU 2 {oh.state[2-17].mix} +MU 2 {zero.state[2-17].mix} +MU 3 {sil.state[2-4].mix} + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/def/HERest_digit/mix3_16.hed Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,17 @@ +MU 3 {one.state[2-17].mix} +MU 3 {two.state[2-17].mix} +MU 3 {three.state[2-17].mix} +MU 3 {four.state[2-17].mix} +MU 3 {five.state[2-17].mix} +MU 3 {six.state[2-17].mix} +MU 3 {seven.state[2-17].mix} +MU 3 {eight.state[2-17].mix} +MU 3 {nine.state[2-17].mix} +MU 3 {oh.state[2-17].mix} +MU 3 {zero.state[2-17].mix} +MU 6 {sil.state[2-4].mix} + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/def/HERest_digit/mix5_16.hed Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,11 @@ +MU 5 {one.state[2-17].mix} +MU 5 {two.state[2-17].mix} +MU 5 {three.state[2-17].mix} +MU 5 {four.state[2-17].mix} +MU 5 {five.state[2-17].mix} +MU 5 {six.state[2-17].mix} +MU 5 {seven.state[2-17].mix} +MU 5 {eight.state[2-17].mix} +MU 5 {nine.state[2-17].mix} +MU 5 {oh.state[2-17].mix} +MU 5 {zero.state[2-17].mix}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/def/HERest_digit/mix7_16.hed Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,17 @@ +MU 7 {one.state[2-17].mix} +MU 7 {two.state[2-17].mix} +MU 7 {three.state[2-17].mix} +MU 7 {four.state[2-17].mix} +MU 7 {five.state[2-17].mix} +MU 7 {six.state[2-17].mix} +MU 7 {seven.state[2-17].mix} +MU 7 {eight.state[2-17].mix} +MU 7 {nine.state[2-17].mix} +MU 7 {oh.state[2-17].mix} +MU 7 {zero.state[2-17].mix} +MU 7 {sil.state[2-4].mix} + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/def/HERest_digit/sil1.hed Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,7 @@ +MU 2 {sil.state[2-4].mix} +AT 2 4 0.2 {sil.transP} +AT 4 2 0.2 {sil.transP} + +TI silst {sil.state[3]} + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/def/config_STANDARD Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,6 @@ +TARGETKIND = USER_D_A +DELTAWINDOW = 3 +ACCWINDOW = 2 +HNET:TRACE = 2 +# next line to read data machine dependent +NATURALREADORDER = TRUE
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsASRforDummies/worker.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,88 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You can obtain a copy of the GNU General Public License from +% http://www.gnu.org/copyleft/gpl.html or by writing to +% Free Software Foundation, Inc.,675 Mass Ave, Cambridge, MA 02139, USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% This is a function the makes sure all of the jobs get completed in an +% orderly fashion. It is responsible for keeping unruly nodes in line. +% NC 2010/1011 + +function worker(workFolder) + +%unit testing +% close all; clear all; clc +% workFolder = 'D:\exps\_bar'; + + +%main script +isloaded = 0; +while ~isloaded + if numel(dir(fullfile(workFolder,'jobLock.txt'))) %Check to see if lock already in place + pTime = randi(30); + disp(['Worker function locked out -> waiting for ' num2str(pTime) ' seconds until next retry']); + pause(pTime); + else + load(fullfile(workFolder,'jobObject.mat')) + isloaded = 1; + end +end + + + +x=obj; +clear obj; +x.initMAP; %Need to alert it to the path + +personalWork = 0; +while(any(x.todoStatus==0)) + x.lockJobList; + x = x.loadSelf; %Reload incase changed + rJobs = 8+randi(8);%Grab 1st 9-16 open jobs + todoNow = find(~x.todoStatus,rJobs,'first'); + x.todoStatus(todoNow) = 1; %Flag it (them) as pending + x.storeSelf; %store pending flag as quickly as possible to minimise race condition impact + x.unlockJobList; + disp(['Grabbed ' num2str(numel(todoNow)) ' sound files for current job.']) + + % --- DO WORK --- + for tt=1:numel(todoNow) + if ~numel(dir(fullfile(x.opFolder,strrep(x.wavList(todoNow(tt)).name, '.wav','.map')))); + x.genFeat(x.wavList(todoNow(tt)).name); + else + disp(['File ' x.wavList(todoNow(tt)).name ' already processed']) + end + + end + % --- END OF WORK --- + + + x.lockJobList; + x = x.loadSelf; %Reload incase changed while processing (probably has) + x.todoStatus(todoNow) = 2; %Flag as complete + x.storeSelf; %Update as done immediately + x.unlockJobList; + + clc + personalWork = personalWork+1; + disp( ['This process has completed ' num2str(personalWork) ' lists'] ) + x.checkStatus +end + +disp('-*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*-') +disp( ' > COMPLETED CURRENT JOB' ) +disp( [' In the folder ' workFolder ' .....'] ) +disp( [' This process completed ' num2str(personalWork) ' lists'] ) +disp('-*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*-') + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsDomenika/test_IC_DominikaTones.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,187 @@ +function test_IC_DominikaTones +% test_MAP1_14 is a general purpose test routine that can be adjusted to +% test a number of different applications of MAP1_14 +% +% A range of options are supplied in the early part of the program +% +% One use of the function is to create demonstrations; filenames <demoxx> +% to illustrate particular features +% +% #1 +% Identify the file (in 'MAPparamsName') containing the model parameters +% +% #2 +% Identify the kind of model required (in 'AN_spikesOrProbability'). +% A full brainstem model (spikes) can be computed or a shorter model +% (probability) that computes only so far as the auditory nerve +% +% #3 +% Choose between a tone signal or file input (in 'signalType') +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the channels in terms of their best frequencies in the vector +% BFlist. +% +% Last minute changes to the parameters fetched earlier can be made using +% the cell array of strings 'paramChanges'. +% Each string must have the same format as the corresponding line in the +% file identified in 'MAPparamsName' +% +% When the demonstration is satisfactory, freeze it by renaming it <demoxx> + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation +AN_spikesOrProbability='spikes'; +% or +% AN_spikesOrProbability='probability'; + + +%% #3 pure tone, harmonic sequence or speech file input +signalType= 'tones'; +sampleRate= 50000; +duration=0.100; % seconds +rampDuration=.005; % raised cosine ramp (seconds) +beginSilence=0.050; +endSilence=0.050; +toneFrequency= 5000; % or a pure tone (Hz) + +% or +% harmonic sequence (Hz) +% F0=210; +% toneFrequency= F0:F0:8000; + +% or +% signalType= 'file'; +% fileName='twister_44kHz'; + + + +%% #4 rms level +% signal details +leveldBSPL= 50; % dB SPL (80 for Lieberman) + + +%% #5 number of channels in the model +% 21-channel model (log spacing) +numChannels=60; +lowestBF=5000; highestBF= 5001; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +% or specify your own channel BFs +% numChannels=1; +% BFlist=toneFrequency; + + +%% #6 change model parameters + +paramChanges={'IHCpreSynapseParams.tauCa= [80e-6];'}; + +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read +% This example declares only one fiber type with a calcium clearance time +% constant of 80e-6 s (HSR fiber) when the probability option is selected. +% paramChanges={'AN_IHCsynapseParams.ANspeedUpFactor=5;', ... +% 'IHCpreSynapseParams.tauCa=86e-6; '}; + + + +%% delare 'showMap' options to control graphical output +showMapOptions.printModelParameters=1; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showEfferent=0; % tracks of AR and MOC +showMapOptions.ICrasterPlot=1; + +% disable certain silly options +if strcmp(AN_spikesOrProbability, 'spikes') + % avoid nonsensical options + showMapOptions.surfProbability=0; + showMapOptions.showACF=0; +end + +if strcmp(signalType, 'file') + % needed for labeling plot + showMapOptions.fileName=fileName; +else + showMapOptions.fileName=[]; +end + +%% Generate stimuli + +switch signalType + case 'tones' + % Create pure tone stimulus + dt=1/sampleRate; % seconds + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + % catch rampTime error + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + intialSilence= zeros(1,round(0.1/dt)); + finalSilence= zeros(1,round(0.2/dt)); + inputSignal= [intialSilence inputSignal' finalSilence]; +end + + +%% run the model +tic +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) +disp('Computing ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +%% the model run is now complete. Now display the results +UTIL_showMAP(showMapOptions, paramChanges) + +if strcmp(signalType,'tones') + disp(['duration=' num2str(duration)]) + disp(['level=' num2str(leveldBSPL)]) + disp(['toneFrequency=' num2str(toneFrequency)]) + global DRNLParams + disp(['attenuation factor =' ... + num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) + disp(['attenuation factor (probability)=' ... + num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) + disp(AN_spikesOrProbability) +end +disp(paramChanges) +toc +path(restorePath) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsMathiasDietz/test_binaural.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,433 @@ +function test_binaural +% test_binaural is a first attempt to produce a binaural model +% incorporating MSO and IC models. +% The monaural response is computed first for left and right stimuli +% before using the CN response as input to the binaural MSO model +% that, in turn, feeds a single cell IC model. +% +% The function has no arguments and everything is set up internally +% +% #1 +% Identify the file (in 'MAPparamsName') containing the model parameters +% the default is 'PL' which uses primary like neurons in the CN to +% simulate spherical bushy cells +% +% #2 +% Set AN_spikesOrProbability'). to 'spikes' +% +% #3 +% Choose between a tone signal or file input (in 'signalType') +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the channels in terms of their best frequencies in the vector +% BFlist. This is currently a single-channel model, so only one BF needed +% +% #6 +% Last minute changes to the parameters fetched earlier can be made using +% the cell array of strings 'paramChanges'. +% Each string must have the same format as the corresponding line in the +% file identified in 'MAPparamsName' +% Currently this is used to specify that only HSR fibers are used and +% for changing the current per AN spike at the CN dendrite +% +% #7 +% specify the parameters of the MSO cells in the MSOParams structure +% +% #8 +% specify the parameters of the IC cells in the ICMSOParams structure +% +% #9 +% identify the plots required from MAP1_14 (i.e. before the bonaural model) +% +% #10 +% Specify ITDs. The program cycles through different ITDs +% + +global CNoutput dtSpikes +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +%% #1 monaural model parameter file name +MAPparamsName='PL'; + + +%% #2 'spikes' are mandatory for this model +AN_spikesOrProbability='spikes'; + + +%% #3 pure tone, harmonic sequence or speech file input +signalType= 'tones'; +sampleRate= 50000; +duration=0.050; % seconds +rampDuration=.005; % raised cosine ramp (seconds) +beginSilence=0.050; +endSilence=0.050; +toneFrequency= 750; % or a pure tone (Hz) + +% or +% harmonic sequence (Hz) +% F0=210; +% toneFrequency= F0:F0:8000; + +% or +% signalType= 'file'; +% fileName='twister_44kHz'; + +if strcmp(signalType, 'file') + % needed for labeling plot + showMapOptions.fileName=fileName; +else + showMapOptions.fileName=[]; +end + +%% #4 rms level +leveldBSPL= 70; + + +%% #5 number of channels in the model +BFlist=toneFrequency; + +%% #6 change model parameters + +paramChanges={'IHCpreSynapseParams.tauCa=80e-6;',... + 'MacGregorMultiParams.currentPerSpike=0.800e-6;'}; + +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read + +%% #7 MSO parameters +MSOParams.nNeuronsPerBF= 10; % N neurons per BF +MSOParams.type = 'primary-like cell'; +MSOParams.fibersPerNeuron=4; % N input fibers +MSOParams.dendriteLPfreq=2000; % dendritic filter +MSOParams.currentPerSpike=0.11e-6; % (A) per spike +MSOParams.currentPerSpike=0.5e-6; % (A) per spike +MSOParams.Cap=4.55e-9; % cell capacitance (Siemens) +MSOParams.tauM=5e-4; % membrane time constant (s) +MSOParams.Ek=-0.01; % K+ eq. potential (V) +MSOParams.dGkSpike=3.64e-5; % K+ cond.shift on spike,S +MSOParams.tauGk= 0.0012; % K+ conductance tau (s) +MSOParams.Th0= 0.01; % equilibrium threshold (V) +MSOParams.c= 0.01; % threshold shift on spike, (V) +MSOParams.tauTh= 0.015; % variable threshold tau +MSOParams.Er=-0.06; % resting potential (V) +MSOParams.Eb=0.06; % spike height (V) +MSOParams.debugging=0; % (special) +MSOParams.wideband=0; % special for wideband units + +%% #8 IC parameters +ICchopperParams.nNeuronsPerBF= 10; % N neurons per BF +ICchopperParams.type = 'chopper cell'; +ICchopperParams.fibersPerNeuron=10; % N input fibers +ICchopperParams.dendriteLPfreq=50; % dendritic filter +ICchopperParams.currentPerSpike=50e-9; % *per spike +ICchopperParams.currentPerSpike=100e-9; % *per spike +ICchopperParams.Cap=1.67e-8; % ??cell capacitance (Siemens) +ICchopperParams.tauM=0.002; % membrane time constant (s) +ICchopperParams.Ek=-0.01; % K+ eq. potential (V) +ICchopperParams.dGkSpike=1.33e-4; % K+ cond.shift on spike,S +ICchopperParams.tauGk= 0.0005;% K+ conductance tau (s) +ICchopperParams.Th0= 0.01; % equilibrium threshold (V) +ICchopperParams.c= 0; % threshold shift on spike, (V) +ICchopperParams.tauTh= 0.02; % variable threshold tau +ICchopperParams.Er=-0.06; % resting potential (V) +ICchopperParams.Eb=0.06; % spike height (V) +ICchopperParams.PSTHbinWidth= 1e-4; + +%% #9 delare 'showMap' options to control graphical output +% this applies to the monaural input model only +showMapOptions.printModelParameters=0; % prints all parameters +showMapOptions.showModelOutput=1; % plot all stages if monaural input + +%% #10 ITDs +% the program cycles through a range of stimulus ITDs +ITDs=0e-6:100e-6:2000e-6; +% ITDs=0; % single shot + +%% Now start computing! +figure(98), clf, set(gcf, 'name', 'binaural demo') +MSOcounts=zeros(1,length(ITDs)); +ICcounts=zeros(1,length(ITDs)); +ITDcount=0; +for ITD=ITDs + ITDcount=ITDcount+1; + delaySamples=round(ITD* sampleRate); + %% Generate stimuli + switch signalType + case 'tones' + % Create pure tone stimulus + dt=1/sampleRate; % seconds + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + % catch rampTime error + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + intialSilence= zeros(1,round(0.1/dt)); + finalSilence= zeros(1,round(0.2/dt)); + inputSignal= [intialSilence inputSignal' finalSilence]; + end + + %% run the monaural model twice + t=dt:dt:dt*length(inputSignal); + for ear={'left','right'} + figure(98), subplot(4,1,1), colour='b'; hold off + switch ear{1} + case 'right' + inputSignal=circshift(inputSignal', delaySamples)'; + hold on + colour='r'; + end + plot(t, inputSignal, colour) + title ('binaural inputs signals') + ylabel('Pa'), xlabel('time') + xlim([0 max(t)]) + + % call to monaural model + MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + % the model run is now complete. Now display the results + UTIL_showMAP(showMapOptions, paramChanges) + + % copy the CN inputspiking pattern to the binaural display + figure(98) + switch ear{1} + case 'left' + CNoutputLeft=CNoutput; + subplot(4,2,3) + case 'right' + CNoutputRight=CNoutput; + subplot(4,2,4) + end + plotInstructions=[]; + plotInstructions.axes=gca; + plotInstructions.displaydt=dtSpikes; + plotInstructions.title= ['CN spikes ' ear{1}]; + plotInstructions.rasterDotSize=2; + if sum(sum(CNoutput))<100 + plotInstructions.rasterDotSize=3; + end + UTIL_plotMatrix(CNoutput, plotInstructions); + + end % left/ right ear + + %% MSO + % run MSO model using left and right CN spikes + MSOspikes=MSO(CNoutputLeft,CNoutputRight, dtSpikes, MSOParams); + + sumspikes=sum(sum(MSOspikes)); + disp(['ITD/ MSO spikes count= ' num2str([ITD sumspikes])]) + MSOcounts(ITDcount)=sumspikes; + figure(98), subplot(4,2, 8), cla, hold off, plot(ITDs*1e6, MSOcounts) + + %% IC chopper + % run IC model using all MSO spikes as input to a single IC cell + ICMSOspikes=ICchopper(MSOspikes, dtSpikes, ICchopperParams); + + sumspikes=sum(sum(ICMSOspikes)); + disp(['ITD/ ICMSO spikes count= ' num2str([ITD sumspikes])]) + ICcounts(ITDcount)=sumspikes; + figure(98), subplot(4,2,8),hold on, plot(ITDs*1e6, ICcounts,'r') + xlabel('ITD'), ylabel(' spike count') + title('MSO (blue)/ IC (red) spike counts') + legend({'MSO','IC'}) + +end % ITDs + +path(restorePath) + + +function MSOspikes=MSO(CNoutputLeft,CNoutputRight, dtSpikes, MSOparams) +%% +[nMSOcells nEpochs]=size(CNoutputLeft); +inputCurrent=zeros(nMSOcells, nEpochs); +MSOmembranePotential=inputCurrent; + +MSO_tauM=MSOparams.tauM; +MSO_tauGk=MSOparams.tauGk; +MSO_tauTh=MSOparams.tauTh; +MSO_cap=MSOparams.Cap; +MSO_c=MSOparams.c; +MSO_b=MSOparams.dGkSpike; +MSO_Th0=MSOparams.Th0; +MSO_Ek=MSOparams.Ek; +MSO_Eb= MSOparams.Eb; +MSO_Er=MSOparams.Er; + +MSO_E=zeros(nMSOcells,1); +MSO_Gk=zeros(nMSOcells,1); +MSO_Th=MSO_Th0*ones(nMSOcells,1); + +% Dendritic filtering, all spikes are replaced by CNalphaFunction functions +MSOdendriteLPfreq= MSOparams.dendriteLPfreq; +MSOcurrentPerSpike=MSOparams.currentPerSpike; +MSOspikeToCurrentTau=1/(2*pi*MSOdendriteLPfreq); +t=dtSpikes:dtSpikes:5*MSOspikeToCurrentTau; +MSO_CNalphaFunction= (MSOcurrentPerSpike / ... + MSOspikeToCurrentTau)*t.*exp(-t / MSOspikeToCurrentTau); +% show alpha function +% figure(84), subplot(4,2,2), plot(t,MSO_CNalphaFunction) +% title(['LP cutoff ' num2str(MSOdendriteLPfreq)]) + +% convert CN spikes to post-dendritic current +CN_spikes=CNoutputLeft+CNoutputRight; +for i=1:nMSOcells + x= conv2(CN_spikes(i,:), MSO_CNalphaFunction); + inputCurrent(i,:)=x(1:nEpochs); +end + +if MSO_c==0 + % faster computation when threshold is stable (c==0) + for t=1:nEpochs + s=MSO_E>MSO_Th0; + dE = (-MSO_E/MSO_tauM + inputCurrent(:,t)/MSO_cap +... + (MSO_Gk/MSO_cap).*(MSO_Ek-MSO_E))*dtSpikes; + dGk=-MSO_Gk*dtSpikes/MSO_tauGk +MSO_b*s; + MSO_E=MSO_E+dE; + MSO_Gk=MSO_Gk+dGk; + MSOmembranePotential(:,t)=MSO_E+s.*(MSO_Eb-MSO_E)+MSO_Er; + end +else + % threshold is changing (MSO_c>0; e.g. bushy cell) + for t=1:nEpochs + dE = (-MSO_E/MSO_tauM + ... + inputCurrent(:,t)/MSO_cap + (MSO_Gk/MSO_cap)... + .*(MSO_Ek-MSO_E))*dtSpikes; + MSO_E=MSO_E+dE; + s=MSO_E>MSO_Th; + MSOmembranePotential(:,t)=MSO_E+s.*(MSO_Eb-MSO_E)+MSO_Er; + dGk=-MSO_Gk*dtSpikes/MSO_tauGk +MSO_b*s; + MSO_Gk=MSO_Gk+dGk; + + % After a spike, the threshold is raised + % otherwise it settles to its baseline + dTh=-(MSO_Th-MSO_Th0)*dtSpikes/MSO_tauTh +s*MSO_c; + MSO_Th=MSO_Th+dTh; + end +end + +figure(98),subplot(4,1,3) +hold off, imagesc(MSOmembranePotential) +title ('MSO (V)') + +MSOspikes=MSOmembranePotential> -0.01; +% Remove any spike that is immediately followed by a spike +% NB 'find' works on columns (whence the transposing) +MSOspikes=MSOspikes'; +idx=find(MSOspikes); +idx=idx(1:end-1); +MSOspikes(idx+1)=0; +MSOspikes=MSOspikes'; + + +function ICMSOspikes=ICchopper(ICMSOspikes, dtSpikes, ICMSOParams) +%% +ICMSOspikes=sum(ICMSOspikes); +[nICMSOcells nEpochs]=size(ICMSOspikes); +inputCurrent=zeros(nICMSOcells, nEpochs); +ICMSOmembranePotential=inputCurrent; + +ICMSO_tauM=ICMSOParams.tauM; +ICMSO_tauGk=ICMSOParams.tauGk; +ICMSO_tauTh=ICMSOParams.tauTh; +ICMSO_cap=ICMSOParams.Cap; +ICMSO_c=ICMSOParams.c; +ICMSO_b=ICMSOParams.dGkSpike; +ICMSO_Th0=ICMSOParams.Th0; +ICMSO_Ek=ICMSOParams.Ek; +ICMSO_Eb= ICMSOParams.Eb; +ICMSO_Er=ICMSOParams.Er; + +ICMSO_E=zeros(nICMSOcells,1); +ICMSO_Gk=zeros(nICMSOcells,1); +ICMSO_Th=ICMSO_Th0*ones(nICMSOcells,1); + +% Dendritic filtering, all spikes are replaced by CNalphaFunction functions +ICMSOdendriteLPfreq= ICMSOParams.dendriteLPfreq; +ICMSOcurrentPerSpike=ICMSOParams.currentPerSpike; +ICMSOspikeToCurrentTau=1/(2*pi*ICMSOdendriteLPfreq); +t=dtSpikes:dtSpikes:5*ICMSOspikeToCurrentTau; +ICMSOalphaFunction= (ICMSOcurrentPerSpike / ... + ICMSOspikeToCurrentTau)*t.*exp(-t / ICMSOspikeToCurrentTau); +% show alpha function +% figure(84), subplot(4,2,5), plot(t,ICMSOalphaFunction) +% title(['IC MSO LP cutoff ' num2str(ICMSOdendriteLPfreq)]) + +% post-dendritic current +for i=1:nICMSOcells + x= conv2(1*ICMSOspikes(i,:), ICMSOalphaFunction); + inputCurrent(i,:)=x(1:nEpochs); +end + +if ICMSO_c==0 + % faster computation when threshold is stable (c==0) + for t=1:nEpochs + s=ICMSO_E>ICMSO_Th0; + dE = (-ICMSO_E/ICMSO_tauM + inputCurrent(:,t)/ICMSO_cap +... + (ICMSO_Gk/ICMSO_cap).*(ICMSO_Ek-ICMSO_E))*dtSpikes; + dGk=-ICMSO_Gk*dtSpikes/ICMSO_tauGk +ICMSO_b*s; + ICMSO_E=ICMSO_E+dE; + ICMSO_Gk=ICMSO_Gk+dGk; + ICMSOmembranePotential(:,t)=ICMSO_E+s.*(ICMSO_Eb-ICMSO_E)+ICMSO_Er; + end +else + % threshold is changing (ICMSO_c>0; e.g. bushy cell) + for t=1:nEpochs + dE = (-ICMSO_E/ICMSO_tauM + ... + inputCurrent(:,t)/ICMSO_cap + (ICMSO_Gk/ICMSO_cap)... + .*(ICMSO_Ek-ICMSO_E))*dtSpikes; + ICMSO_E=ICMSO_E+dE; + s=ICMSO_E>ICMSO_Th; + ICMSOmembranePotential(:,t)=ICMSO_E+s.*(ICMSO_Eb-ICMSO_E)+ICMSO_Er; + dGk=-ICMSO_Gk*dtSpikes/ICMSO_tauGk +ICMSO_b*s; + ICMSO_Gk=ICMSO_Gk+dGk; + + % After a spike, the threshold is raised + % otherwise it settles to its baseline + dTh=-(ICMSO_Th-ICMSO_Th0)*dtSpikes/ICMSO_tauTh +s*ICMSO_c; + ICMSO_Th=ICMSO_Th+dTh; + end +end + +t=dtSpikes:dtSpikes:dtSpikes*length(ICMSOmembranePotential); +figure(98),subplot(4,2,7) +plot(t, ICMSOmembranePotential) +ylim([-0.07 0]), xlim([0 max(t)]) +title('IC (V)') + +ICMSOspikes=ICMSOmembranePotential> -0.01; +% now remove any spike that is immediately followed by a spike +% NB 'find' works on columns (whence the transposing) +ICMSOspikes=ICMSOspikes'; +idx=find(ICMSOspikes); +idx=idx(1:end-1); +ICMSOspikes(idx+1)=0; +ICMSOspikes=ICMSOspikes'; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsMikaelDeroche/runMAP1_14.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,181 @@ +function runMAP1_14 +% runMAP1_14 is a general purpose test routine that can be adjusted to +% test a number of different applications of MAP1_14 +% +% A range of options are supplied in the early part of the program +% +% #1 +% Identify the file (in 'MAPparamsName') containing the model parameters +% +% #2 +% Identify the kind of model required (in 'AN_spikesOrProbability'). +% A full brainstem model ('spikes') can be computed or a shorter model +% ('probability') that computes only so far as the auditory nerve +% +% #3 +% Choose between a tone signal or file input (in 'signalType') +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the channels in terms of their best frequencies in the vector +% BFlist. +% +% Last minute changes to the parameters can be made using +% the cell array of strings 'paramChanges'. +% Each string must have the same format as the corresponding line in the +% file identified in 'MAPparamsName' + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation: select one +% AN_spikesOrProbability='spikes'; +% or +AN_spikesOrProbability='probability'; + + +%% #3 A. pure tone, B. harmonic sequence or C. speech file input +% comment out unwanted code + +% A. tone +sampleRate= 441000; +signalType= 'tones'; +toneFrequency= 5000; % or a pure tone (Hz) +duration=0.500; % seconds +beginSilence=0.050; +endSilence=0.050; +rampDuration=.005; % raised cosine ramp (seconds) + +% or +% B. harmonic tone (Hz) - useful to demonstrate a broadband sound +% sampleRate= 44100; +% signalType= 'tones'; +% toneFrequency= F0:F0:8000; +% duration=0.500; % seconds +% beginSilence=0.250; +% endSilence=0.250; +% F0=210; +% rampDuration=.005; % raised cosine ramp (seconds) + +% or +% C. signalType= 'file'; +% fileName='twister_44kHz'; + +%% #4 rms level +% signal details +leveldBSPL= 70; % dB SPL (80 for Lieberman) + +%% #5 number of channels in the model +% 21-channel model (log spacing) +numChannels=21; +lowestBF=250; highestBF= 6000; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +% or specify your own channel BFs +% numChannels=1; +% BFlist=toneFrequency; + + +%% #6 change model parameters + +paramChanges={}; + +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read +% This example declares only one fiber type with a calcium clearance time +% constant of 80e-6 s (HSR fiber) when the probability option is selected. +% paramChanges={'AN_IHCsynapseParams.ANspeedUpFactor=5;', ... +% 'IHCpreSynapseParams.tauCa=86e-6; '}; + + + +%% delare 'showMap' options to control graphical output +% see UTIL_showMAP for more options +showMapOptions.printModelParameters=1; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showEfferent=1; % tracks of AR and MOC +showMapOptions.surfProbability=1; % 2D plot of HSR response + +if strcmp(signalType, 'file') + % needed for labeling plot + showMapOptions.fileName=fileName; +else + showMapOptions.fileName=[]; +end + +%% Generate stimuli +switch signalType + case 'tones' + % Create pure tone stimulus + dt=1/sampleRate; % seconds + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + % catch rampTime error + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + intialSilence= zeros(1,round(0.1/dt)); + finalSilence= zeros(1,round(0.2/dt)); + inputSignal= [intialSilence inputSignal' finalSilence]; +end + + +%% run the model +tic +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) +disp('Computing ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +%% the model run is now complete. Now display the results +UTIL_showMAP(showMapOptions, paramChanges) + +if strcmp(signalType,'tones') + disp(['duration=' num2str(duration)]) + disp(['level=' num2str(leveldBSPL)]) + disp(['toneFrequency=' num2str(toneFrequency)]) + global DRNLParams + disp(['attenuation factor =' ... + num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) + disp(['attenuation factor (probability)=' ... + num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) + disp(AN_spikesOrProbability) +end +disp(paramChanges) +toc +path(restorePath) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsMikaelDeroche/testACF.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,260 @@ +% function [P dt lags SACF]= testACF +% testACF is a *script* to demonstrate the smoothed ACF of +% Balaguer-Ballestera, E. Denham, S.L. and Meddis, R. (2008). +% +% Convert this to a *function* by uncommenting the first line +% The function returns the P matrix plotted in Figure 96. +% If a function is used, the following outputs are returned: +% P: smoothed SACF (lags x time matrix) +% dt: time interval between successive columns of P +% lags: lags used in computing P +% SACF: unsmoothed SACFs +% +% A range of options are supplied in the early part of the program +% +% #1 +% Identify the model parameter file (in 'MAPparamsName') +% +% #2 +% Identify the kind of model required (in 'AN_spikesOrProbability') +% 'probability' is recommended for ACF work +% +% #3 +% Choose between a harmonic complex or file input +% by commenting out unwanted code +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the model channel BFs in the vector 'BFlist'. +% +% #6 +% Last minute changes to the model parameters can be made using +% the cell array of strings 'paramChanges'. +% This is used here to control the details of the ACF computations +% Read the notes in this section for more information +% +% displays: +% Figure 97 shows the AN response to the stimulus. this is a channel x time +% display. The z-axis (and colour) is the AN fiber firing rate +% +% Figure 96 shows the P-matrix, the smoothed SACF. +% +% Figure 89 shows a summary of the evolution of the unsmoothed SACF +% over time. If you wish to take a snapshot of the P-matrix at a +% particular time, this figure can help identify when to take it. +% The index on the y-axis, identifies the required row numbers +% of the P or SACF matrix, e.g. P(:,2000) +% +% On request, (filteredSACFParams.plotACFs=1) Figure 89 shows the channel +% by channel ACFs at intervals during the computation as a movie. +% The number of ACF displays is controlled by 'plotACFsInterval' +% and the movie can be slowed or speeded up using 'plotMoviePauses' +% (see paramChanges section below). + +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% This global will find results from MAP1_14 +global savedInputSignal ANprobRateOutput ANoutput dt dtSpikes savedBFlist + % This global,from model parameter file +global filteredSACFParams + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +% User sets up requirements +%% #1 parameter file name +MAPparamsName='Normal'; % recommended + + +%% #2 probability (fast) or spikes (slow) representation: select one +% AN_spikesOrProbability='spikes'; +% or +AN_spikesOrProbability='probability'; % recommended + +%% #3 A. harmonic sequence or B. speech file input +% Comment out unwanted code +% A. harmonic tone (Hz) - useful to demonstrate a broadband sound +sampleRate= 44100; % recommended 44100 +signalType= 'tones'; +duration=0.100; % seconds +beginSilence=0.020; +endSilence=0.020; +rampDuration=.005; % raised cosine ramp (seconds) + +% toneFrequency is a vector of component frequencies +F0=120; +toneFrequency= [3*F0 4*F0 5*F0]; + +% or +% B. file input +signalType= 'file'; +fileName='Oh No'; + +%% #4 rms level +leveldBSPL= 60; % dB SPL (80 for Lieberman) + +%% #5 number of channels in the model +% 21-channel model (log spacing of BFs) +numChannels=21; +lowestBF=250; highestBF= 6000; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +%% #6 change model parameters +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read (see manual) + +% Take control of ACF parameters +% The filteredACF parameters are set in the MAPparamsNormal file +% However, it is convenient to change them here leving the file intacta + minPitch= 80; maxPitch= 500; numPitches=50; + maxLag=1/minPitch; minLag=1/maxPitch; + lags= linspace(minLag, maxLag, numPitches); + +paramChanges={... + 'filteredSACFParams.lags=lags; % autocorrelation lags vector;',... + 'filteredSACFParams.acfTau= 2; % (Wiegrebe) time constant ACF;',... + 'filteredSACFParams.lambda= 0.12; % slower filter to smooth ACF;',... + 'filteredSACFParams.plotACFs=1; % plot ACFs while computing;',... + 'filteredSACFParams.plotACFsInterval=0.01;',... + 'filteredSACFParams.plotMoviePauses=.1; ',... + 'filteredSACFParams.usePressnitzer=0; % attenuates ACF at long lags;',... + 'filteredSACFParams.lagsProcedure= ''useAllLags'';',... + }; + +% Notes: +% acfTau: time constant of unsmoothed ACF +% lambda: time constant of smoothed ACFS +% plotACFs: plot ACFs during computation (0 to switch off, for speed) +% plotACFsInterval: sampling interval for plots +% plotMoviePauses: pause duration between frames to allow viewing +% usePressnitzer: gives low weights to long lags +% lagsProcedure: used to fiddle with output (ignore) + +%% delare 'showMap' options to control graphical output +% see UTIL_showMAP for more options +showMapOptions=[]; +% showMapOptions.showModelOutput=0; % plot of all stages +showMapOptions.surfAN=1; % surface plot of HSR response +showMapOptions.PSTHbinwidth=0.001; % smoothing for PSTH + +if exist('fileName','var') + % needed for labeling plot + showMapOptions.fileName=fileName; +end + +%% Generate stimuli +switch signalType + case 'tones' + % Create tone stimulus + dt=1/sampleRate; % seconds + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + % catch rampTime error + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; +end + +wavplay(inputSignal, sampleRate) + +%% run the model + +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) +disp('Computing ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +%% The model run is now complete. Now display the results +% display the AN response +UTIL_showMAP(showMapOptions) + +% compute ACF +switch AN_spikesOrProbability + case 'probability' + % use only HSR fibers + inputToACF=ANprobRateOutput(end-length(savedBFlist)+1:end,:); + otherwise + inputToACF=ANoutput; + dt=dtSpikes; +end + +disp ('computing ACF...') + +% read paramChanges to get new filteredSACFParams +for i=1:length(paramChanges) +eval(paramChanges{i}); +end + +[P BFlist SACF]= filteredSACF(inputToACF, dt, savedBFlist, filteredSACFParams); +disp(' ACF done.') + +%% plot original waveform on summary/smoothed ACF plot +figure(96), clf +subplot(3,1,3) +t=dt*(1:length(savedInputSignal)); +plot(t,savedInputSignal, 'k') +xlim([0 t(end)]) +title(['stimulus: ' num2str(leveldBSPL, '%4.0f') ' dB SPL']); + +% plot SACF +figure(96) +subplot(2,1,1) +imagesc(P) +colormap bone +ylabel('periodicities (Hz)'), xlabel('time (s)') +title(['smoothed SACF. (periodicity x time)']) +% y-axis specifies pitches (1/lags) +% Force MATLAB to show the lowest pitch +postedYvalues=[1 get(gca,'ytick')]; set(gca,'ytick',postedYvalues) +pitches=1./filteredSACFParams.lags; +set(gca,'ytickLabel', round(pitches(postedYvalues))) +% x-axis is time at which P is samples +[nCH nTimes]=size(P); +t=dt:dt:dt*nTimes; +tt=get(gca,'xtick'); +set(gca,'xtickLabel', round(100*t(tt))/100) + +%% On a new figure show a cascade of SACFs +figure(89), clf +% select 100 samples; +[r c]=size(SACF); +step=round(c/100); +idx=step:step:c; + +UTIL_cascadePlot(SACF(:,idx)', 1./pitches) + +xlabel('lag (s)'), ylabel('time pointer -->') +title(' SACF summary over time') +yValues=get(gca,'yTick'); +set(gca,'yTickLabel', num2str(yValues'*100)) + +path(restorePath) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsRM/MAPdemoMultiChOAE.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,105 @@ +function [frequencies fft_ampdB]= ... + MAPdemoMultiChOAE (leveldBSPL, toneFrequencies) +% MAPdemo runs the MATLAB auditory periphery model +% +% The OAE is simulated by combining the output from all DRNL channels +% +% arguments leveldBSPL and toneFrequencies are optional +% defaults are 70 and [5000 6000] +% +% e.g. +% MAPdemoMultiChOAE (60, [3000 4000]) + + +global dt DRNLoutput +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'utilities'], ... + ['..' filesep 'parameterStore'], ['..' filesep 'wavFileStore'],... + ['..' filesep 'testPrograms']) + +% set parameter file here +paramsName='Normal'; +% choose probability because spikes not used to evaluate BM +AN_spikesOrProbability='probability'; +% add parameter changes here. paramchanges is a cell array of command +% strings +paramChanges={}; + +% DRNL channels +lowestBF=1000; highestBF= 8000; numChannels=41; +% includes BFs at 250 500 1000 2000 4000 8000 (for 11, 21, 31 BFs) +% the output from all these filters will be combined to form the OAE +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +if nargin<2 + toneFrequencies= 2000; % single pure tone test + toneFrequencies=[ 2000 3000]; % F1 F2 for DPOAEs + toneFrequencies=[ 5000 6000]; % F1 F2 +end +duration=0.05; % seconds +duration=0.05; % seconds +rampDuration=.005; + +if nargin<1 + leveldBSPL=70; % dB SPL +end +amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak level) + +% Create pure stimulus +sampleRate= 100000; +dt=1/sampleRate; +time=dt: dt: duration; +inputSignal=sum(sin(2*pi*toneFrequencies'*time), 1); +inputSignal=amp*inputSignal; + +% apply ramps +if rampDuration>0.5*duration, rampDuration=duration/2; end +rampTime=dt:dt:rampDuration; +ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; +inputSignal=inputSignal.*ramp; % at the beginning +ramp=fliplr(ramp); +inputSignal=inputSignal.*ramp; % and at the end + +% add 10 ms silence +silenceDuration=0.01; +silence= zeros(1,round(silenceDuration/dt)); +inputSignal= [silence inputSignal silence]; +time=dt: dt: dt*length(inputSignal); + +%% delare 'showMap' options to control graphical output +showMapOptions.printModelParameters=0; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=0; % tracks of AR and MOC +showMapOptions.surfProbability=0; % 2D plot of HSR response +showMapOptions.surfSpikes=0; % 2D plot of spikes histogram +showMapOptions.ICrates=0; % IC rates by CNtauGk + + +MAP1_14(inputSignal, 1/dt, BFlist, ... + paramsName, AN_spikesOrProbability, paramChanges); + +UTIL_showMAP(showMapOptions, paramChanges) +pause(0.1) + +% use this to produce a comnplete record of model parameters +% UTIL_showAllMAPStructures + +OAE=sum(DRNLoutput); +figure(5),subplot(2,1,1) +plot(time,OAE) +title(['F=' num2str(toneFrequencies)]) +[fft_powerdB, fft_phase, frequencies, fft_ampdB]= UTIL_FFT(OAE, dt, 1e-15); +idx=find(frequencies<1e4); + +figure(5),subplot(2,1,2) +plot(frequencies(idx),fft_ampdB(idx)) +title ('FFT of OAE') +ylabel('dB') +ylim([0 100]) +grid on + +path(restorePath);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsRM/Pavel_MAP1_14.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,189 @@ +function Pavel_MAP1_14 +% test_MAP1_14 is a general purpose test routine that can be adjusted to +% test a number of different applications of MAP1_14 +% +% A range of options are supplied in the early part of the program +% +% One use of the function is to create demonstrations; filenames <demoxx> +% to illustrate particular features +% +% #1 +% Identify the file (in 'MAPparamsName') containing the model parameters +% +% #2 +% Identify the kind of model required (in 'AN_spikesOrProbability'). +% A full brainstem model (spikes) can be computed or a shorter model +% (probability) that computes only so far as the auditory nerve +% +% #3 +% Choose between a tone signal or file input (in 'signalType') +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the channels in terms of their best frequencies in the vector +% BFlist. +% +% Last minute changes to the parameters fetched earlier can be made using +% the cell array of strings 'paramChanges'. +% Each string must have the same format as the corresponding line in the +% file identified in 'MAPparamsName' +% +% When the demonstration is satisfactory, freeze it by renaming it <demoxx> + +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation +AN_spikesOrProbability='spikes'; + +% or +% AN_spikesOrProbability='probability'; +% NB probabilities are not corrected for refractory effects + + +%% #3 pure tone, harmonic sequence or speech file input +signalType= 'tones'; +sampleRate= 100000; +duration=0.1; % seconds +% toneFrequency= 250:250:8000; % harmonic sequence (Hz) +toneFrequency= 1000; % or a pure tone (Hz8 +rampDuration=.005; % seconds + +% or +% signalType= 'file'; +% fileName='twister_44kHz'; + + +%% #4 rms level +% signal details +leveldBSPL= 30; % dB SPL + + +%% #5 number of channels in the model +% 21-channel model (log spacing) +% numChannels=21; +% lowestBF=250; highestBF= 8000; +% BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +% or specify your own channel BFs +numChannels=1; +BFlist=toneFrequency; + + +%% #6 change model parameters +paramChanges=[]; + +% or +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read +% This example declares only one fiber type with a calcium clearance time +% constant of 80e-6 s (HSR fiber) when the probability option is selected. +% It also removes the speed up that normally takes place for AN spikes +% It also increases the number of AN fibers computed to 500. +paramChanges={... + 'AN_IHCsynapseParams.ANspeedUpFactor=1;', ... + 'IHCpreSynapseParams.tauCa=86e-6;',... + 'AN_IHCsynapseParams.numFibers= 500;' }; + + +%% delare 'showMap' options to control graphical output +global showMapOptions + +% or (example: show everything including an smoothed SACF output +showMapOptions.printModelParameters=1; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=0; % tracks of AR and MOC +showMapOptions.surfProbability=0; % 2D plot of HSR response +if strcmp(AN_spikesOrProbability, 'spikes') + % avoid nonsensical options + showMapOptions.surfProbability=0; + showMapOptions.showACF=0; +end +if strcmp(signalType, 'file') + % needed for labeling plot + showMapOptions.fileName=fileName; +else + showMapOptions.fileName=[]; +end + +%% Generate stimuli + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore']) +switch signalType + case 'tones' + inputSignal=createMultiTone(sampleRate, toneFrequency, ... + leveldBSPL, duration, rampDuration); + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + silence= zeros(1,round(0.1/dt)); + inputSignal= [silence inputSignal' silence]; +end + + +%% run the model +tic + +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model']) +disp('Computing ...') + + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); +toc + +% the model run is now complete. Now display the results +% the model run is now complete. Now display the results +disp(' param changes to list of parameters below') +for i=1:length(paramChanges) + disp(paramChanges{i}) +end +UTIL_showMAP(showMapOptions) + +toc +path(restorePath) + + +function inputSignal=createMultiTone(sampleRate, toneFrequency, ... + leveldBSPL, duration, rampDuration) +% Create pure tone stimulus +dt=1/sampleRate; % seconds +time=dt: dt: duration; +inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); +amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) +inputSignal=amp*inputSignal; + +% apply ramps +% catch rampTime error +if rampDuration>0.5*duration, rampDuration=duration/2; end +rampTime=dt:dt:rampDuration; +ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; +inputSignal=inputSignal.*ramp; +ramp=fliplr(ramp); +inputSignal=inputSignal.*ramp; + +% add 10 ms silence +silence= zeros(1,round(0.03/dt)); +inputSignal= [silence inputSignal silence]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsRM/pitchModel_RM.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,215 @@ +function pitchModel_RM +% Modification of testMAP_14 to replicate the pitch model published +% in JASA 2006. +% +% A range of options are supplied in the early part of the program +% +% One use of the function is to create demonstrations; filenames <demoxx> +% to illustrate particular features +% +% #1 +% Identify the file (in 'MAPparamsName') containing the model parameters +% +% #2 +% Identify the kind of model required (in 'AN_spikesOrProbability'). +% A full brainstem model (spikes) can be computed or a shorter model +% (probability) that computes only so far as the auditory nerve +% +% #3 +% Choose between a tone signal or file input (in 'signalType') +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the channels in terms of their best frequencies in the vector +% BFlist. +% +% Last minute changes to the parameters fetched earlier can be made using +% the cell array of strings 'paramChanges'. +% Each string must have the same format as the corresponding line in the +% file identified in 'MAPparamsName' +% +% When the demonstration is satisfactory, freeze it by renaming it <demoxx> + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + + + +% Pitch model modification here +global ICrate % used to collect rate profile from showMAP temporary +rates=[]; F0count=0; + +% F0s=[150 200 250]; % fundamental frequency +% harmonics= 3:5; + +% F0s=[3000]; % fundamental frequency +F0s=50:5:1000; +harmonics= 1; +% F0s=150; +for F0=F0s + F0count=F0count+1; + + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation +AN_spikesOrProbability='spikes'; + +% or +% NB probabilities are not corrected for refractory effects +% AN_spikesOrProbability='probability'; + + +%% #3 pure tone, harmonic sequence or speech file input +signalType= 'tones'; +sampleRate= 50000; +duration=0.50; % seconds +% toneFrequency= 1000; % or a pure tone (Hz8 + +% F0=210; +toneFrequency= F0*harmonics; % harmonic sequence (Hz) + +rampDuration=.005; % raised cosine ramp (seconds) + +% or + +% signalType= 'file'; +% fileName='twister_44kHz'; + + +%% #4 rms level +% signal details +leveldBSPL= 50; % dB SPL + + +%% #5 number of channels in the model +% 21-channel model (log spacing) +numChannels=21; +lowestBF=250; highestBF= 8000; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +% or specify your own channel BFs +% numChannels=1; +BFlist=toneFrequency; +% BFlist=500; + + +%% #6 change model parameters +paramChanges={... + 'MacGregorMultiParams.currentPerSpike=25e-9;'... + 'MacGregorMultiParams.tauGk= [0.1e-3:.00005 : 1e-3];'... +'MacGregorParams.currentPerSpike=40e-9;'... +}; + +%% delare 'showMap' options to control graphical output + +showMapOptions.printModelParameters=0; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=0; % tracks of AR and MOC +showMapOptions.surfProbability=0; % 2D plot of HSR response +showMapOptions.surfSpikes=0; % 2D plot of spikes histogram +showMapOptions.ICrates=1; % IC rates by CNtauGk + +% disable certain silly options +if strcmp(AN_spikesOrProbability, 'spikes') + % avoid nonsensical options + showMapOptions.surfProbability=0; + showMapOptions.showACF=0; +else + showMapOptions.surfSpikes=0; +end +if strcmp(signalType, 'file') + % needed for labeling plot + showMapOptions.fileName=fileName; +else + showMapOptions.fileName=[]; +end + +%% Generate stimuli + +switch signalType + case 'tones' + inputSignal=createMultiTone(sampleRate, toneFrequency, ... + leveldBSPL, duration, rampDuration); + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + silence= zeros(1,round(0.1/dt)); + inputSignal= [silence inputSignal' silence]; +end + + +%% run the model +tic + +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model']) +disp([num2str(F0) ' F0']) +disp('Computing ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +% the model run is now complete. Now display the results +UTIL_showMAP(showMapOptions, paramChanges) + +%% pitch model Collect and analyse data +% ICrate is global and computed in showMAP +% a vector of 'stage4' rates; one value for each tauCNGk +rates=[rates; ICrate]; +figure(92), imagesc(rates) +ylabel ('F0 no'), xlabel('tauGk') +% figure(92), plot(rates), ylim([0 inf]) + +h=figure(99); CNmovie(F0count)=getframe(h); +figure(91), plot(rates'),ylim([0 inf]) +pause (0.1) + +end +%% show results +toc +figure(91), plot(F0s,rates'), xlabel('F0'), ylabel('rate'),ylim([0 inf]) +% figure(99),clf,movie(CNmovie,1,4) +path(restorePath) + + +function inputSignal=createMultiTone(sampleRate, toneFrequency, ... + leveldBSPL, duration, rampDuration) +% Create pure tone stimulus +dt=1/sampleRate; % seconds +time=dt: dt: duration; +inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); +amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) +inputSignal=amp*inputSignal; + +% apply ramps +% catch rampTime error +if rampDuration>0.5*duration, rampDuration=duration/2; end +rampTime=dt:dt:rampDuration; +ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; +inputSignal=inputSignal.*ramp; +ramp=fliplr(ramp); +inputSignal=inputSignal.*ramp; + +% add 10 ms silence +silence= zeros(1,round(0.005/dt)); +inputSignal= [silence inputSignal silence]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsRM/temp.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,134 @@ +function test_speechInNoise + +leveldBSPL= 60; % dB SPL +leveldBSPLNoise=-55; + +paramChanges={}; +% no attenuation +paramChanges={'DRNLParams.rateToAttenuationFactorProb = 0.00; '}; +% fixed attenuation +% paramChanges={'DRNLParams.rateToAttenuationFactorProb = -0.04;'}; +% % dynamic attenuation +paramChanges={'DRNLParams.MOCtauProb =.15;', ... + 'DRNLParams.rateToAttenuationFactorProb = 0.01; '}; +% +fileName='twister_44kHz'; +fileName='1o7a_44kHz'; + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation +AN_spikesOrProbability='spikes'; +% or +AN_spikesOrProbability='probability'; + + +%% #3 speech file input + +beginSilence=.25; +endSilence=0.25; +noiseRampDuration=0.01; + +%% #5 number of channels in the model +% 21-channel model (log spacing) +numChannels=21; +lowestBF=300; highestBF= 6000; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +% or specify your own channel BFs +% numChannels=1; +% BFlist=1000; + + + +%% delare 'showMap' options to control graphical output +showMapOptions.printModelParameters=1; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=1; % tracks of AR and MOC +showMapOptions.surfProbability=1; % 2D plot of HSR response +showMapOptions.surfSpikes=0; % 2D plot of spikes histogram +showMapOptions.ICrates=0; % IC rates by CNtauGk +showMapOptions.PSTHbinwidth=0.002; + +% disable certain silly options +if strcmp(AN_spikesOrProbability, 'spikes') + % avoid nonsensical options + showMapOptions.surfProbability=0; + showMapOptions.showACF=0; +else + showMapOptions.surfSpikes=0; +end + % needed for labeling plot + showMapOptions.fileName=fileName; + +%% Generate stimuli + + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + + % add silences + intialSilence= zeros(1,round(beginSilence*sampleRate)); + finalSilence= zeros(1,round(endSilence*sampleRate)); + inputSignal= [intialSilence inputSignal' finalSilence]; + + [inputNoise sampleRateN]=wavread('babble'); + inputNoise=inputNoise(1:length(inputSignal)); + inputNoise=inputNoise(:,1); + targetRMS=20e-6*10^(leveldBSPLNoise/20); + rms=(mean(inputNoise.^2))^0.5; + amp=targetRMS/rms; + inputNoise=inputNoise*amp; + time=dt: dt: dt*length(inputNoise); + rampTime=dt:dt:noiseRampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*noiseRampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputNoise=inputNoise'.*ramp; + inputSignal=inputSignal+inputNoise; + + + +%% run the model +tic + +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model']) +disp('Computing ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +%% the model run is now complete. Now display the results +UTIL_showMAP(showMapOptions, paramChanges) +figure(97), view([-3 82]) +title(['speech/ noise: ' num2str([leveldBSPL leveldBSPLNoise])]) + +disp(['level=' num2str(leveldBSPL)]) +disp(['noise level=' num2str(leveldBSPLNoise)]) + +global DRNLParams +disp(['attenuation factor =' ... + num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) +disp(['attenuation factor (probability)=' ... + num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) +disp(AN_spikesOrProbability) +disp(paramChanges) +toc +path(restorePath) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsRM/testACF.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,261 @@ +% function [LP_SACF dt lags SACF]= testACF +% testACF is a *script* to demonstrate the smoothed ACF of +% Balaguer-Ballestera, E. Denham, S.L. and Meddis, R. (2008). +% +% Convert this to a *function* by uncommenting the first line +% The function returns the LP_SACF matrix plotted in Figure 96. +% If a function is used, the following outputs are returned: +% LP_SACF: smoothed SACF (lags x time matrix) +% dt: time interval between successive columns of LP_SACF +% lags: lags used in computing LP_SACF +% SACF: unsmoothed SACFs +% +% A range of options are supplied in the early part of the program +% +% #1 +% Identify the model parameter file (in 'MAPparamsName') +% +% #2 +% Identify the kind of model required (in 'AN_spikesOrProbability') +% 'probability' is recommended for ACF work +% +% #3 +% Choose between a harmonic complex or file input +% by commenting out unwanted code +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the model channel BFs in the vector 'BFlist'. +% +% #6 +% Last minute changes to the model parameters can be made using +% the cell array of strings 'paramChanges'. +% This is used here to control the details of the ACF computations +% Read the notes in this section for more information +% +% displays: +% Figure 97 shows the AN response to the stimulus. this is a channel x time +% display. The z-axis (and colour) is the AN fiber firing rate +% +% Figure 96 shows the LP_SACF-matrix, the smoothed SACF. +% +% Figure 89 shows a summary of the evolution of the unsmoothed SACF +% over time. If you wish to take a snapshot of the LP_SACF-matrix at a +% particular time, this figure can help identify when to take it. +% The index on the y-axis, identifies the required row numbers +% of the LP_SACF or SACF matrix, e.g. LP_SACF(:,2000) +% +% On request, (filteredSACFParams.plotACFs=1) Figure 89 shows the channel +% by channel ACFs at intervals during the computation as a movie. +% The number of ACF displays is controlled by 'plotACFsInterval' +% and the movie can be slowed or speeded up using 'plotMoviePauses' +% (see paramChanges section below). + +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% This global will find results from MAP1_14 +global savedInputSignal ANprobRateOutput ANoutput dt dtSpikes savedBFlist +% This global,from model parameter file +global filteredSACFParams + +% User sets up requirements +%% #1 parameter file name +MAPparamsName='Normal'; % recommended + + +%% #2 probability (fast) or spikes (slow) representation: select one +% AN_spikesOrProbability='spikes'; +% or +AN_spikesOrProbability='probability'; % recommended + +%% #3 A. harmonic sequence or B. speech file input +% Comment out unwanted code +% A. harmonic tone (Hz) - useful to demonstrate a broadband sound +sampleRate= 44100; % recommended 44100 +signalType= 'tones'; +duration=0.100; % seconds +beginSilence=0.020; +endSilence=0.020; +rampDuration=.005; % raised cosine ramp (seconds) + +% toneFrequency is a vector of component frequencies +F0=120; +toneFrequency= [3*F0 4*F0 5*F0]; + +% or +% B. file input +% signalType= 'file'; +% fileName='Oh No'; +% fileName='twister_44kHz'; + +%% #4 rms level +leveldBSPL= 100; % dB SPL (80 for Lieberman) + +%% #5 number of channels in the model +% 21-channel model (log spacing of BFs) +numChannels=21; +lowestBF=250; highestBF= 5000; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +%% #6 change model parameters +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read (see manual) + +% Take control of ACF parameters +% The filteredACF parameters are set in the MAPparamsNormal file +% However, it is convenient to change them here leving the file intacta +minPitch= 400; maxPitch= 3000; numPitches=200; +maxLag=1/minPitch; minLag=1/maxPitch; +lags= linspace(minLag, maxLag, numPitches); + +paramChanges={... + 'filteredSACFParams.lags=lags; % autocorrelation lags vector;',... + 'filteredSACFParams.acfTau= 2; % (Wiegrebe) time constant ACF;',... + 'filteredSACFParams.lambda= 0.12; % slower filter to smooth ACF;',... + 'filteredSACFParams.plotACFs=1; % plot ACFs while computing;',... + 'filteredSACFParams.plotACFsInterval=0.01;',... + 'filteredSACFParams.plotMoviePauses=.1; ',... + 'filteredSACFParams.usePressnitzer=0; % attenuates ACF at long lags;',... + 'filteredSACFParams.lagsProcedure= ''useAllLags'';',... + }; + +% Notes: +% acfTau: time constant of unsmoothed ACF +% lambda: time constant of smoothed ACFS +% plotACFs: plot ACFs during computation (0 to switch off, for speed) +% plotACFsInterval: sampling interval for plots +% plotMoviePauses: pause duration between frames to allow viewing +% usePressnitzer: gives low weights to long lags +% lagsProcedure: used to fiddle with output (ignore) + +%% delare 'showMap' options to control graphical output +% see UTIL_showMAP for more options +showMapOptions=[]; +% showMapOptions.showModelOutput=0; % plot of all stages +showMapOptions.surfAN=1; % surface plot of HSR response +showMapOptions.PSTHbinwidth=0.001; % smoothing for PSTH + +if exist('fileName','var') + % needed for labeling plot + showMapOptions.fileName=fileName; +end + +%% Generate stimuli +switch signalType + case 'tones' + % Create tone stimulus + dt=1/sampleRate; % seconds + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + % catch rampTime error + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; +end + +wavplay(inputSignal, sampleRate) + +%% run the model +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) +disp('Computing MAP ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +%% The model run is now complete. Now display the results +% display the AN response +UTIL_showMAP(showMapOptions) + +% compute ACF +switch AN_spikesOrProbability + case 'probability' + % use only HSR fibers + inputToACF=ANprobRateOutput(end-length(savedBFlist)+1:end,:); + otherwise + inputToACF=ANoutput; + dt=dtSpikes; +end + +disp ('computing ACF...') + +% read paramChanges to get new filteredSACFParams +for i=1:length(paramChanges) + eval(paramChanges{i}); +end + +[LP_SACF BFlist SACF]= filteredSACF(inputToACF, dt, savedBFlist, ... + filteredSACFParams); +disp(' ACF done.') + +%% plot original waveform on summary/smoothed ACF plot +figure(96), clf +subplot(3,1,3) +t=dt*(1:length(savedInputSignal)); +plot(t,savedInputSignal, 'k') +xlim([0 t(end)]) +title(['stimulus: ' num2str(leveldBSPL, '%4.0f') ' dB SPL']); + +% plot SACF +figure(96) +subplot(2,1,1) +imagesc(LP_SACF) +colormap bone +ylabel('periodicities (Hz)'), xlabel('time (s)') +title(['smoothed SACF. (periodicity x time)']) +% y-axis specifies pitches (1/lags) +% Force MATLAB to show the lowest pitch +postedYvalues=[1 get(gca,'ytick')]; set(gca,'ytick',postedYvalues) +pitches=1./filteredSACFParams.lags; +set(gca,'ytickLabel', round(pitches(postedYvalues))) +% x-axis is time at which LP_SACF is samples +[nCH nTimes]=size(LP_SACF); +t=dt:dt:dt*nTimes; +tt=get(gca,'xtick'); +set(gca,'xtickLabel', round(100*t(tt))/100) + +%% On a new figure show a cascade of SACFs +figure(89), clf +% select 100 samples; +[r c]=size(SACF); +step=round(c/100); +idx=step:step:c; + +UTIL_cascadePlot(SACF(:,idx)', 1./pitches) + +xlabel('lag (s)'), ylabel('time pointer -->') +title(' SACF summary over time') +yValues=get(gca,'yTick'); +set(gca,'yTickLabel', num2str(yValues'*100)) + +path(restorePath) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsRM/testDPOAE.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,25 @@ +% testDPOAE + +addpath (['..' filesep 'testPrograms']) + +leveldB=60; +f1=3000; +frequencyDiffs=20:20:1000; +result=[]; +frequenciesSoFar=[]; +for f2=f1+frequencyDiffs + [frequencies fft_ampdB]=testDPOAE (leveldB, [f1 f2]); + dpFreq=2*f1-f2; + [a idx]=min((frequencies-dpFreq).^2); + result=[result fft_ampdB(idx)]; + frequenciesSoFar=[frequenciesSoFar dpFreq]; + figure(4), plot(frequenciesSoFar, result) + title(['F1= ' num2str(f1) ' F2= ' ... + num2str(f1+ [min(frequencyDiffs) max(frequencyDiffs)])... + ' leveldB= ' num2str(leveldB)]) + xlabel('DP (2f1- f2) frequency'), ylim([0 100]) +end + +grid on + +disp(result)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsRM/test_Dolan_and_Nuttall.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,298 @@ +function test_Dolan_and_Nuttall +% test_MAP1_14 is a general purpose test routine that can be adjusted to +% test a number of different applications of MAP1_14 +% +% A range of options are supplied in the early part of the program +% +% One use of the function is to create demonstrations; filenames <demoxx> +% to illustrate particular features +% +% #1 +% Identify the file (in 'MAPparamsName') containing the model parameters +% +% #2 +% Identify the kind of model required (in 'AN_spikesOrProbability'). +% A full brainstem model (spikes) can be computed or a shorter model +% (probability) that computes only so far as the auditory nerve +% +% #3 +% Choose between a tone signal or file input (in 'signalType') +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the channels in terms of their best frequencies in the vector +% BFlist. +% +% Last minute changes to the parameters fetched earlier can be made using +% the cell array of strings 'paramChanges'. +% Each string must have the same format as the corresponding line in the +% file identified in 'MAPparamsName' +% +% When the demonstration is satisfactory, freeze it by renaming it <demoxx> + +global dt dtSpikes savedBFlist saveAN_spikesOrProbability saveMAPparamsName... + savedInputSignal OMEextEarPressure TMoutput OMEoutput ARattenuation ... + DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... + IHCoutput ANprobRateOutput ANoutput savePavailable ANtauCas ... + CNtauGk CNoutput ICoutput ICmembraneOutput ICfiberTypeRates ... + MOCattenuation +global OMEParams DRNLParams IHC_cilia_RPParams IHCpreSynapseParams +global AN_IHCsynapseParams MacGregorParams MacGregorMultiParams +global ICrate + + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation +AN_spikesOrProbability='spikes'; +% or +% AN_spikesOrProbability='probability'; + + +%% #3 pure tone, harmonic sequence or speech file input +signalType= 'tones'; +toneFrequency= 4000; % or a pure tone (Hz) + +sampleRate= 44100; % must agree with noise +duration=0.010; % seconds +beginSilence=0.010; +endSilence=0.010; +rampDuration=.001; % raised cosine ramp (seconds) +noiseRampDuration=0.002; + +% or +% harmonic sequence (Hz) +% F0=210; +% toneFrequency= F0:F0:8000; + +% or +% signalType= 'file'; +% fileName='twister_44kHz'; + + + +% %% #4 rms level +% % signal details +% leveldBSPL= 80; % dB SPL (80 for Lieberman) +% leveldBSPLNoise=-30; + +%% #5 number of channels in the model +% 21-channel model (log spacing) +numChannels=21; +lowestBF=250; highestBF= 8000; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +% % or specify your own channel BFs +% numChannels=1; +% BFlist=toneFrequency; + + +%% #6 change model parameters + +paramChanges={}; + +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read +% This example declares only one fiber type with a calcium clearance time +% constant of 80e-6 s (HSR fiber) when the probability option is selected. +% paramChanges={'AN_IHCsynapseParams.ANspeedUpFactor=5;', ... +% 'IHCpreSynapseParams.tauCa=86e-6; '}; +% paramChanges={'DRNLParams.MOCtauProb =.25;', ... +% 'DRNLParams.rateToAttenuationFactorProb = 0.02; '}; + +paramChanges={'AN_IHCsynapseParams.numFibers= 50; ',... + 'DRNLParams.MOCtauProb =.15;', ... + 'DRNLParams.rateToAttenuationFactorProb = 0.00; '}; + +% paramChanges={'AN_IHCsynapseParams.numFibers= 50; ',... +% 'DRNLParams.rateToAttenuationFactorProb = -0.007;'}; + + +%% delare 'showMap' options to control graphical output +showMapOptions.printModelParameters=1; % prints all parameters +showMapOptions.showModelOutput=0; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=1; % tracks of AR and MOC +showMapOptions.surfProbability=1; % 2D plot of HSR response +showMapOptions.surfSpikes=1; % 2D plot of spikes histogram +showMapOptions.ICrates=0; % IC rates by CNtauGk + +% disable certain silly options +if strcmp(AN_spikesOrProbability, 'spikes') + % avoid nonsensical options + showMapOptions.surfProbability=0; + showMapOptions.showACF=0; +end + +if strcmp(signalType, 'file') + % needed for labeling plot + showMapOptions.fileName=fileName; +else + showMapOptions.fileName=[]; +end + +fprintf('\n') +disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) +disp('Computing ...') + +%%systematic +probeLevels=30:10:80; +noiseLevels=[-100 30]; +noRepeats=10; + +% probeLevels=80; +% noiseLevels=[-30]; +% noRepeats=10; + +peakCAPs=zeros(4,length(probeLevels)); + +for noiseCondition=1:length(noiseLevels) + leveldBSPLNoise=noiseLevels(noiseCondition); + levelNo=0; + for probeLevel=probeLevels + leveldBSPL=probeLevel; + levelNo=levelNo+1; + summedCAP=[]; + for repeatNo= 1:noRepeats + disp(['repeat no: ' num2str(repeatNo)]) + %% Generate stimuli + + switch signalType + case 'tones' + % Create pure tone stimulus + dt=1/sampleRate; % seconds + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + % catch rampTime error + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + % [inputNoise sampleRateN]=wavread('babble'); + [inputNoise sampleRateN]=wavread('white noise'); + inputNoise=inputNoise(1:length(inputSignal)); + inputNoise=inputNoise(:,1); + targetRMS=20e-6*10^(leveldBSPLNoise/20); + rms=(mean(inputNoise.^2))^0.5; + amp=targetRMS/rms; + inputNoise=inputNoise*amp; + time=dt: dt: dt*length(inputNoise); + rampTime=dt:dt:noiseRampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*noiseRampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputNoise=inputNoise'.*ramp; + ramp=fliplr(ramp); + inputNoise=inputNoise.*ramp; + + inputSignal=inputSignal+inputNoise; + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + toneOnset=2*beginSilence; + + figure(2), subplot(3,1,1) + time=dt:dt:dt*length(inputSignal); + plot(time,inputSignal,'k') + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + intialSilence= zeros(1,round(0.1/dt)); + finalSilence= zeros(1,round(0.2/dt)); + inputSignal= [intialSilence inputSignal' finalSilence]; + + end + + + %% run the model + tic + + MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + + %% the model run is now complete. Now display the results + % UTIL_showMAP(showMapOptions, paramChanges) + + wholeNerveCAP = UTIL_CAPgenerator... + (ANoutput, dtSpikes, BFlist, AN_IHCsynapseParams.numFibers, 1); + + if isempty(summedCAP) + summedCAP=wholeNerveCAP; + else + summedCAP=summedCAP+wholeNerveCAP; + end + + switch AN_spikesOrProbability + case 'spikes' + ANoutput = sum(ANoutput, 1); + case 'probability' + ANoutput = ANprobRateOutput(13+21,:); + end + figure(2), subplot(3,1,2), plot(ANoutput) + spikeTimes=dtSpikes:dtSpikes:dtSpikes* length(wholeNerveCAP); + figure(2), subplot(3,1,3), plot(spikeTimes,summedCAP/repeatNo) + ylim([-50 50]) + end % repeat + + spikeTimes=dtSpikes:dtSpikes:dtSpikes* length(wholeNerveCAP); + idx=find(spikeTimes>toneOnset & ... + spikeTimes>toneOnset+duration+.005); + averageCAP=summedCAP/repeatNo; + peakCAP=max(averageCAP(idx)); + peakCAPs(noiseCondition,levelNo)=peakCAPs(noiseCondition,levelNo)+ peakCAP; + + if strcmp(signalType,'tones') + disp(['duration=' num2str(duration)]) + disp(['level=' num2str(leveldBSPL)]) + disp(['toneFrequency=' num2str(toneFrequency)]) + disp(['leveldBSPLNoise=' num2str(leveldBSPLNoise)]) + + disp(['attenuation factor =' ... + num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) + disp(['attenuation factor (probability)=' ... + num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) + disp(AN_spikesOrProbability) + end + + + disp([ 'peak CAP ' num2str(peakCAP)]) + + for i=1:length(paramChanges) + disp(paramChanges{i}) + end + end % probe level + figure(9), subplot(3,1,3), plot(probeLevels,peakCAPs) +end % condition +%% + +path(restorePath) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsRM/test_MAP1_14Hopkins.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,194 @@ +function test_MAP1_14Hopkins +% test_MAP1_14 is a general purpose test routine that can be adjusted to +% test a number of different applications of MAP1_14 +% +% A range of options are supplied in the early part of the program +% +% One use of the function is to create demonstrations; filenames <demoxx> +% to illustrate particular features +% +% #1 +% Identify the file (in 'MAPparamsName') containing the model parameters +% +% #2 +% Identify the kind of model required (in 'AN_spikesOrProbability'). +% A full brainstem model (spikes) can be computed or a shorter model +% (probability) that computes only so far as the auditory nerve +% +% #3 +% Choose between a tone signal or file input (in 'signalType') +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the channels in terms of their best frequencies in the vector +% BFlist. +% +% Last minute changes to the parameters fetched earlier can be made using +% the cell array of strings 'paramChanges'. +% Each string must have the same format as the corresponding line in the +% file identified in 'MAPparamsName' +% +% When the demonstration is satisfactory, freeze it by renaming it <demoxx> + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation +AN_spikesOrProbability='spikes'; +% or +AN_spikesOrProbability='probability'; + + +%% #3 pure tone, harmonic sequence or speech file input +signalType= 'tones'; +sampleRate= 50000; +duration=0.100; % seconds +rampDuration=.005; % raised cosine ramp (seconds) +beginSilence=0.050; +endSilence=0.050; +toneFrequency= 1000; % or a pure tone (Hz) + +% or +% harmonic sequence (Hz) +F0=1000/11; +toneFrequency= 10*F0:F0:12*F0; + +% or +% signalType= 'file'; +% fileName='twister_44kHz'; + + + +%% #4 rms level +% signal details +leveldBSPL= [44 50 44]; % dB SPL (80 for Lieberman) +leveldBSPL= [50 50 50]; % dB SPL (80 for Lieberman) +noiseLevel=-35; + +%% #5 number of channels in the model +% 21-channel model (log spacing) +numChannels=21; +lowestBF=500; highestBF= 2000; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +% or specify your own channel BFs +numChannels=1; +BFlist=1000; + + +%% #6 change model parameters + +paramChanges={}; + +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read +% This example declares only one fiber type with a calcium clearance time +% constant of 80e-6 s (HSR fiber) when the probability option is selected. +% paramChanges={'AN_IHCsynapseParams.ANspeedUpFactor=5;', ... +% 'IHCpreSynapseParams.tauCa=86e-6; '}; + + + +%% delare 'showMap' options to control graphical output +showMapOptions.printModelParameters=1; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=0; % tracks of AR and MOC +showMapOptions.surfProbability=1; % 2D plot of HSR response +showMapOptions.surfSpikes=0; % 2D plot of spikes histogram +showMapOptions.ICrates=0; % IC rates by CNtauGk +showMapOptions.PSTHbinwidth=0.001; +showMapOptions.colorbar=0; +showMapOptions.view=[0 90]; + +% disable certain silly options +if strcmp(AN_spikesOrProbability, 'spikes') + % avoid nonsensical options + showMapOptions.surfProbability=0; + showMapOptions.showACF=0; +end + +if strcmp(signalType, 'file') + % needed for labeling plot + showMapOptions.fileName=fileName; +else + showMapOptions.fileName=[]; +end + +%% Generate stimuli + +switch signalType + case 'tones' + % Create pure tone stimulus + dt=1/sampleRate; % seconds + time=dt: dt: duration; + amp=10.^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=sin(2*pi*toneFrequency'*time); + amps=repmat(amp',1,length(time)); + inputSignal=amps.*inputSignal; + inputSignal=sum(inputSignal, 1); + % apply ramps + % catch rampTime error + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + %% TEN noise input simple or mixed + [noise sampleRate]=wavread('TEN.wav'); + dt=1/sampleRate; + noise=noise(:,1); + targetRMS=20e-6*10^(noiseLevel/20); + rms=(mean(noise.^2))^0.5; + amp=targetRMS/rms; + noise=noise*amp; + inputSignal=inputSignal+noise(1:length(inputSignal))'; +end + + +%% run the model +tic +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) +disp('Computing ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +%% the model run is now complete. Now display the results +UTIL_showMAP(showMapOptions, paramChanges) +figure(97) +title (['tones / noise levels: ' num2str([leveldBSPL noiseLevel])]) +if strcmp(signalType,'tones') + disp(['duration=' num2str(duration)]) + disp(['level=' num2str(leveldBSPL)]) + disp(['toneFrequency=' num2str(toneFrequency)]) + global DRNLParams + disp(['attenuation factor =' ... + num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) + disp(['attenuation factor (probability)=' ... + num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) + disp(AN_spikesOrProbability) +end +disp(paramChanges) +toc +path(restorePath) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsRM/test_MAP1_14RAM.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,174 @@ +function test_DRNL_Ruggero97 +% test_DRNL_Ruggero97 attempts to match Ruggero's (1992 and 1997) +% iso-intensity data by fiddling with the parameters + +% # BF is the BF of the filter to be assessed +BF=9000; + +% # test frequencies. check that BF is one of them +% copy Ruggero's test tones as fara as possible +numFs=6; lowestF=4000; highestF= 11030; +toneFrequencyList=round(logspace(log10(lowestF), log10(highestF), numFs)); + +% # parameter file name. this is the base set of parameters +MAPparamsName='Normal'; + +% # probability representation (not directly relevant here as only +% the DRNL output is used +AN_spikesOrProbability='probability'; + +% # tone characteristics +sampleRate= 100000; +duration=0.0200; % Ruggero uses 5, 10, 25 ms tones +rampDuration=0.0015; % raised cosine ramp (seconds) +beginSilence=0.050; +endSilence=0.020; + +% # levels +levels=[3 10:10:80]; + +%% # change model parameters +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read + +paramChanges={}; +% switch off all efferent effects and then play with DRNL params +paramChanges={... + 'DRNLParams.rateToAttenuationFactorProb = 0.00; ',... + 'OMEParams.rateToAttenuationFactorProb=0.0;', ... + 'DRNLParams.ctBMdB = -20;'... + 'DRNLParams.g=1000;'... + 'DRNLParams.linCFs=7000;'... + 'DRNLParams.linBWs=3500;'... + }; + +% delare 'showMap' options to control graphical output +% (not needed but might be useful +% showMapOptions.printModelParameters=0; % prints all parameters +% showMapOptions.showModelOutput=1; % plot of all stages +% showMapOptions.printFiringRates=0; % prints stage activity levels +% showMapOptions.showACF=0; % shows SACF (probability only) +% showMapOptions.showEfferent=0; % tracks of AR and MOC +% showMapOptions.surfProbability=0; % 2D plot of HSR response +% showMapOptions.surfSpikes=0; % 2D plot of spikes histogram +% showMapOptions.ICrates=0; % IC rates by CNtauGk +% showMapOptions.fileName=[]; + +% run the program +global dt DRNLoutput DRNLParams TMoutput +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) +figure(2), clf,figure(3), clf,figure(4), clf, + +peakOutputDisp=zeros(length(levels),length(toneFrequencyList)); +peakStapesDisp=zeros(length(levels),length(toneFrequencyList)); + +%% now vary level and frequency while measuring the response +levelNo=0; +for leveldBSPL=levels + levelNo=levelNo+1; + disp(['level: ' num2str(leveldBSPL)]) + + freqNo=0; + for toneFrequency=toneFrequencyList + freqNo=freqNo+1; + + %% Generate stimuli + dt=1/sampleRate; + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + %% run the model + + MAP1_14(inputSignal, sampleRate, BF, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + peakOutputDisp(levelNo,freqNo)=max(DRNLoutput); + peakStapesDisp(levelNo,freqNo)=max(TMoutput); + + % the model run is complete. Now display the results if debugging + % UTIL_showMAP(showMapOptions, paramChanges) + end % probe frequencies + + % monitor progress + figure(2), loglog(toneFrequencyList, peakOutputDisp), hold on + xlabel('frequency') + ylabel('peak DRNL displacement (m)') + title ('DRNL uncorrected displacement') +end % levels +figure(2),legend(num2str(toneFrequencyList'),'location','northwest') + +% convert from model BM displacement to Ruggero's velocity +DRNLvelocity= peakOutputDisp ... + .*repmat(2*pi*toneFrequencyList,length(levels),1); +% convert from model stapes displacement to Ruggero's velocity +stapesVelocity= peakStapesDisp ... + .*repmat(2*pi*toneFrequencyList,length(levels),1); +% velocity gain is increased velocity attributable to the DRNL +DRNLvelocityGain = DRNLvelocity./stapesVelocity; +DRNLvelocityGain_dB= 20*log10(DRNLvelocityGain ); +% iso-intensity equates all functions at the same outpu for the lowest +% velocity tested +isoIntensityDRNLvel_dB= DRNLvelocityGain_dB- ... + repmat(DRNLvelocityGain_dB(:,1),1,length(toneFrequencyList)); + +% displays +% iso-velocity function (nrmalised by stapes velocity) +figure(3), clf, semilogx(toneFrequencyList, isoIntensityDRNLvel_dB) +ylim([-10 60]) +xlim([2000 20000]) +xlabel('frequency') +ylabel('peak DRNL velocity gain (dB)') +title(['CF= ' num2str(BF) ' Hz']) +legend(num2str(levels'),'location','northwest') + +% velocity I/O function +figure(4), clf, semilogy(levels, (DRNLvelocity*1e6)'), hold on +ylim([1e0 1e4]) +xlim([0 100]) +xlabel('level') +ylabel(' velocity (microm/ s)') +title(['CF= ' num2str(BF) ' Hz']) +legend(num2str(toneFrequencyList'),'location','northwest') + +% command window reports +disp(''), disp('***') +disp(AN_spikesOrProbability) + +% DRNL parameter set +UTIL_showStructureSummary(DRNLParams, 'DRNLParams', 10) + +% stimulus characteristics +disp(['CF=' num2str(BF)]) +disp(['tone Duration=' num2str(rampDuration)]) +disp(['ramp Duration=' num2str(duration)]) + +% particular parameter changes used on this run +for i=1:length(paramChanges) + disp(paramChanges{i}) +end + +% if required dump one or more matrices in tab spaced format +% (suitable for pasting directly into EXCEL. +% UTIL_printTabTable([toneFrequencyList' peakOutputDisp']) + +% leave the path as you found it +path(restorePath) + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsRM/test_MAP1_14RAMworks.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,147 @@ +function test_MAP1_14RAM + +global dt DRNLoutput +global DRNLParams TMoutput +global peakOutputDisp alignedOutput toneFrequencyList levels leveldBSPL logOutput + + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) +figure(2), clf + +%% # BFlist is the BF of the filter to be assessed +BFlist=8000; +numChannels=1; + +% define probe frequencies +numFs=6; +lowestF=BFlist/5; highestF= BFlist*1.4; +lowestF=4000; highestF= 12600; +toneFrequencyList=round(logspace(log10(lowestF), log10(highestF), numFs)); + +%% # parameter file name +MAPparamsName='Normal'; + +%% # probability representation +AN_spikesOrProbability='probability'; + +%% # tone duration +sampleRate= 100000; +duration=0.0200; % seconds +rampDuration=.005; % raised cosine ramp (seconds) +beginSilence=0.050; +endSilence=0.050; + +%% # levels +% probe details +levels=0:10:80; +% levels=80; + +%% # change model parameters +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read + +% adjust linear path gain (previously g=100) +% switch off all efferent effects +paramChanges={... + 'DRNLParams.rateToAttenuationFactorProb = 0.00; ',... + 'OMEParams.rateToAttenuationFactorProb=0.0;', ... + 'DRNLParams.ctBMdB = -10;'... + 'DRNLParams.g=200;'... + 'DRNLParams.linCFs=6500;'... + 'DRNLParams.linBWs=2000;'... + }; +% 'DRNLParams.a=0;'... + + +%% delare 'showMap' options to control graphical output +showMapOptions.printModelParameters=0; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=0; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=0; % tracks of AR and MOC +showMapOptions.surfProbability=0; % 2D plot of HSR response +showMapOptions.surfSpikes=0; % 2D plot of spikes histogram +showMapOptions.ICrates=0; % IC rates by CNtauGk +showMapOptions.fileName=[]; + + +%% now vary level and frequency +peakOutputDisp=zeros(length(levels),length(toneFrequencyList)); +peakStapesDisp=zeros(length(levels),length(toneFrequencyList)); +levelNo=0; +for leveldBSPL=levels + levelNo=levelNo+1; +disp(['level: ' num2str(leveldBSPL)]) + freqNo=0; + for toneFrequency=toneFrequencyList + freqNo=freqNo+1; + + %% Generate stimuli + dt=1/sampleRate; + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + + %% run the model +% fprintf('\n') +% disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) +% disp('Computing ...') + + MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + peakOutputDisp(levelNo,freqNo)=max(DRNLoutput); + peakStapesDisp(levelNo,freqNo)=max(TMoutput); + + %% the model run is complete. Now display the results +% UTIL_showMAP(showMapOptions, paramChanges) + end % probe frequencies +figure(2), loglog(toneFrequencyList, peakOutputDisp), hold on +xlabel('frequency') +ylabel('peak DRNL displacement (m)') + +end % levels +%% alignmed plot + + +x=peakOutputDisp./peakStapesDisp; +logOutput=20*log10(x.*repmat((2*pi*toneFrequencyList),length(levels),1)); +alignedOutput=logOutput-repmat(logOutput(:,1), 1, length(toneFrequencyList)); +figure(3), clf, semilogx(toneFrequencyList, alignedOutput), hold on +ylim([-20 80]) +xlim([2000 20000]) +% legend(num2str(levels')) +xlabel('frequency') +ylabel('peak DRNL velocity (dB)') +title(['level ' num2str(leveldBSPL) ' dB SPL']) + +disp(['level=' num2str(leveldBSPL)]) +disp(['toneFrequency=' num2str(toneFrequency)]) + +disp(['attenuation factor =' ... + num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) +disp(['attenuation factor (probability)=' ... + num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) +disp(AN_spikesOrProbability) +disp(paramChanges) + +% UTIL_printTabTable([toneFrequencyList' peakOutputDisp']) + +path(restorePath) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsRM/test_speechInNoise.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,147 @@ +function test_speechInNoise + +leveldBSPL= 60; % dB SPL +leveldBSPLNoise=-55; + +% leveldBSPL= 90; % test AR +% leveldBSPLNoise=85; +OMEatten=0.1; +OMEatten=0.00; + +MOCattenuation=0.00; % no attenuation +% MOCattenuation=-0.02; % fixed +% MOCattenuation=0.02; % dynamic within-channel +MOCtauProb=0.150; + +paramChanges={}; + +% no attenuation +paramChanges={['DRNLParams.rateToAttenuationFactorProb = '... + num2str(MOCattenuation) ';'],... + ['DRNLParams.MOCtauProb =' num2str(MOCtauProb) ';'],... + ['OMEParams.rateToAttenuationFactorProb=' num2str(OMEatten) ';'] }; + +% +fileName='twister_44kHz'; +% fileName='1o7a_44kHz'; + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation +AN_spikesOrProbability='spikes'; +% or +AN_spikesOrProbability='probability'; + + +%% #3 speech file input + +beginSilence=.25; +endSilence=0.25; +noiseRampDuration=0.01; + +%% #5 number of channels in the model +% 21-channel model (log spacing) +numChannels=21; +lowestBF=300; highestBF= 6000; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +% or specify your own channel BFs +% numChannels=1; +% BFlist=toneFrequency; + + + +%% delare 'showMap' options to control graphical output +showMapOptions.printModelParameters=1; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=0; % tracks of AR and MOC +showMapOptions.surfProbability=1; % 2D plot of HSR response +showMapOptions.surfSpikes=0; % 2D plot of spikes histogram +showMapOptions.ICrates=0; % IC rates by CNtauGk +showMapOptions.PSTHbinwidth=0.010; +showMapOptions.colorbar=0; +showMapOptions.view=[0 90]; + +% disable certain silly options +if strcmp(AN_spikesOrProbability, 'spikes') + % avoid nonsensical options + showMapOptions.surfProbability=0; + showMapOptions.showACF=0; +else + showMapOptions.surfSpikes=0; +end + % needed for labeling plot + showMapOptions.fileName=fileName; + +%% Generate stimuli + + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + + % add silences + intialSilence= zeros(1,round(beginSilence*sampleRate)); + finalSilence= zeros(1,round(endSilence*sampleRate)); + inputSignal= [intialSilence inputSignal' finalSilence]; + + [inputNoise sampleRateN]=wavread('babble'); + inputNoise=inputNoise(1:length(inputSignal)); + inputNoise=inputNoise(:,1); + targetRMS=20e-6*10^(leveldBSPLNoise/20); + rms=(mean(inputNoise.^2))^0.5; + amp=targetRMS/rms; + inputNoise=inputNoise*amp; + time=dt: dt: dt*length(inputNoise); + rampTime=dt:dt:noiseRampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*noiseRampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputNoise=inputNoise'.*ramp; + inputSignal=inputSignal+inputNoise; + + + +%% run the model +tic + +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model']) +disp('Computing ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +%% the model run is now complete. Now display the results +UTIL_showMAP(showMapOptions, paramChanges) +figure(97), +title(['speech/ noise: ' num2str([leveldBSPL leveldBSPLNoise]) ... + '; atten/ tau: ' num2str([MOCattenuation MOCtauProb] )],'fontSize', 14) + +disp(['level=' num2str(leveldBSPL)]) +disp(['noise level=' num2str(leveldBSPLNoise)]) + +global DRNLParams +disp(['attenuation factor =' ... + num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) +disp(['attenuation factor (probability)=' ... + num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) +disp(AN_spikesOrProbability) +disp(paramChanges) +toc +path(restorePath) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsRM/test_speechInNoiseDelmanhorst.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,150 @@ +function test_speechInNoiseDelmanhorst +% as used for Delmanhorst presentation + +leveldBSPL= 60; % dB SPL +leveldBSPLNoise=-55; + +% leveldBSPL= 90; % test AR +% leveldBSPLNoise=85; + +% AR rate to attenuation factor +OMEatten=0.1; +% OMEatten=0.00; + +MOCattenuation=0.00; % no attenuation +% MOCattenuation=-0.02; % fixed +% MOCattenuation=0.02; % dynamic within-channel + +MOCtauProb=0.150; + +paramChanges={}; + +% no attenuation +paramChanges={['DRNLParams.rateToAttenuationFactorProb = '... + num2str(MOCattenuation) ';'],... + ['DRNLParams.MOCtauProb =' num2str(MOCtauProb) ';'],... + ['OMEParams.rateToAttenuationFactorProb=' num2str(OMEatten) ';'] }; + +% +fileName='twister_44kHz'; +% fileName='1o7a_44kHz'; + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation +% AN_spikesOrProbability='spikes'; +% or +AN_spikesOrProbability='probability'; + +%% #3 speech file input + +beginSilence=.25; +endSilence=0.25; +noiseRampDuration=0.01; + +%% #5 number of channels in the model +% 21-channel model (log spacing) +numChannels=21; +lowestBF=300; highestBF= 6000; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +% or specify your own channel BFs +% numChannels=1; +% BFlist=toneFrequency; + + + +%% delare 'showMap' options to control graphical output +showMapOptions.printModelParameters=1; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=0; % tracks of AR and MOC +showMapOptions.surfProbability=1; % 2D plot of HSR response +showMapOptions.surfSpikes=0; % 2D plot of spikes histogram +showMapOptions.ICrates=0; % IC rates by CNtauGk +showMapOptions.PSTHbinwidth=0.010; +showMapOptions.colorbar=0; +showMapOptions.view=[0 90]; + +% disable certain silly options +if strcmp(AN_spikesOrProbability, 'spikes') + % avoid nonsensical options + showMapOptions.surfProbability=0; + showMapOptions.showACF=0; +else + showMapOptions.surfSpikes=0; +end + % needed for labeling plot + showMapOptions.fileName=fileName; + +%% Generate stimuli + + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + + % add silences + intialSilence= zeros(1,round(beginSilence*sampleRate)); + finalSilence= zeros(1,round(endSilence*sampleRate)); + inputSignal= [intialSilence inputSignal' finalSilence]; + + [inputNoise sampleRateN]=wavread('babble'); + inputNoise=inputNoise(1:length(inputSignal)); + inputNoise=inputNoise(:,1); + targetRMS=20e-6*10^(leveldBSPLNoise/20); + rms=(mean(inputNoise.^2))^0.5; + amp=targetRMS/rms; + inputNoise=inputNoise*amp; + time=dt: dt: dt*length(inputNoise); + rampTime=dt:dt:noiseRampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*noiseRampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputNoise=inputNoise'.*ramp; + inputSignal=inputSignal+inputNoise; + + + +%% run the model +tic + +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model']) +disp('Computing ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +%% the model run is now complete. Now display the results +UTIL_showMAP(showMapOptions, paramChanges) +figure(97), +title(['speech/ noise: ' num2str([leveldBSPL leveldBSPLNoise]) ... + '; atten/ tau: ' num2str([MOCattenuation MOCtauProb] )],'fontSize', 14) + +disp(['level=' num2str(leveldBSPL)]) +disp(['noise level=' num2str(leveldBSPLNoise)]) + +global DRNLParams +disp(['attenuation factor =' ... + num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) +disp(['attenuation factor (probability)=' ... + num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) +disp(AN_spikesOrProbability) +disp(paramChanges) +toc +path(restorePath) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsRM/test_toneInNoise.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,225 @@ +function test_toneInNoise +% test_MAP1_14 is a general purpose test routine that can be adjusted to +% test a number of different applications of MAP1_14 +% +% A range of options are supplied in the early part of the program +% +% One use of the function is to create demonstrations; filenames <demoxx> +% to illustrate particular features +% +% #1 +% Identify the file (in 'MAPparamsName') containing the model parameters +% +% #2 +% Identify the kind of model required (in 'AN_spikesOrProbability'). +% A full brainstem model (spikes) can be computed or a shorter model +% (probability) that computes only so far as the auditory nerve +% +% #3 +% Choose between a tone signal or file input (in 'signalType') +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the channels in terms of their best frequencies in the vector +% BFlist. +% +% Last minute changes to the parameters fetched earlier can be made using +% the cell array of strings 'paramChanges'. +% Each string must have the same format as the corresponding line in the +% file identified in 'MAPparamsName' +% +% When the demonstration is satisfactory, freeze it by renaming it <demoxx> + +global dt dtSpikes savedBFlist saveAN_spikesOrProbability saveMAPparamsName... + savedInputSignal OMEextEarPressure TMoutput OMEoutput ARattenuation ... + DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... + IHCoutput ANprobRateOutput ANoutput savePavailable ANtauCas ... + CNtauGk CNoutput ICoutput ICmembraneOutput ICfiberTypeRates ... + MOCattenuation +global OMEParams DRNLParams IHC_cilia_RPParams IHCpreSynapseParams +global AN_IHCsynapseParams MacGregorParams MacGregorMultiParams +global ICrate + + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation +AN_spikesOrProbability='spikes'; +% or +AN_spikesOrProbability='probability'; + + +%% #3 pure tone, harmonic sequence or speech file input +signalType= 'tones'; +sampleRate= 44100; % must agree with noise +duration=0.010; % seconds +rampDuration=.001; % raised cosine ramp (seconds) +beginSilence=0.010; +endSilence=0.010; +toneFrequency= 2000; % or a pure tone (Hz) + +% or +% harmonic sequence (Hz) +% F0=210; +% toneFrequency= F0:F0:8000; + +% or +% signalType= 'file'; +% fileName='twister_44kHz'; + + + +%% #4 rms level +% signal details +leveldBSPL= 70; % dB SPL (80 for Lieberman) +leveldBSPLNoise=30; + +%% #5 number of channels in the model +% 21-channel model (log spacing) +numChannels=21; +lowestBF=250; highestBF= 8000; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +% % or specify your own channel BFs +% numChannels=1; +% BFlist=toneFrequency; + + +%% #6 change model parameters + +paramChanges={}; + +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read +% This example declares only one fiber type with a calcium clearance time +% constant of 80e-6 s (HSR fiber) when the probability option is selected. +% paramChanges={'AN_IHCsynapseParams.ANspeedUpFactor=5;', ... +% 'IHCpreSynapseParams.tauCa=86e-6; '}; +paramChanges={'DRNLParams.MOCtauProb =.25;', ... + 'DRNLParams.rateToAttenuationFactorProb = 0.02; '}; +% paramChanges={'DRNLParams.MOCtauProb =.15;', ... +% 'DRNLParams.rateToAttenuationFactorProb = 0.00; '}; + + +%% delare 'showMap' options to control graphical output +showMapOptions.printModelParameters=1; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=1; % tracks of AR and MOC +showMapOptions.surfProbability=1; % 2D plot of HSR response +showMapOptions.surfSpikes=1; % 2D plot of spikes histogram +showMapOptions.ICrates=0; % IC rates by CNtauGk + +% disable certain silly options +if strcmp(AN_spikesOrProbability, 'spikes') + % avoid nonsensical options + showMapOptions.surfProbability=0; + showMapOptions.showACF=0; +end + +if strcmp(signalType, 'file') + % needed for labeling plot + showMapOptions.fileName=fileName; +else + showMapOptions.fileName=[]; +end + +%% Generate stimuli + +switch signalType + case 'tones' + % Create pure tone stimulus + dt=1/sampleRate; % seconds + time=dt: dt: duration; + inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); + amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) + inputSignal=amp*inputSignal; + % apply ramps + % catch rampTime error + if rampDuration>0.5*duration, rampDuration=duration/2; end + rampTime=dt:dt:rampDuration; + ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; + inputSignal=inputSignal.*ramp; + ramp=fliplr(ramp); + inputSignal=inputSignal.*ramp; + % add silence + intialSilence= zeros(1,round(beginSilence/dt)); + finalSilence= zeros(1,round(endSilence/dt)); + inputSignal= [intialSilence inputSignal finalSilence]; + +% [inputNoise sampleRateN]=wavread('babble'); + [inputNoise sampleRateN]=wavread('white noise'); + inputNoise=inputNoise(1:length(inputSignal)); + inputNoise=inputNoise(:,1); + targetRMS=20e-6*10^(leveldBSPLNoise/20); + rms=(mean(inputNoise.^2))^0.5; + amp=targetRMS/rms; + inputNoise=inputNoise*amp; + inputSignal=inputSignal+inputNoise'; + figure(2), subplot(2,1,1) + time=dt:dt:dt*length(inputSignal); + plot(time,inputSignal,'k') + + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + intialSilence= zeros(1,round(0.1/dt)); + finalSilence= zeros(1,round(0.2/dt)); + inputSignal= [intialSilence inputSignal' finalSilence]; + +end + + +%% run the model +tic +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability]) +disp('Computing ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +%% the model run is now complete. Now display the results +UTIL_showMAP(showMapOptions, paramChanges) + +if strcmp(signalType,'tones') + disp(['duration=' num2str(duration)]) + disp(['level=' num2str(leveldBSPL)]) + disp(['toneFrequency=' num2str(toneFrequency)]) + global DRNLParams + disp(['attenuation factor =' ... + num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ]) + disp(['attenuation factor (probability)=' ... + num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ]) + disp(AN_spikesOrProbability) +end + +figure(2), subplot(2,1,2), plot(ANprobRateOutput(13+21,:)) +disp([ 'peak channel 13: ' num2str(max(ANprobRateOutput(13+21,:)))]) + +for i=1:length(paramChanges) + disp(paramChanges{i}) +end + +path(restorePath) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/IPIHextract.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,181 @@ +function [iih,IPIhisttime,IPIhistweight]=IPIHextract(IFRAN_pattern, sfreq) +% +% tracks the formants according to an analysis proposed in Secker-Walker +% JASA 1990, section V.A +% Tim Jürgens, February 2011, code from Guy Brown included +% +% input: IFRAN_pattern: pattern of the auditory model (dependend on the number of modules used) +% first dimension: frequency channel, +% second dimension: time (samples) +% sfreq: sampling frequency +% output: iih: interpeak-interval histogram, matrix very similar +% the plot 5 in the Secker-Walker paper +% +% +% + + +time_axis = 0:1/sfreq:(size(IFRAN_pattern,2)-1)/sfreq; + +%find how many samples of AN_pattern are 10ms and 3ms +%one_sample_is_a_time_of = time_axis(2); +[tmp, start_time_index] = min(abs(0-time_axis)); +[tmp, stop20_time_index] = min(abs(0.020-time_axis)); +number_of_samples20ms = stop20_time_index - start_time_index; + +[tmp, stop10_time_index] = min(abs(0.010-time_axis)); +number_of_samples10ms = stop10_time_index - start_time_index; +every_10ms = 1:number_of_samples10ms:size(IFRAN_pattern,2)-number_of_samples20ms; + +hamm_window = hamming(11); +halfHamming = (length(hamm_window)-1)/2; + +% window normalization + +norm = conv(ones(1,number_of_samples20ms),hamm_window); +norm = norm(5+1:end-5)'; +win_size = number_of_samples20ms; +half_win_size = floor(win_size/2); +hop_size = number_of_samples10ms; + +%parameters of the autocorrelation +params.acfTau=0.1; +params.lags=[0:1/sfreq:0.02-1/sfreq]; +sampledacf = runningACF(IFRAN_pattern,sfreq,params); +sampledacf(sampledacf<0)=0; +sampledacf = sqrt(sampledacf); + + +%pre-allocation due to speed +%Acorr = zeros(size(IFRAN_pattern,1),size(every_3ms,2),number_of_samples10ms*2+1); +%RAcorr = zeros(size(IFRAN_pattern,1),size(every_3ms,2),number_of_samples10ms*2+1); +%SRAcorr = zeros(size(IFRAN_pattern,1),size(every_3ms,2),number_of_samples10ms*2+1-10); +IPIhisttime = zeros(size(IFRAN_pattern,1),size(every_10ms,2),3); +IPIhistweight = zeros(size(IFRAN_pattern,1),size(every_10ms,2),3); %maximum 3 peaks from the SRA +iih = zeros(half_win_size,size(sampledacf,1)); + + + + + +for iCounter = 1:size(sampledacf,2) %each channel + fprintf('Channel No. %i\n',iCounter); + %time_counter = 1; + %for jCounter = every_3ms %every 3ms time segment + + for frame=1:size(sampledacf,1) + + %%debug + %if iCounter == 130 + % disp('here'); + %end + + + sra = conv(squeeze(sampledacf(frame,iCounter,:)),hamm_window); + sra = sra(halfHamming+1:end-halfHamming)./norm; + df = [0 ; diff(sra)]; + idx = find((df(1:end-1)>=0)&(df(2:end)<0)); + % interpolate + a=df(idx); + b=df(idx+1); + idx = (idx-1+a./(a-b)); + % get rid of a zero peak, if it exists + idx = idx(idx>1); + %include the zeroth peak + idx = [1 idx']'; + % peak values corresponding to these intervals + amp = interp1(1:length(sra),sra,idx,'linear'); + % if required, remove peaks that lie below the mean sra + % note that we disregard the value at zero delay + %if (params.removePeaksBelowMean) + valid = find(amp>1.2*mean(sra(1:floor(length(sra)/2)))); + %valid = find(amp>mean(sra)); + %just take the mean of the first half of the sra as a comparison + idx = idx(valid); + amp = amp(valid); + %end + % only use the first four peaks (three intervals) + idx = idx(1:min(4,length(idx))); + % find the intervals + interval = diff(idx); + % now histogram the intervals + if (~isempty(interval)) + for k=1:length(interval) + if interval(k)<=half_win_size + iih(round(interval(k)),frame) = iih(round(interval(k)),frame)+amp(k); + IPIhisttime(iCounter,frame,k) = interval(k)/sfreq; + IPIhistweight(iCounter,frame,k) = amp(k); + end + end + end + + end + + + + + %% end Guy's code + + + % %take the autocorrelation (ACF) of a 10ms-segment of each channel + % Acorr(iCounter,time_counter,:) = xcorr(IFRAN_pattern(iCounter,jCounter:number_of_samples10ms+jCounter),'biased'); %biased scales the ACF by the reciprocal of the length of the segment + % %root calculation + % RAcorr(iCounter,time_counter,:) = sqrt(abs(Acorr(iCounter,time_counter,:))); + % + % %smoothing using the 11-point hamming window + % for kCounter = 6:size(RAcorr(iCounter,time_counter,:),3)-5 %start with 6 and end with 5 samples + % %less the length of time_axis not to get in conflict with the length of + % %the hamm_window + % SRAcorr(iCounter,time_counter,kCounter-5) = ... + % squeeze(RAcorr(iCounter,time_counter,(kCounter-5):(kCounter+5)))'*hamm_window./sum(hamm_window); + % end + % + % %mean value of actual SRA + % SRA_mean = mean(SRAcorr(iCounter,time_counter,:)); + % + % %find signed zero-crossings of the first derivative (=difference) + % z_crossings_indices = find(diff(sign(diff(squeeze(SRAcorr(iCounter,time_counter,:))))) < 0)+1; %+1 is necessary, because diff shortens vector by 1 + % middle_index = ceil(size(SRAcorr(iCounter,time_counter,:),3)/2); + % + % validCounter = 1; + % valid_z_crossings_indices = []; + % %find valid zero-crossings (peak higher than meanvalue and within first 5 ms of SRA) + % for lCounter = 1:length(z_crossings_indices) + % if (SRAcorr(iCounter,time_counter,z_crossings_indices(lCounter)) > SRA_mean) && ... + % (abs(z_crossings_indices(lCounter)-middle_index) < round(number_of_samples10ms/2)); + % valid_z_crossings_indices(validCounter) = z_crossings_indices(lCounter); + % validCounter = validCounter+1; + % end + % end + % + % %find main peak in the ACF + % [tmp,index_of_z_crossings_main_index] = min(abs(middle_index-valid_z_crossings_indices)); + % if ~tmp == 0 + % disp('middle peak not appropriately found'); + % end + % + % %%% for debugging + % % if iCounter == 130 + % % disp('here'); + % % figure, plot(squeeze(SRAcorr(iCounter,time_counter,:))); + % % hold on, plot([1 length(squeeze(SRAcorr(iCounter,time_counter,:)))],[SRA_mean SRA_mean],'r-'); + % % end + % %%% + % + % %generate IPI-histogram: take the first 3 intervals of SRAcorr + % %(positive delay) in the first 5 ms + % histcounter = 1; + % for lCounter = index_of_z_crossings_main_index+1:min([length(valid_z_crossings_indices(index_of_z_crossings_main_index+1:end)) 3])+index_of_z_crossings_main_index + % sampledifference = abs(valid_z_crossings_indices(lCounter)-valid_z_crossings_indices(lCounter-1)); + % %the difference between two adjacent peaks in the SRA is taken + % %as IPI estimate + % IPIhisttime(iCounter,time_counter,histcounter) = sampledifference*one_sample_is_a_time_of; + % %the amplitude of the SRA at the start of the SRA interval is + % %taken as the IPIweight + % IPIhistweight(iCounter,time_counter,histcounter) = SRAcorr(iCounter,time_counter,valid_z_crossings_indices(lCounter-1)); + % histcounter = histcounter + 1; + % end + + %time_counter = time_counter+1; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/MAP1_14_olde_version_dont_use.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,1269 @@ +function MAP1_14(inputSignal, sampleRate, BFlist, MAPparamsName, ... + AN_spikesOrProbability, paramChanges) +% To test this function use test_MAP1_14 in this folder +% +% example: +% <navigate to 'MAP1_14\MAP'> +% [inputSignal FS] = wavread('../wavFileStore/twister_44kHz'); +% MAP1_14(inputSignal, FS, -1, 'Normal', 'probability', []) +% +% All arguments are mandatory. +% +% BFlist is a vector of BFs but can be '-1' to allow MAPparams to choose +% MAPparamsName='Normal'; % source of model parameters +% AN_spikesOrProbability='spikes'; % or 'probability' +% paramChanges is a cell array of strings that can be used to make last +% minute parameter changes, e.g., to simulate OHC loss +% e.g. paramChanges{1}= 'DRNLParams.a=0;'; % disable OHCs +% e.g. paramchanges={}; % no changes +% The model parameters are established in the MAPparams<***> file +% and stored as global + +restorePath=path; +addpath (['..' filesep 'parameterStore']) + +global OMEParams DRNLParams IHC_cilia_RPParams IHCpreSynapseParams +global AN_IHCsynapseParams MacGregorParams MacGregorMultiParams + +% All of the results of this function are stored as global +global dt ANdt savedBFlist saveAN_spikesOrProbability saveMAPparamsName... + savedInputSignal OMEextEarPressure TMoutput OMEoutput ARattenuation ... + DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... + IHCoutput ANprobRateOutput ANoutput savePavailable ANtauCas ... + CNtauGk CNoutput ICoutput ICmembraneOutput ICfiberTypeRates ... + MOCattenuation + +% Normally only ICoutput(logical spike matrix) or ANprobRateOutput will be +% needed by the user; so the following will suffice +% global ANdt ICoutput ANprobRateOutput + +% Note that sampleRate has not changed from the original function call and +% ANprobRateOutput is sampled at this rate +% However ANoutput, CNoutput and IC output are stored as logical +% 'spike' matrices using a lower sample rate (see ANdt). + +% When AN_spikesOrProbability is set to probability, +% no spike matrices are computed. +% When AN_spikesOrProbability is set to 'spikes', +% no probability output is computed + +% Efferent control variables are ARattenuation and MOCattenuation +% These are scalars between 1 (no attenuation) and 0. +% They are represented with dt=1/sampleRate (not ANdt) +% They are computed using either AN probability rate output +% or IC (spikes) output as approrpriate. +% AR is computed using across channel activity +% MOC is computed on a within-channel basis. + +if nargin<1 + error(' MAP1_14 is not a script but a function that must be called') +end + +if nargin<6 + paramChanges=[]; +end +% Read parameters from MAPparams<***> file in 'parameterStore' folder +% Beware, 'BFlist=-1' is a legitimate argument for MAPparams<> +% It means that the calling program allows MAPparams to specify the list +cmd=['method=MAPparams' MAPparamsName ... + '(BFlist, sampleRate, 0, paramChanges);']; +eval(cmd); +BFlist=DRNLParams.nonlinCFs; + +% save as global for later plotting if required +savedBFlist=BFlist; +saveAN_spikesOrProbability=AN_spikesOrProbability; +saveMAPparamsName=MAPparamsName; + +dt=1/sampleRate; +duration=length(inputSignal)/sampleRate; +% segmentDuration is specified in parameter file (must be >efferent delay) +segmentDuration=method.segmentDuration; +segmentLength=round(segmentDuration/ dt); +segmentTime=dt*(1:segmentLength); % used in debugging plots + +% all spiking activity is computed using longer epochs +ANspeedUpFactor=AN_IHCsynapseParams.ANspeedUpFactor; % e.g.5 times + +% inputSignal must be row vector +[r c]=size(inputSignal); +if r>c, inputSignal=inputSignal'; end % transpose +% ignore stereo signals +inputSignal=inputSignal(1,:); % drop any second channel +savedInputSignal=inputSignal; + +% Segment the signal +% The sgment length is given but the signal length must be adjusted to be a +% multiple of both the segment length and the reduced segmentlength +[nSignalRows signalLength]=size(inputSignal); +segmentLength=ceil(segmentLength/ANspeedUpFactor)*ANspeedUpFactor; +% Make the signal length a whole multiple of the segment length +nSignalSegments=ceil(signalLength/segmentLength); +padSize=nSignalSegments*segmentLength-signalLength; +pad=zeros(nSignalRows,padSize); +inputSignal=[inputSignal pad]; +[ignore signalLength]=size(inputSignal); + +% AN (spikes) is computed at a lower sample rate when spikes required +% so it has a reduced segment length (see 'ANspeeUpFactor' above) +% AN CN and IC all use this sample interval +ANdt=dt*ANspeedUpFactor; +reducedSegmentLength=round(segmentLength/ANspeedUpFactor); +reducedSignalLength= round(signalLength/ANspeedUpFactor); + +%% Initialise with respect to each stage before computing +% by allocating memory, +% by computing constants +% by establishing easy to read variable names +% The computations are made in segments and boundary conditions must +% be established and stored. These are found in variables with +% 'boundary' or 'bndry' in the name + +%% OME --- +% external ear resonances +OMEexternalResonanceFilters=OMEParams.externalResonanceFilters; +[nOMEExtFilters c]=size(OMEexternalResonanceFilters); +% details of external (outer ear) resonances +OMEgaindBs=OMEexternalResonanceFilters(:,1); +OMEgainScalars=10.^(OMEgaindBs/20); +OMEfilterOrder=OMEexternalResonanceFilters(:,2); +OMElowerCutOff=OMEexternalResonanceFilters(:,3); +OMEupperCutOff=OMEexternalResonanceFilters(:,4); +% external resonance coefficients +ExtFilter_b=cell(nOMEExtFilters,1); +ExtFilter_a=cell(nOMEExtFilters,1); +for idx=1:nOMEExtFilters + Nyquist=sampleRate/2; + [b, a] = butter(OMEfilterOrder(idx), ... + [OMElowerCutOff(idx) OMEupperCutOff(idx)]... + /Nyquist); + ExtFilter_b{idx}=b; + ExtFilter_a{idx}=a; +end +OMEExtFilterBndry=cell(2,1); +OMEextEarPressure=zeros(1,signalLength); % pressure at tympanic membrane + +% pressure to velocity conversion using smoothing filter (50 Hz cutoff) +tau=1/(2*pi*50); +a1=dt/tau-1; a0=1; +b0=1+ a1; +TMdisp_b=b0; TMdisp_a=[a0 a1]; +% figure(9), freqz(TMdisp_b, TMdisp_a) +OME_TMdisplacementBndry=[]; + +% OME high pass (simulates poor low frequency stapes response) +OMEhighPassHighCutOff=OMEParams.OMEstapesLPcutoff; +Nyquist=sampleRate/2; +[stapesDisp_b,stapesDisp_a] = butter(1, OMEhighPassHighCutOff/Nyquist, 'high'); +% figure(10), freqz(stapesDisp_b, stapesDisp_a) + +OMEhighPassBndry=[]; + +% OMEampStapes might be reducdant (use OMEParams.stapesScalar) +stapesScalar= OMEParams.stapesScalar; + +% Acoustic reflex +efferentDelayPts=round(OMEParams.ARdelay/dt); +% smoothing filter +a1=dt/OMEParams.ARtau-1; a0=1; +b0=1+ a1; +ARfilt_b=b0; ARfilt_a=[a0 a1]; + +ARattenuation=ones(1,signalLength); +ARrateThreshold=OMEParams.ARrateThreshold; % may not be used +ARrateToAttenuationFactor=OMEParams.rateToAttenuationFactor; +ARrateToAttenuationFactorProb=OMEParams.rateToAttenuationFactorProb; +ARboundary=[]; +ARboundaryProb=0; + +% save complete OME record (stapes displacement) +OMEoutput=zeros(1,signalLength); +TMoutput=zeros(1,signalLength); + +%% BM --- +% BM is represented as a list of locations identified by BF +DRNL_BFs=BFlist; +nBFs= length(DRNL_BFs); + +% DRNLchannelParameters=DRNLParams.channelParameters; +DRNLresponse= zeros(nBFs, segmentLength); + +MOCrateToAttenuationFactor=DRNLParams.rateToAttenuationFactor; +rateToAttenuationFactorProb=DRNLParams.rateToAttenuationFactorProb; +MOCrateThresholdProb=DRNLParams.MOCrateThresholdProb; + +% smoothing filter for MOC +a1=dt/DRNLParams.MOCtau-1; a0=1; +b0=1+ a1; +MOCfilt_b=b0; MOCfilt_a=[a0 a1]; +% figure(9), freqz(stapesDisp_b, stapesDisp_a) +MOCboundary=cell(nBFs,1); +MOCprobBoundary=cell(nBFs,1); + +MOCattSegment=zeros(nBFs,reducedSegmentLength); +MOCattenuation=ones(nBFs,signalLength); + +% if DRNLParams.a>0 +% DRNLcompressionThreshold=10^((1/(1-DRNLParams.c))* ... +% log10(DRNLParams.b/DRNLParams.a)); +% else +% DRNLcompressionThreshold=inf; +% end +DRNLcompressionThreshold=DRNLParams.cTh; +DRNLlinearOrder= DRNLParams.linOrder; +DRNLnonlinearOrder= DRNLParams.nonlinOrder; + +DRNLa=DRNLParams.a; +DRNLb=DRNLParams.b; +DRNLc=DRNLParams.c; +linGAIN=DRNLParams.g; +% +% gammatone filter coefficients for linear pathway +bw=DRNLParams.linBWs'; +phi = 2 * pi * bw * dt; +cf=DRNLParams.linCFs'; +theta = 2 * pi * cf * dt; +cos_theta = cos(theta); +sin_theta = sin(theta); +alpha = -exp(-phi).* cos_theta; +b0 = ones(nBFs,1); +b1 = 2 * alpha; +b2 = exp(-2 * phi); +z1 = (1 + alpha .* cos_theta) - (alpha .* sin_theta) * i; +z2 = (1 + b1 .* cos_theta) - (b1 .* sin_theta) * i; +z3 = (b2 .* cos(2 * theta)) - (b2 .* sin(2 * theta)) * i; +tf = (z2 + z3) ./ z1; +a0 = abs(tf); +a1 = alpha .* a0; +GTlin_a = [b0, b1, b2]; +GTlin_b = [a0, a1]; +GTlinOrder=DRNLlinearOrder; +GTlinBdry=cell(nBFs,GTlinOrder); + +% nonlinear gammatone filter coefficients +bw=DRNLParams.nlBWs'; +phi = 2 * pi * bw * dt; +cf=DRNLParams.nonlinCFs'; +theta = 2 * pi * cf * dt; +cos_theta = cos(theta); +sin_theta = sin(theta); +alpha = -exp(-phi).* cos_theta; +b0 = ones(nBFs,1); +b1 = 2 * alpha; +b2 = exp(-2 * phi); +z1 = (1 + alpha .* cos_theta) - (alpha .* sin_theta) * i; +z2 = (1 + b1 .* cos_theta) - (b1 .* sin_theta) * i; +z3 = (b2 .* cos(2 * theta)) - (b2 .* sin(2 * theta)) * i; +tf = (z2 + z3) ./ z1; +a0 = abs(tf); +a1 = alpha .* a0; +GTnonlin_a = [b0, b1, b2]; +GTnonlin_b = [a0, a1]; +GTnonlinOrder=DRNLnonlinearOrder; +GTnonlinBdry1=cell(nBFs, GTnonlinOrder); +GTnonlinBdry2=cell(nBFs, GTnonlinOrder); + +% complete BM record (BM displacement) +DRNLoutput=zeros(nBFs, signalLength); + + +%% IHC --- +% IHC cilia activity and receptor potential +% viscous coupling between BM and stereocilia displacement +% Nyquist=sampleRate/2; +% IHCcutoff=1/(2*pi*IHC_cilia_RPParams.tc); +% [IHCciliaFilter_b,IHCciliaFilter_a]=... +% butter(1, IHCcutoff/Nyquist, 'high'); +a1=dt/IHC_cilia_RPParams.tc-1; a0=1; +b0=1+ a1; +% high pass (i.e. low pass reversed) +IHCciliaFilter_b=[a0 a1]; IHCciliaFilter_a=b0; +% figure(9), freqz(IHCciliaFilter_b, IHCciliaFilter_a) + +IHCciliaBndry=cell(nBFs,1); + +% IHC apical conductance (Boltzman function) +IHC_C= IHC_cilia_RPParams.C; +IHCu0= IHC_cilia_RPParams.u0; +IHCu1= IHC_cilia_RPParams.u1; +IHCs0= IHC_cilia_RPParams.s0; +IHCs1= IHC_cilia_RPParams.s1; +IHCGmax= IHC_cilia_RPParams.Gmax; +IHCGa= IHC_cilia_RPParams.Ga; % (leakage) + +IHCGu0 = IHCGa+IHCGmax./(1+exp(IHCu0/IHCs0).*(1+exp(IHCu1/IHCs1))); +IHCrestingCiliaCond=IHCGu0; + +% Receptor potential +IHC_Cab= IHC_cilia_RPParams.Cab; +IHC_Gk= IHC_cilia_RPParams.Gk; +IHC_Et= IHC_cilia_RPParams.Et; +IHC_Ek= IHC_cilia_RPParams.Ek; +IHC_Ekp= IHC_Ek+IHC_Et*IHC_cilia_RPParams.Rpc; + +IHCrestingV= (IHC_Gk*IHC_Ekp+IHCGu0*IHC_Et)/(IHCGu0+IHC_Gk); + +IHC_Vnow= IHCrestingV*ones(nBFs,1); % initial voltage +IHC_RP= zeros(nBFs,segmentLength); + +% complete record of IHC receptor potential (V) +IHCciliaDisplacement= zeros(nBFs,segmentLength); +IHCoutput= zeros(nBFs,signalLength); +IHC_cilia_output= zeros(nBFs,signalLength); + + +%% pre-synapse --- +% Each BF is replicated using a different fiber type to make a 'channel' +% The number of channels is nBFs x nANfiberTypes +% Fiber types are specified in terms of tauCa +nANfiberTypes= length(IHCpreSynapseParams.tauCa); +ANtauCas= IHCpreSynapseParams.tauCa; +nANchannels= nANfiberTypes*nBFs; +synapticCa= zeros(nANchannels,segmentLength); + +% Calcium control (more calcium, greater release rate) +ECa=IHCpreSynapseParams.ECa; +gamma=IHCpreSynapseParams.gamma; +beta=IHCpreSynapseParams.beta; +tauM=IHCpreSynapseParams.tauM; +mICa=zeros(nANchannels,segmentLength); +GmaxCa=IHCpreSynapseParams.GmaxCa; +synapse_z= IHCpreSynapseParams.z; +synapse_power=IHCpreSynapseParams.power; + +% tauCa vector is established across channels to allow vectorization +% (one tauCa per channel). Do not confuse with ANtauCas (one pre fiber type) +tauCa=repmat(ANtauCas, nBFs,1); +tauCa=reshape(tauCa, nANchannels, 1); + +% presynapse startup values (vectors, length:nANchannels) +% proportion (0 - 1) of Ca channels open at IHCrestingV +mICaCurrent=((1+beta^-1 * exp(-gamma*IHCrestingV))^-1)... + *ones(nBFs*nANfiberTypes,1); +% corresponding startup currents +ICaCurrent= (GmaxCa*mICaCurrent.^3) * (IHCrestingV-ECa); +CaCurrent= ICaCurrent.*tauCa; + +% vesicle release rate at startup (one per channel) +% kt0 is used only at initialisation +kt0= -synapse_z * CaCurrent.^synapse_power; + + +%% AN --- +% each row of the AN matrices represents one AN fiber +% The results computed either for probabiities *or* for spikes (not both) +% Spikes are necessary if CN and IC are to be computed +nFibersPerChannel= AN_IHCsynapseParams.numFibers; +nANfibers= nANchannels*nFibersPerChannel; +AN_refractory_period= AN_IHCsynapseParams.refractory_period; + +y=AN_IHCsynapseParams.y; +l=AN_IHCsynapseParams.l; +x=AN_IHCsynapseParams.x; +r=AN_IHCsynapseParams.r; +M=round(AN_IHCsynapseParams.M); + +% probability (NB initial 'P' on everything) +PAN_ydt = repmat(AN_IHCsynapseParams.y*dt, nANchannels,1); +PAN_ldt = repmat(AN_IHCsynapseParams.l*dt, nANchannels,1); +PAN_xdt = repmat(AN_IHCsynapseParams.x*dt, nANchannels,1); +PAN_rdt = repmat(AN_IHCsynapseParams.r*dt, nANchannels,1); +PAN_rdt_plus_ldt = PAN_rdt + PAN_ldt; +PAN_M=round(AN_IHCsynapseParams.M); + +% compute starting values +Pcleft = kt0* y* M ./ (y*(l+r)+ kt0* l); +Pavailable = Pcleft*(l+r)./kt0; +Preprocess = Pcleft*r/x; % canbe fractional + +ANprobability=zeros(nANchannels,segmentLength); +ANprobRateOutput=zeros(nANchannels,signalLength); +lengthAbsRefractoryP= round(AN_refractory_period/dt); +cumANnotFireProb=ones(nANchannels,signalLength); +% special variables for monitoring synaptic cleft (specialists only) +savePavailableSeg=zeros(nANchannels,segmentLength); +savePavailable=zeros(nANchannels,signalLength); + +% spikes % ! ! ! ! ! ! ! ! +lengthAbsRefractory= round(AN_refractory_period/ANdt); + +AN_ydt= repmat(AN_IHCsynapseParams.y*ANdt, nANfibers,1); +AN_ldt= repmat(AN_IHCsynapseParams.l*ANdt, nANfibers,1); +AN_xdt= repmat(AN_IHCsynapseParams.x*ANdt, nANfibers,1); +AN_rdt= repmat(AN_IHCsynapseParams.r*ANdt, nANfibers,1); +AN_rdt_plus_ldt= AN_rdt + AN_ldt; +AN_M= round(AN_IHCsynapseParams.M); + +% kt0 is initial release rate +% Establish as a vector (length=channel x number of fibers) +kt0= repmat(kt0', nFibersPerChannel, 1); +kt0=reshape(kt0, nANfibers,1); + +% starting values for reservoirs +AN_cleft = kt0* y* M ./ (y*(l+r)+ kt0* l); +AN_available = round(AN_cleft*(l+r)./kt0); %must be integer +AN_reprocess = AN_cleft*r/x; + +% output is in a logical array spikes = 1/ 0. +ANspikes= false(nANfibers,reducedSegmentLength); +ANoutput= false(nANfibers,reducedSignalLength); + + +%% CN (first brain stem nucleus - could be any subdivision of CN) +% Input to a CN neuorn is a random selection of AN fibers within a channel +% The number of AN fibers used is ANfibersFanInToCN +% CNtauGk (Potassium time constant) determines the rate of firing of +% the unit when driven hard by a DC input (not normally >350 sp/s) +% If there is more than one value, everything is replicated accordingly + +ANavailableFibersPerChan=AN_IHCsynapseParams.numFibers; +ANfibersFanInToCN=MacGregorMultiParams.fibersPerNeuron; + +CNtauGk=MacGregorMultiParams.tauGk; % row vector of CN types (by tauGk) +nCNtauGk=length(CNtauGk); + +% the total number of 'channels' is now greater +% 'channel' is defined as collections of units with the same parameters +% i.e. same BF, same ANtau, same CNtauGk +nCNchannels=nANchannels*nCNtauGk; + +nCNneuronsPerChannel=MacGregorMultiParams.nNeuronsPerBF; +tauGk=repmat(CNtauGk, nCNneuronsPerChannel,1); +tauGk=reshape(tauGk,nCNneuronsPerChannel*nCNtauGk,1); + +% Now the number of neurons has been increased +nCNneurons=nCNneuronsPerChannel*nCNchannels; +CNmembranePotential=zeros(nCNneurons,reducedSegmentLength); + +% establish which ANfibers (by name) feed into which CN nuerons +CNinputfiberLists=zeros(nANchannels*nCNneuronsPerChannel, ANfibersFanInToCN); +unitNo=1; +for ch=1:nANchannels + % Each channel contains a number of units =length(listOfFanInValues) + for idx=1:nCNneuronsPerChannel + for idx2=1:nCNtauGk + fibersUsed=(ch-1)*ANavailableFibersPerChan + ... + ceil(rand(1,ANfibersFanInToCN)* ANavailableFibersPerChan); + CNinputfiberLists(unitNo,:)=fibersUsed; + unitNo=unitNo+1; + end + end +end + +% input to CN units +AN_PSTH=zeros(nCNneurons,reducedSegmentLength); + +% Generate CNalphaFunction function +% by which spikes are converted to post-synaptic currents +CNdendriteLPfreq= MacGregorMultiParams.dendriteLPfreq; +CNcurrentPerSpike=MacGregorMultiParams.currentPerSpike; +CNspikeToCurrentTau=1/(2*pi*CNdendriteLPfreq); +t=ANdt:ANdt:5*CNspikeToCurrentTau; +CNalphaFunction= (1 / ... + CNspikeToCurrentTau)*t.*exp(-t /CNspikeToCurrentTau); +CNalphaFunction=CNalphaFunction*CNcurrentPerSpike; + +% figure(98), plot(t,CNalphaFunction) +% working memory for implementing convolution + +CNcurrentTemp=... + zeros(nCNneurons,reducedSegmentLength+length(CNalphaFunction)-1); +% trailing alphas are parts of humps carried forward to the next segment +CNtrailingAlphas=zeros(nCNneurons,length(CNalphaFunction)); + +CN_tauM=MacGregorMultiParams.tauM; +CN_tauTh=MacGregorMultiParams.tauTh; +CN_cap=MacGregorMultiParams.Cap; +CN_c=MacGregorMultiParams.c; +CN_b=MacGregorMultiParams.dGkSpike; +CN_Ek=MacGregorMultiParams.Ek; +CN_Eb= MacGregorMultiParams.Eb; +CN_Er=MacGregorMultiParams.Er; +CN_Th0= MacGregorMultiParams.Th0; +CN_E= zeros(nCNneurons,1); +CN_Gk= zeros(nCNneurons,1); +CN_Th= MacGregorMultiParams.Th0*ones(nCNneurons,1); +CN_Eb=CN_Eb.*ones(nCNneurons,1); +CN_Er=CN_Er.*ones(nCNneurons,1); +CNtimeSinceLastSpike=zeros(nCNneurons,1); +% tauGk is the main distinction between neurons +% in fact they are all the same in the standard model +tauGk=repmat(tauGk,nANchannels,1); + +CNoutput=false(nCNneurons,reducedSignalLength); + + +%% MacGregor (IC - second nucleus) -------- +nICcells=nANchannels*nCNtauGk; % one cell per channel +CN_PSTH=zeros(nICcells ,reducedSegmentLength); + +% ICspikeWidth=0.00015; % this may need revisiting +% epochsPerSpike=round(ICspikeWidth/ANdt); +% if epochsPerSpike<1 +% error(['MacGregorMulti: sample rate too low to support ' ... +% num2str(ICspikeWidth*1e6) ' microsec spikes']); +% end + +% short names +IC_tauM=MacGregorParams.tauM; +IC_tauGk=MacGregorParams.tauGk; +IC_tauTh=MacGregorParams.tauTh; +IC_cap=MacGregorParams.Cap; +IC_c=MacGregorParams.c; +IC_b=MacGregorParams.dGkSpike; +IC_Th0=MacGregorParams.Th0; +IC_Ek=MacGregorParams.Ek; +IC_Eb= MacGregorParams.Eb; +IC_Er=MacGregorParams.Er; + +IC_E=zeros(nICcells,1); +IC_Gk=zeros(nICcells,1); +IC_Th=IC_Th0*ones(nICcells,1); + +% Dendritic filtering, all spikes are replaced by CNalphaFunction functions +ICdendriteLPfreq= MacGregorParams.dendriteLPfreq; +ICcurrentPerSpike=MacGregorParams.currentPerSpike; +ICspikeToCurrentTau=1/(2*pi*ICdendriteLPfreq); +t=ANdt:ANdt:3*ICspikeToCurrentTau; +IC_CNalphaFunction= (ICcurrentPerSpike / ... + ICspikeToCurrentTau)*t.*exp(-t / ICspikeToCurrentTau); +% figure(98), plot(t,IC_CNalphaFunction) + +% working space for implementing alpha function +ICcurrentTemp=... + zeros(nICcells,reducedSegmentLength+length(IC_CNalphaFunction)-1); +ICtrailingAlphas=zeros(nICcells, length(IC_CNalphaFunction)); + +ICfiberTypeRates=zeros(nANfiberTypes,reducedSignalLength); +ICoutput=false(nICcells,reducedSignalLength); + +ICmembranePotential=zeros(nICcells,reducedSegmentLength); +ICmembraneOutput=zeros(nICcells,signalLength); + + +%% Main program %% %% %% %% %% %% %% %% %% %% %% %% %% %% + +% Compute the entire model for each segment +segmentStartPTR=1; +reducedSegmentPTR=1; % when sampling rate is reduced +while segmentStartPTR<signalLength + segmentEndPTR=segmentStartPTR+segmentLength-1; + % shorter segments after speed up. + shorterSegmentEndPTR=reducedSegmentPTR+reducedSegmentLength-1; + + inputPressureSegment=inputSignal... + (:,segmentStartPTR:segmentStartPTR+segmentLength-1); + + % segment debugging plots + % figure(98) + % plot(segmentTime,inputPressureSegment), title('signalSegment') + + + % OME ---------------------- + + % OME Stage 1: external resonances. Add to inputSignal pressure wave + y=inputPressureSegment; + for n=1:nOMEExtFilters + % any number of resonances can be used + [x OMEExtFilterBndry{n}] = ... + filter(ExtFilter_b{n},ExtFilter_a{n},... + inputPressureSegment, OMEExtFilterBndry{n}); + x= x* OMEgainScalars(n); + % This is a parallel resonance so add it + y=y+x; + end + inputPressureSegment=y; + OMEextEarPressure(segmentStartPTR:segmentEndPTR)= inputPressureSegment; + + % OME stage 2: convert input pressure (velocity) to + % tympanic membrane(TM) displacement using low pass filter + [TMdisplacementSegment OME_TMdisplacementBndry] = ... + filter(TMdisp_b,TMdisp_a,inputPressureSegment, ... + OME_TMdisplacementBndry); + % and save it + TMoutput(segmentStartPTR:segmentEndPTR)= TMdisplacementSegment; + + % OME stage 3: middle ear high pass effect to simulate stapes inertia + [stapesDisplacement OMEhighPassBndry] = ... + filter(stapesDisp_b,stapesDisp_a,TMdisplacementSegment, ... + OMEhighPassBndry); + + % OME stage 4: apply stapes scalar + stapesDisplacement=stapesDisplacement*stapesScalar; + + % OME stage 5: acoustic reflex stapes attenuation + % Attenuate the TM response using feedback from LSR fiber activity + if segmentStartPTR>efferentDelayPts + stapesDisplacement= stapesDisplacement.*... + ARattenuation(segmentStartPTR-efferentDelayPts:... + segmentEndPTR-efferentDelayPts); + end + + % segment debugging plots + % figure(98) + % plot(segmentTime, stapesDisplacement), title ('stapesDisplacement') + + % and save + OMEoutput(segmentStartPTR:segmentEndPTR)= stapesDisplacement; + + + %% BM ------------------------------ + % Each location is computed separately + for BFno=1:nBFs + + % *linear* path + linOutput = stapesDisplacement * linGAIN; % linear gain + + for order = 1 : GTlinOrder + [linOutput GTlinBdry{BFno,order}] = ... + filter(GTlin_b(BFno,:), GTlin_a(BFno,:), linOutput, ... + GTlinBdry{BFno,order}); + end + + % *nonLinear* path + % efferent attenuation (0 <> 1) + if segmentStartPTR>efferentDelayPts + MOC=MOCattenuation(BFno, segmentStartPTR-efferentDelayPts:... + segmentEndPTR-efferentDelayPts); + else % no MOC available yet + MOC=ones(1, segmentLength); + end + % apply MOC to nonlinear input function + nonlinOutput=stapesDisplacement.* MOC; + + % first gammatone filter (nonlin path) + for order = 1 : GTnonlinOrder + [nonlinOutput GTnonlinBdry1{BFno,order}] = ... + filter(GTnonlin_b(BFno,:), GTnonlin_a(BFno,:), ... + nonlinOutput, GTnonlinBdry1{BFno,order}); + end + +% % original broken stick instantaneous compression +% y= nonlinOutput.* DRNLa; % linear section. +% % compress parts of the signal above the compression threshold +% abs_x = abs(nonlinOutput); +% idx=find(abs_x>DRNLcompressionThreshold); +% if ~isempty(idx)>0 +% y(idx)=sign(y(idx)).* (DRNLb*abs_x(idx).^DRNLc); +% end +% nonlinOutput=y; + + + % new broken stick instantaneous compression + y= nonlinOutput.* DRNLa; % linear section attenuation/gain. + % compress parts of the signal above the compression threshold +% holdY=y; + abs_y = abs(y); + idx=find(abs_y>DRNLcompressionThreshold); + if ~isempty(idx)>0 +% y(idx)=sign(y(idx)).* (DRNLcompressionThreshold + ... +% (abs_y(idx)-DRNLcompressionThreshold).^DRNLc); + y(idx)=sign(y(idx)).* (DRNLcompressionThreshold + ... + (abs_y(idx)-DRNLcompressionThreshold)*DRNLc); + end + nonlinOutput=y; + +% % Boltzmann compression +% y=(nonlinOutput * DRNLa*100000); +% holdY=y; +% y=abs(y); +% s=10; u=0.0; +% x=1./(1+exp(-(y-u)/s))-0.5; +% nonlinOutput=sign(nonlinOutput).*x/10000; + + +% if segmentStartPTR==10*segmentLength+1 +% figure(90) +% plot(holdY,'b'), hold on +% plot(nonlinOutput, 'r'), hold off +% ylim([-1e-5 1e-5]) +% pause(1) +% end + + % second filter removes distortion products + for order = 1 : GTnonlinOrder + [ nonlinOutput GTnonlinBdry2{BFno,order}] = ... + filter(GTnonlin_b(BFno,:), GTnonlin_a(BFno,:), ... + nonlinOutput, GTnonlinBdry2{BFno,order}); + end + + % combine the two paths to give the DRNL displacement + DRNLresponse(BFno,:)=linOutput+nonlinOutput; +% disp(num2str(max(linOutput))) + end % BF + + % segment debugging plots + % figure(98) + % if size(DRNLresponse,1)>3 + % imagesc(DRNLresponse) % matrix display + % title('DRNLresponse'); % single or double channel response + % else + % plot(segmentTime, DRNLresponse) + % end + + % and save it + DRNLoutput(:, segmentStartPTR:segmentEndPTR)= DRNLresponse; + + + %% IHC ------------------------------------ + % BM displacement to IHCciliaDisplacement is a high-pass filter + % because of viscous coupling + for idx=1:nBFs + [IHCciliaDisplacement(idx,:) IHCciliaBndry{idx}] = ... + filter(IHCciliaFilter_b,IHCciliaFilter_a, ... + DRNLresponse(idx,:), IHCciliaBndry{idx}); + end + + % apply scalar + IHCciliaDisplacement=IHCciliaDisplacement* IHC_C; + + % and save it + IHC_cilia_output(:,segmentStartPTR:segmentStartPTR+segmentLength-1)=... + IHCciliaDisplacement; + + % compute apical conductance + G=IHCGmax./(1+exp(-(IHCciliaDisplacement-IHCu0)/IHCs0).*... + (1+exp(-(IHCciliaDisplacement-IHCu1)/IHCs1))); + Gu=G + IHCGa; + + % Compute receptor potential + for idx=1:segmentLength + IHC_Vnow=IHC_Vnow+ (-Gu(:, idx).*(IHC_Vnow-IHC_Et)-... + IHC_Gk*(IHC_Vnow-IHC_Ekp))* dt/IHC_Cab; + IHC_RP(:,idx)=IHC_Vnow; + end + + % segment debugging plots + % if size(IHC_RP,1)>3 + % surf(IHC_RP), shading interp, title('IHC_RP') + % else + % plot(segmentTime, IHC_RP) + % end + + % and save it + IHCoutput(:, segmentStartPTR:segmentStartPTR+segmentLength-1)=IHC_RP; + + + %% synapse ----------------------------- + % Compute the vesicle release rate for each fiber type at each BF + % replicate IHC_RP for each fiber type + Vsynapse=repmat(IHC_RP, nANfiberTypes,1); + + % look-up table of target fraction channels open for a given IHC_RP + mICaINF= 1./( 1 + exp(-gamma * Vsynapse) /beta); + % fraction of channel open - apply time constant + for idx=1:segmentLength + % mICaINF is the current 'target' value of mICa + mICaCurrent=mICaCurrent+(mICaINF(:,idx)-mICaCurrent)*dt./tauM; + mICa(:,idx)=mICaCurrent; + end + + ICa= (GmaxCa* mICa.^3) .* (Vsynapse- ECa); + + for idx=1:segmentLength + CaCurrent=CaCurrent + ICa(:,idx)*dt - CaCurrent*dt./tauCa; + synapticCa(:,idx)=CaCurrent; + end + synapticCa=-synapticCa; % treat synapticCa as positive substance + + % NB vesicleReleaseRate is /s and is independent of dt + vesicleReleaseRate = synapse_z * synapticCa.^synapse_power; % rate + + % segment debugging plots + % if size(vesicleReleaseRate,1)>3 + % surf(vesicleReleaseRate), shading interp, title('vesicleReleaseRate') + % else + % plot(segmentTime, vesicleReleaseRate) + % end + + + %% AN + switch AN_spikesOrProbability + case 'probability' + % No refractory effect is applied + for t = 1:segmentLength; + M_Pq=PAN_M-Pavailable; + M_Pq(M_Pq<0)=0; + Preplenish = M_Pq .* PAN_ydt; + Pejected = Pavailable.* vesicleReleaseRate(:,t)*dt; + Preprocessed = M_Pq.*Preprocess.* PAN_xdt; + + ANprobability(:,t)= min(Pejected,1); + reuptakeandlost= PAN_rdt_plus_ldt .* Pcleft; + reuptake= PAN_rdt.* Pcleft; + + Pavailable= Pavailable+ Preplenish- Pejected+ Preprocessed; + Pcleft= Pcleft + Pejected - reuptakeandlost; + Preprocess= Preprocess + reuptake - Preprocessed; + Pavailable(Pavailable<0)=0; + savePavailableSeg(:,t)=Pavailable; % synapse tracking + + end + + % and save it as *rate* + ANrate=ANprobability/dt; + ANprobRateOutput(:, segmentStartPTR:... + segmentStartPTR+segmentLength-1)= ANrate; + % monitor synapse contents (only sometimes used) + savePavailable(:, segmentStartPTR:segmentStartPTR+segmentLength-1)=... + savePavailableSeg; + + %% Apply refractory effect + % the probability of a spike's occurring in the preceding + % refractory window (t= tnow-refractory period to tnow-) + % pFired= 1 - II(1-p(t)), + % we need a running account of cumProb=II(1-p(t)) + % cumProb(t)= cumProb(t-1)*(1-p(t))/(1-p(t-refracPeriod)) + % cumProb(0)=0 + % pFired(t)= 1-cumProb(t) + % This gives the fraction of firing events that must be + % discounted because of a firing event in the refractory + % period + % p(t)= ANprobOutput(t) * pFired(t) + % where ANprobOutput is the uncorrected firing probability + % based on vesicle release rate + % NB this covers only the absoute refractory period + % not the relative refractory period. To approximate this it + % is necessary to extend the refractory period by 50% + + + for t = segmentStartPTR:segmentEndPTR; + if t>1 + ANprobRateOutput(:,t)= ANprobRateOutput(:,t)... + .* cumANnotFireProb(:,t-1); + end + % add recent and remove distant probabilities + refrac=round(lengthAbsRefractoryP * 1.5); + if t>refrac + cumANnotFireProb(:,t)= cumANnotFireProb(:,t-1)... + .*(1-ANprobRateOutput(:,t)*dt)... + ./(1-ANprobRateOutput(:,t-refrac)*dt); + end + end +% figure(88), plot(cumANnotFireProb'), title('cumNotFire') +% figure(89), plot(ANprobRateOutput'), title('ANprobRateOutput') + + %% Estimate efferent effects. ARattenuation (0 <> 1) + % acoustic reflex + [r c]=size(ANrate); + if r>nBFs % Only if LSR fibers are computed + ARAttSeg=mean(ANrate(1:nBFs,:),1); %LSR channels are 1:nBF + % smooth + [ARAttSeg, ARboundaryProb] = ... + filter(ARfilt_b, ARfilt_a, ARAttSeg, ARboundaryProb); + ARAttSeg=ARAttSeg-ARrateThreshold; + ARAttSeg(ARAttSeg<0)=0; % prevent negative strengths + ARattenuation(segmentStartPTR:segmentEndPTR)=... + (1-ARrateToAttenuationFactorProb.* ARAttSeg); + end + % plot(ARattenuation) + + % MOC attenuation based on within-channel HSR fiber activity + HSRbegins=nBFs*(nANfiberTypes-1)+1; + rates=ANrate(HSRbegins:end,:); + if rateToAttenuationFactorProb<0 + % negative factor implies a fixed attenuation + MOCattenuation(:,segmentStartPTR:segmentEndPTR)= ... + ones(size(rates))* -rateToAttenuationFactorProb; + else + for idx=1:nBFs + [smoothedRates, MOCprobBoundary{idx}] = ... + filter(MOCfilt_b, MOCfilt_a, rates(idx,:), ... + MOCprobBoundary{idx}); + smoothedRates=smoothedRates-MOCrateThresholdProb; + smoothedRates=max(smoothedRates, 0); + + x=(1- smoothedRates* rateToAttenuationFactorProb); + x=max(x, 10^(-30/20)); + MOCattenuation(idx,segmentStartPTR:segmentEndPTR)= x; + end + end + MOCattenuation(MOCattenuation<0)=0.001; + + % plot(MOCattenuation) + + + case 'spikes' + ANtimeCount=0; + % implement speed upt + for t = ANspeedUpFactor:ANspeedUpFactor:segmentLength; + ANtimeCount=ANtimeCount+1; + % convert release rate to probabilities + releaseProb=vesicleReleaseRate(:,t)*ANdt; + % releaseProb is the release probability per channel + % but each channel has many synapses + releaseProb=repmat(releaseProb',nFibersPerChannel,1); + releaseProb=reshape(releaseProb, nFibersPerChannel*nANchannels,1); + + % AN_available=round(AN_available); % vesicles must be integer, (?needed) + M_q=AN_M- AN_available; % number of missing vesicles + M_q(M_q<0)= 0; % cannot be less than 0 + + % AN_N1 converts probability to discrete events + % it considers each event that might occur + % (how many vesicles might be released) + % and returns a count of how many were released + + % slow line +% probabilities= 1-(1-releaseProb).^AN_available; + probabilities= 1-intpow((1-releaseProb), AN_available); + ejected= probabilities> rand(length(AN_available),1); + + reuptakeandlost = AN_rdt_plus_ldt .* AN_cleft; + reuptake = AN_rdt.* AN_cleft; + + % slow line +% probabilities= 1-(1-AN_reprocess.*AN_xdt).^M_q; + probabilities= 1-intpow((1-AN_reprocess.*AN_xdt), M_q); + reprocessed= probabilities>rand(length(M_q),1); + + % slow line +% probabilities= 1-(1-AN_ydt).^M_q; + probabilities= 1-intpow((1-AN_ydt), M_q); + + replenish= probabilities>rand(length(M_q),1); + + AN_available = AN_available + replenish - ejected ... + + reprocessed; + AN_cleft = AN_cleft + ejected - reuptakeandlost; + AN_reprocess = AN_reprocess + reuptake - reprocessed; + + % ANspikes is logical record of vesicle release events>0 + ANspikes(:, ANtimeCount)= ejected; + end % t + + % zero any events that are preceded by release events ... + % within the refractory period + % The refractory period consist of two periods + % 1) the absolute period where no spikes occur + % 2) a relative period where a spike may occur. This relative + % period is realised as a variable length interval + % where the length is chosen at random + % (esentially a linear ramp up) + + % Andreas has a fix for this + for t = 1:ANtimeCount-2*lengthAbsRefractory; + % identify all spikes across fiber array at time (t) + % idx is a list of channels where spikes occurred + % ?? try sparse matrices? + idx=find(ANspikes(:,t)); + for j=idx % consider each spike + % specify variable refractory period + % between abs and 2*abs refractory period + nPointsRefractory=lengthAbsRefractory+... + round(rand*lengthAbsRefractory); + % disable spike potential for refractory period + % set all values in this range to 0 + ANspikes(j,t+1:t+nPointsRefractory)=0; + end + end %t + + % segment debugging + % plotInstructions.figureNo=98; + % plotInstructions.displaydt=ANdt; + % plotInstructions.numPlots=1; + % plotInstructions.subPlotNo=1; + % UTIL_plotMatrix(ANspikes, plotInstructions); + + % and save it. NB, AN is now on 'speedUp' time + ANoutput(:, reducedSegmentPTR: shorterSegmentEndPTR)=ANspikes; + + + %% CN Macgregor first neucleus ------------------------------- + % input is from AN so ANdt is used throughout + % Each CNneuron has a unique set of input fibers selected + % at random from the available AN fibers (CNinputfiberLists) + + % Create the dendritic current for that neuron + % First get input spikes to this neuron + synapseNo=1; + for ch=1:nCNchannels + for idx=1:nCNneuronsPerChannel + % determine candidate fibers for this unit + fibersUsed=CNinputfiberLists(synapseNo,:); + % ANpsth has a bin width of ANdt + % (just a simple sum across fibers) + AN_PSTH(synapseNo,:) = ... + sum(ANspikes(fibersUsed,:), 1); + synapseNo=synapseNo+1; + end + end + + % One alpha function per spike + [alphaRows alphaCols]=size(CNtrailingAlphas); + + for unitNo=1:nCNneurons + CNcurrentTemp(unitNo,:)= ... + conv2(AN_PSTH(unitNo,:),CNalphaFunction); + end +% disp(['sum(AN_PSTH)= ' num2str(sum(AN_PSTH(1,:)))]) + % add post-synaptic current left over from previous segment + CNcurrentTemp(:,1:alphaCols)=... + CNcurrentTemp(:,1:alphaCols)+ CNtrailingAlphas; + + % take post-synaptic current for this segment + CNcurrentInput= CNcurrentTemp(:, 1:reducedSegmentLength); +% disp(['mean(CNcurrentInput)= ' num2str(mean(CNcurrentInput(1,:)))]) + + % trailingalphas are the ends of the alpha functions that + % spill over into the next segment + CNtrailingAlphas= ... + CNcurrentTemp(:, reducedSegmentLength+1:end); + + if CN_c>0 + % variable threshold condition (slow) + for t=1:reducedSegmentLength + CNtimeSinceLastSpike=CNtimeSinceLastSpike-ANdt; + s=CN_E>CN_Th & CNtimeSinceLastSpike<0 ; + CNtimeSinceLastSpike(s)=0.0005; % 0.5 ms for sodium spike + dE =(-CN_E/CN_tauM + ... + CNcurrentInput(:,t)/CN_cap+(... + CN_Gk/CN_cap).*(CN_Ek-CN_E))*ANdt; + dGk=-CN_Gk*ANdt./tauGk + CN_b*s; + dTh=-(CN_Th-CN_Th0)*ANdt/CN_tauTh + CN_c*s; + CN_E=CN_E+dE; + CN_Gk=CN_Gk+dGk; + CN_Th=CN_Th+dTh; + CNmembranePotential(:,t)=CN_E+s.*(CN_Eb-CN_E)+CN_Er; + end + else + % static threshold (faster) + E=zeros(1,reducedSegmentLength); + Gk=zeros(1,reducedSegmentLength); + ss=zeros(1,reducedSegmentLength); + for t=1:reducedSegmentLength + % time of previous spike moves back in time + CNtimeSinceLastSpike=CNtimeSinceLastSpike-ANdt; + % action potential if E>threshold + % allow time for s to reset between events + s=CN_E>CN_Th0 & CNtimeSinceLastSpike<0 ; + ss(t)=s(1); + CNtimeSinceLastSpike(s)=0.0005; % 0.5 ms for sodium spike + dE = (-CN_E/CN_tauM + ... + CNcurrentInput(:,t)/CN_cap +... + (CN_Gk/CN_cap).*(CN_Ek-CN_E))*ANdt; + dGk=-CN_Gk*ANdt./tauGk +CN_b*s; + CN_E=CN_E+dE; + CN_Gk=CN_Gk+dGk; + E(t)=CN_E(1); + Gk(t)=CN_Gk(1); + % add spike to CN_E and add resting potential (-60 mV) + CNmembranePotential(:,t)=CN_E +s.*(CN_Eb-CN_E)+CN_Er; + end + end +% disp(['CN_E= ' num2str(sum(CN_E(1,:)))]) +% disp(['CN_Gk= ' num2str(sum(CN_Gk(1,:)))]) +% disp(['CNmembranePotential= ' num2str(sum(CNmembranePotential(1,:)))]) +% plot(CNmembranePotential(1,:)) + + + % extract spikes. A spike is a substantial upswing in voltage + CN_spikes=CNmembranePotential> -0.02; +% disp(['CNspikesbefore= ' num2str(sum(sum(CN_spikes)))]) + + % now remove any spike that is immediately followed by a spike + % NB 'find' works on columns (whence the transposing) + % for each spike put a zero in the next epoch + CN_spikes=CN_spikes'; + idx=find(CN_spikes); + idx=idx(1:end-1); + CN_spikes(idx+1)=0; + CN_spikes=CN_spikes'; +% disp(['CNspikes= ' num2str(sum(sum(CN_spikes)))]) + + % segment debugging + % plotInstructions.figureNo=98; + % plotInstructions.displaydt=ANdt; + % plotInstructions.numPlots=1; + % plotInstructions.subPlotNo=1; + % UTIL_plotMatrix(CN_spikes, plotInstructions); + + % and save it + CNoutput(:, reducedSegmentPTR:shorterSegmentEndPTR)=... + CN_spikes; + + + %% IC ---------------------------------------------- + % MacGregor or some other second order neurons + + % combine CN neurons in same channel (BF x AN tau x CNtau) + % i.e. same BF, same tauCa, same CNtau + % to generate inputs to single IC unit + channelNo=0; + for idx=1:nCNneuronsPerChannel: ... + nCNneurons-nCNneuronsPerChannel+1; + channelNo=channelNo+1; + CN_PSTH(channelNo,:)=... + sum(CN_spikes(idx:idx+nCNneuronsPerChannel-1,:)); + end + + [alphaRows alphaCols]=size(ICtrailingAlphas); + for ICneuronNo=1:nICcells + ICcurrentTemp(ICneuronNo,:)= ... + conv2(CN_PSTH(ICneuronNo,:), IC_CNalphaFunction); + end + + % add the unused current from the previous convolution + ICcurrentTemp(:,1:alphaCols)=ICcurrentTemp(:,1:alphaCols)... + + ICtrailingAlphas; + % take what is required and keep the trailing part for next time + inputCurrent=ICcurrentTemp(:, 1:reducedSegmentLength); + ICtrailingAlphas=ICcurrentTemp(:, reducedSegmentLength+1:end); + + if IC_c==0 + % faster computation when threshold is stable (c==0) + for t=1:reducedSegmentLength + s=IC_E>IC_Th0; + dE = (-IC_E/IC_tauM + inputCurrent(:,t)/IC_cap +... + (IC_Gk/IC_cap).*(IC_Ek-IC_E))*ANdt; + dGk=-IC_Gk*ANdt/IC_tauGk +IC_b*s; + IC_E=IC_E+dE; + IC_Gk=IC_Gk+dGk; + ICmembranePotential(:,t)=IC_E+s.*(IC_Eb-IC_E)+IC_Er; + end + else + % threshold is changing (IC_c>0; e.g. bushy cell) + for t=1:reducedSegmentLength + dE = (-IC_E/IC_tauM + ... + inputCurrent(:,t)/IC_cap + (IC_Gk/IC_cap)... + .*(IC_Ek-IC_E))*ANdt; + IC_E=IC_E+dE; + s=IC_E>IC_Th; + ICmembranePotential(:,t)=IC_E+s.*(IC_Eb-IC_E)+IC_Er; + dGk=-IC_Gk*ANdt/IC_tauGk +IC_b*s; + IC_Gk=IC_Gk+dGk; + + % After a spike, the threshold is raised + % otherwise it settles to its baseline + dTh=-(IC_Th-Th0)*ANdt/IC_tauTh +s*IC_c; + IC_Th=IC_Th+dTh; + end + end + + ICspikes=ICmembranePotential> -0.01; + % now remove any spike that is immediately followed by a spike + % NB 'find' works on columns (whence the transposing) + ICspikes=ICspikes'; + idx=find(ICspikes); + idx=idx(1:end-1); + ICspikes(idx+1)=0; + ICspikes=ICspikes'; + + nCellsPerTau= nICcells/nANfiberTypes; + firstCell=1; + lastCell=nCellsPerTau; + for tauCount=1:nANfiberTypes + % separate rates according to fiber types + % currently only the last segment is saved + ICfiberTypeRates(tauCount, ... + reducedSegmentPTR:shorterSegmentEndPTR)=... + sum(ICspikes(firstCell:lastCell, :))... + /(nCellsPerTau*ANdt); + firstCell=firstCell+nCellsPerTau; + lastCell=lastCell+nCellsPerTau; + end + + ICoutput(:,reducedSegmentPTR:shorterSegmentEndPTR)=ICspikes; + + % store membrane output on original dt scale + % do this for single channel models only + % and only for the HSR-driven IC cells + if round(nICcells/length(ANtauCas))==1 % single channel + % select HSR driven cells + x= ICmembranePotential(length(ANtauCas),:); + % restore original dt + x= repmat(x, ANspeedUpFactor,1); + x= reshape(x,1,segmentLength); + if nANfiberTypes>1 % save HSR and LSR + y=repmat(ICmembranePotential(end,:),... + ANspeedUpFactor,1); + y= reshape(y,1,segmentLength); + x=[x; y]; + end + ICmembraneOutput(:, segmentStartPTR:segmentEndPTR)= x; + end + + % estimate efferent effects. + % ARis based on LSR units. LSR channels are 1:nBF + if nANfiberTypes>1 % AR is multi-channel only + ARAttSeg=sum(ICspikes(1:nBFs,:),1)/ANdt; + [ARAttSeg, ARboundary] = ... + filter(ARfilt_b, ARfilt_a, ARAttSeg, ARboundary); + ARAttSeg=ARAttSeg-ARrateThreshold; + ARAttSeg(ARAttSeg<0)=0; % prevent negative strengths + % scale up to dt from ANdt + x= repmat(ARAttSeg, ANspeedUpFactor,1); + x=reshape(x,1,segmentLength); + ARattenuation(segmentStartPTR:segmentEndPTR)=... + (1-ARrateToAttenuationFactor* x); + ARattenuation(ARattenuation<0)=0.001; + else + % single channel model; disable AR + ARattenuation(segmentStartPTR:segmentEndPTR)=... + ones(1,segmentLength); + end + + % MOC attenuation using HSR response only + % Separate MOC effect for each BF + HSRbegins=nBFs*(nANfiberTypes-1)+1; + rates=ICspikes(HSRbegins:end,:)/ANdt; + for idx=1:nBFs + [smoothedRates, MOCboundary{idx}] = ... + filter(MOCfilt_b, MOCfilt_a, rates(idx,:), ... + MOCboundary{idx}); + % spont 'rates' is zero for IC + MOCattSegment(idx,:)=smoothedRates; + % expand timescale back to model dt from ANdt + x= repmat(MOCattSegment(idx,:), ANspeedUpFactor,1); + x= reshape(x,1,segmentLength); + MOCattenuation(idx,segmentStartPTR:segmentEndPTR)= ... + (1- MOCrateToAttenuationFactor* x); + end + MOCattenuation(MOCattenuation<0)=0.04; + % segment debugging + % plotInstructions.figureNo=98; + % plotInstructions.displaydt=ANdt; + % plotInstructions.numPlots=1; + % plotInstructions.subPlotNo=1; + % UTIL_plotMatrix(ICspikes, plotInstructions); + + end % AN_spikesOrProbability + segmentStartPTR=segmentStartPTR+segmentLength; + reducedSegmentPTR=reducedSegmentPTR+reducedSegmentLength; + + +end % segment + +%% apply refractory correction to spike probabilities + +% the probability of a spike's having occurred in the preceding +% refractory window +% pFired= 1 - II(1-p(t)), t= tnow-refractory period: tnow-1 +% we need a running account of cumProb=II(1-p(t)) +% cumProb(t)= cumProb(t-1)*(1-p(t-1))/(1-p(t-refracPeriod)) +% cumProb(0)=0 +% pFired(t)= 1-cumProb(t) +% whence +% p(t)= ANprobOutput(t) * pFired(t) +% where ANprobOutput is the uncorrected probability + + +% switch AN_spikesOrProbability +% case 'probability' +% ANprobOutput=ANprobRateOutput*dt; +% [r nEpochs]=size(ANprobOutput); +% % find probability of no spikes in refractory period +% pNoSpikesInRefrac=ones(size(ANprobOutput)); +% pSpike=zeros(size(ANprobOutput)); +% for epochNo=lengthAbsRefractoryP+2:nEpochs +% pNoSpikesInRefrac(:,epochNo)=... +% pNoSpikesInRefrac(:,epochNo-2)... +% .*(1-pSpike(:,epochNo-1))... +% ./(1-pSpike(:,epochNo-lengthAbsRefractoryP-1)); +% pSpike(:,epochNo)= ANprobOutput(:,epochNo)... +% .*pNoSpikesInRefrac(:,epochNo); +% end +% ANprobRateOutput=pSpike/dt; +% end + +path(restorePath)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/Pavel_MAP1_14.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,189 @@ +function Pavel_MAP1_14 +% test_MAP1_14 is a general purpose test routine that can be adjusted to +% test a number of different applications of MAP1_14 +% +% A range of options are supplied in the early part of the program +% +% One use of the function is to create demonstrations; filenames <demoxx> +% to illustrate particular features +% +% #1 +% Identify the file (in 'MAPparamsName') containing the model parameters +% +% #2 +% Identify the kind of model required (in 'AN_spikesOrProbability'). +% A full brainstem model (spikes) can be computed or a shorter model +% (probability) that computes only so far as the auditory nerve +% +% #3 +% Choose between a tone signal or file input (in 'signalType') +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the channels in terms of their best frequencies in the vector +% BFlist. +% +% Last minute changes to the parameters fetched earlier can be made using +% the cell array of strings 'paramChanges'. +% Each string must have the same format as the corresponding line in the +% file identified in 'MAPparamsName' +% +% When the demonstration is satisfactory, freeze it by renaming it <demoxx> + +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation +AN_spikesOrProbability='spikes'; + +% or +% AN_spikesOrProbability='probability'; +% NB probabilities are not corrected for refractory effects + + +%% #3 pure tone, harmonic sequence or speech file input +signalType= 'tones'; +sampleRate= 100000; +duration=0.1; % seconds +% toneFrequency= 250:250:8000; % harmonic sequence (Hz) +toneFrequency= 1000; % or a pure tone (Hz8 +rampDuration=.005; % seconds + +% or +% signalType= 'file'; +% fileName='twister_44kHz'; + + +%% #4 rms level +% signal details +leveldBSPL= 30; % dB SPL + + +%% #5 number of channels in the model +% 21-channel model (log spacing) +% numChannels=21; +% lowestBF=250; highestBF= 8000; +% BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +% or specify your own channel BFs +numChannels=1; +BFlist=toneFrequency; + + +%% #6 change model parameters +paramChanges=[]; + +% or +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read +% This example declares only one fiber type with a calcium clearance time +% constant of 80e-6 s (HSR fiber) when the probability option is selected. +% It also removes the speed up that normally takes place for AN spikes +% It also increases the number of AN fibers computed to 500. +paramChanges={... + 'AN_IHCsynapseParams.ANspeedUpFactor=1;', ... + 'IHCpreSynapseParams.tauCa=86e-6;',... + 'AN_IHCsynapseParams.numFibers= 500;' }; + + +%% delare 'showMap' options to control graphical output +global showMapOptions + +% or (example: show everything including an smoothed SACF output +showMapOptions.printModelParameters=1; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=0; % tracks of AR and MOC +showMapOptions.surfProbability=0; % 2D plot of HSR response +if strcmp(AN_spikesOrProbability, 'spikes') + % avoid nonsensical options + showMapOptions.surfProbability=0; + showMapOptions.showACF=0; +end +if strcmp(signalType, 'file') + % needed for labeling plot + showMapOptions.fileName=fileName; +else + showMapOptions.fileName=[]; +end + +%% Generate stimuli + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore']) +switch signalType + case 'tones' + inputSignal=createMultiTone(sampleRate, toneFrequency, ... + leveldBSPL, duration, rampDuration); + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + silence= zeros(1,round(0.1/dt)); + inputSignal= [silence inputSignal' silence]; +end + + +%% run the model +tic + +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model']) +disp('Computing ...') + + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); +toc + +% the model run is now complete. Now display the results +% the model run is now complete. Now display the results +disp(' param changes to list of parameters below') +for i=1:length(paramChanges) + disp(paramChanges{i}) +end +UTIL_showMAP(showMapOptions) + +toc +path(restorePath) + + +function inputSignal=createMultiTone(sampleRate, toneFrequency, ... + leveldBSPL, duration, rampDuration) +% Create pure tone stimulus +dt=1/sampleRate; % seconds +time=dt: dt: duration; +inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); +amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) +inputSignal=amp*inputSignal; + +% apply ramps +% catch rampTime error +if rampDuration>0.5*duration, rampDuration=duration/2; end +rampTime=dt:dt:rampDuration; +ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; +inputSignal=inputSignal.*ramp; +ramp=fliplr(ramp); +inputSignal=inputSignal.*ramp; + +% add 10 ms silence +silence= zeros(1,round(0.03/dt)); +inputSignal= [silence inputSignal silence]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/cutsignal.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,96 @@ +function outsignal = cutsignal(insignal,samplingfrequency,vocabularyset) + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% This function cuts the silence before and after the audiosignal +% +% (c) Tim Jürgens, Medizinische Physik, Feb.2006 +% +% usage: outsignal = cutsignal(insignal,samplingfrequency,vocabularyset) +% +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% initial_energythreshold = 0.11; +% final_energythreshold = 0.05; +% +% % choose initial and final delay due to different vocabularysets +% if (strcmp(vocabularyset,'f_f') > 0) +% initial_delay = 0.17; % the speech started delay seconds before trespassing threshold (default 0.01, for f_f: 0.17) +% final_delay = 0.2; %default 0.1, for f_f: 0.2 +% else +% initial_delay = 0.01; % the speech started delay seconds before trespassing threshold (default 0.01, for f_f: 0.2) +% final_delay = 0.1; %default 0.1, for f_f: 0.2 +% end +% +% [initialsample_of_frame, energy] = compute_energy(insignal, samplingfrequency); +% for i = 1:length(initialsample_of_frame) +% if (energy(i) > initial_energythreshold) +% initialsample = initialsample_of_frame(i)-samplingfrequency*initial_delay; +% break; +% end +% end +% +% time_inverted_energy = energy(end:-1:1); %turn signal around +% time_inverted_initialsample = initialsample_of_frame(end:-1:1); +% +% for i = 1:length(time_inverted_initialsample) +% if (time_inverted_energy(i) > final_energythreshold) +% finalsample = time_inverted_initialsample(i)+samplingfrequency*final_delay; +% break; +% end +% end +% +% if (finalsample > length(insignal)) +% finalsample = length(insignal); +% end +% outsignal = insignal(initialsample:finalsample); + +if nargin < 3 + vocabularyset = 'a_a'; %default vocabularyset +end + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +initial_threshold = 0.15; %amplitude threshold for detection of speech +final_threshold = 0.1; + +% choose initial and final delay due to different vocabularysets +if (strcmp(vocabularyset,'f_f') > 0) + initial_delay = 0.2; % the speech started delay seconds before trespassing threshold (default 0.01, for f_f: 0.2) + final_delay = 0.2; %default 0.1, for f_f: 0.2 +else + initial_delay = 0.01; % the speech started delay seconds before trespassing threshold (default 0.01, for f_f: 0.2) + final_delay = 0.1; %default 0.1, for f_f: 0.2 +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +%cut signal with taking initial delay into account +for(i = 1:length(insignal)) + if (abs(insignal(i)) > initial_threshold) + initialsample = i - samplingfrequency*initial_delay; + break; + end +end + + +time_inverted_insignal = insignal(end:-1:1); %turn signal around +% cut it with taking a final delay into account +for(i = 1:length(time_inverted_insignal)) + if (abs(time_inverted_insignal(i)) > final_threshold) + finalsample = i - samplingfrequency*final_delay; + break; + end +end + +if (finalsample < 0) + finalsample = 0; +end +if (initialsample < 1) + initialsample = 1; +end + +%% output %%% +outsignal = insignal(initialsample:end-finalsample); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/enframe.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,74 @@ +function [f,t]=enframe(x,win,inc) +%ENFRAME split signal up into (overlapping) frames: one per row. [F,T]=(X,WIN,INC) +% +% F = ENFRAME(X,LEN) splits the vector X(:) up into +% frames. Each frame is of length LEN and occupies +% one row of the output matrix. The last few frames of X +% will be ignored if its length is not divisible by LEN. +% It is an error if X is shorter than LEN. +% +% F = ENFRAME(X,LEN,INC) has frames beginning at increments of INC +% The centre of frame I is X((I-1)*INC+(LEN+1)/2) for I=1,2,... +% The number of frames is fix((length(X)-LEN+INC)/INC) +% +% F = ENFRAME(X,WINDOW) or ENFRAME(X,WINDOW,INC) multiplies +% each frame by WINDOW(:) +% +% The second output argument, T, gives the time in samples at the centre +% of each frame. T=i corresponds to the time of sample X(i). +% +% Example of frame-based processing: +% INC=20 % set frame increment +% NW=INC*2 % oversample by a factor of 2 (4 is also often used) +% S=cos((0:NW*7)*6*pi/NW); % example input signal +% W=sqrt(hamming(NW+1)); W(end)=[]; % sqrt hamming window of period NW +% F=enframe(S,W,INC); % split into frames +% ... process frames ... +% X=overlapadd(F,W,INC); % reconstitute the time waveform (omit "X=" to plot waveform) + +% Copyright (C) Mike Brookes 1997 +% Version: $Id: enframe.m,v 1.7 2009/11/01 21:08:21 dmb Exp $ +% +% VOICEBOX is a MATLAB toolbox for speech processing. +% Home page: http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You can obtain a copy of the GNU General Public License from +% http://www.gnu.org/copyleft/gpl.html or by writing to +% Free Software Foundation, Inc.,675 Mass Ave, Cambridge, MA 02139, USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +nx=length(x(:)); +nwin=length(win); +if (nwin == 1) + len = win; +else + len = nwin; +end +if (nargin < 3) + inc = len; +end +nf = fix((nx-len+inc)/inc); +f=zeros(nf,len); +indf= inc*(0:(nf-1)).'; +inds = (1:len); +f(:) = x(indf(:,ones(1,len))+inds(ones(nf,1),:)); +if (nwin > 1) + w = win(:)'; + f = f .* w(ones(nf,1),:); +end +if nargout>1 + t=(1+len)/2+indf; +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/fourier_analyse.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,22 @@ +function [frequency,out] = fourier_analyse(in, sfreq,color_plot) +% +%plots absolute values of fft of input signal on a semilogarithmic scale +%long term spectrum +% +%use: [frequency,out] = fourier_analyse(in, sfreq) +% in: input signal +% sfreq: samplingfrequency +% frequency: frequency vector +% out: fourier-spectrum (complex) +if ~exist('color_plot') + color_plot = 'b'; +end +out = fft(in)/(length(in)); %%Normierung auf Wurzel der Länge, da Matlab intern ohne Normierung der Hin-Fouriertransformation arbeitet +t = [0:1/sfreq:length(in)/sfreq-1/sfreq]; +frequency = [0:1/t(end):1/(2*(t(2)-t(1)))]; +%spektrale leistungsdichte wird geplottet, wobei eine amplitude von 1 100 +%dB entspricht +plot(frequency,20*log10(sqrt(2)*abs(out(1:round(length(in)/2)))),color_plot); +%sqrt(2) weil Gesamtenergie auch in Spiegelfrequenzen enthalten +xlabel('frequency / Hz'); +ylabel('fourier amplitude / dB'); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/fourierautocorrelationhistogram.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,83 @@ +function fach=fourierautocorrelationhistogram(ANpattern,sfreq) + + +time_axis = 0:1/sfreq:(size(ANpattern,2)-1)/sfreq; + +%find how many samples of AN_pattern are 10ms and 3ms +%one_sample_is_a_time_of = time_axis(2); +[tmp, start_time_index] = min(abs(0-time_axis)); +[tmp, stop20_time_index] = min(abs(0.020-time_axis)); +number_of_samples20ms = stop20_time_index - start_time_index; + +[tmp, stop3_time_index] = min(abs(0.003-time_axis)); +number_of_samples3ms = stop3_time_index - start_time_index; +every_3ms = 1:number_of_samples3ms:size(ANpattern,2)-number_of_samples20ms; + +hamm_window = hamming(11); +halfHamming = (length(hamm_window)-1)/2; + +% window normalization + +norm = conv(ones(1,floor(number_of_samples20ms/2)),hamm_window); +norm = norm(5+1:end-5)'; +win_size = number_of_samples20ms; +half_win_size = floor(win_size/2); +hop_size = number_of_samples3ms; + +%preallocation due to speed +fach = zeros(half_win_size,size(every_3ms,2)); + +for iCounter = 1:size(ANpattern,1) %each channel + fprintf('Channel No. %i\n',iCounter); + %time_counter = 1; + %for jCounter = every_3ms %every 3ms time segment + + + + %% Guy's code + % enframe this signal + + frames = enframe(ANpattern(iCounter,:),win_size,hop_size); + + % compute the autocorrelation + + acf = real(ifft(abs(fft(frames,[],2)).^2,[],2)); + acf(acf<0)=0; + acf = sqrt(acf(:,1:half_win_size)); + + % smooth with hamming window and take the root + + for frame=1:size(acf,1) + + + smoothed_correlation = conv(acf(frame,:),hamm_window); + smoothed_correlation = smoothed_correlation(halfHamming+1:end-halfHamming)./norm'; + fsra = abs(fft(smoothed_correlation-mean(smoothed_correlation))); + fsra = fsra(1:floor(length(fsra)/2)); + + t = [0:1/sfreq:length(smoothed_correlation)/sfreq-1/sfreq]; + frequency = [0:1/t(end):1/(2*(t(2)-t(1)))]; + %identify peaks in the fft + df = [0 ; diff(fsra')]; + idx = find((df(1:end-1)>=0)&(df(2:end)<0)); +% % interpolate +% a=df(idx); +% b=df(idx+1); +% idx = (idx-1+a./(a-b)); + [sorted,sortedindex]=sort(fsra(idx),'descend'); + % just take the three highest values of the fourier-transform + valid_peak_index = sortedindex(1:min([length(sortedindex) 3])); + amp = sorted(1:min([length(sortedindex) 3])); + + %store valid peaks according to amplitude in a histogram + if (~isempty(valid_peak_index)) + for k=1:length(valid_peak_index), + fach(idx(valid_peak_index(k)),frame) = fach(idx(valid_peak_index(k)),frame)+amp(k); + end + end + %transform index into frequencies + + end +end + +%fach = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/fourierautocorrelationhistogram_direct.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,109 @@ +function fach=fourierautocorrelationhistogram_direct(ANpattern,sfreq,plothandle) + + +time_axis = 0:1/sfreq:(size(ANpattern,2)-1)/sfreq; + +%find how many samples of AN_pattern are 10ms and 3ms +%one_sample_is_a_time_of = time_axis(2); +[tmp, start_time_index] = min(abs(0-time_axis)); +[tmp, stop20_time_index] = min(abs(0.020-time_axis)); +number_of_samples20ms = stop20_time_index - start_time_index; + +[tmp, stop3_time_index] = min(abs(0.003-time_axis)); +number_of_samples3ms = stop3_time_index - start_time_index; +every_3ms = 1:number_of_samples3ms:size(ANpattern,2)-number_of_samples20ms; + +hamm_window = hamming(11); +halfHamming = (length(hamm_window)-1)/2; + +% window normalization + +norm = conv(ones(1,floor(number_of_samples20ms/2)),hamm_window); +norm = norm(5+1:end-5)'; +win_size = number_of_samples20ms; +half_win_size = floor(win_size/2); +hop_size = number_of_samples3ms; + +%preallocation due to speed +fach = zeros(half_win_size,size(every_3ms,2)+1); + +for iCounter = 1:size(ANpattern,1) %each channel + fprintf('Channel No. %i\n',iCounter); + %time_counter = 1; + %for jCounter = every_3ms %every 3ms time segment + + + + %% Guy's code + % enframe this signal + + frames = enframe(ANpattern(iCounter,:),win_size,hop_size); + + % compute the autocorrelation + + %acf = real(ifft(abs(fft(frames,[],2)).^2,[],2)); + %acf(acf<0)=0; + %acf = sqrt(acf(:,1:half_win_size)); + + % smooth with hamming window and take the root + + for frame=1:size(frames,1) + + + smoothed_frame = conv(frames(frame,:),hamm_window); + smoothed_frame = smoothed_frame(halfHamming+1:end-halfHamming); + fsra = 20*log10(abs(fft(smoothed_frame-mean(smoothed_frame)))); + fsra = fsra(1:floor(length(fsra)/2)); + + t = [0:1/sfreq:length(smoothed_frame)/sfreq-1/sfreq]; + frequency = [0:1/t(end):1/(2*(t(2)-t(1)))]; + %identify peaks in the fft + df = [0 ; diff(fsra')]; + idx = find((df(1:end-1)>=0)&(df(2:end)<0)); +% % interpolate +% a=df(idx); +% b=df(idx+1); +% idx = (idx-1+a./(a-b)); + [sorted,sortedindex]=sort(fsra(idx),'descend'); + % just take the three highest values of the fourier-transform + valid_peak_index = sortedindex(1:min([length(sortedindex) 1])); + amp = sorted(1:min([length(sortedindex) 1])); + + %store valid peaks according to amplitude in a histogram + if (~isempty(valid_peak_index)) + for k=1:length(valid_peak_index), + fach(idx(valid_peak_index(k)),frame) = fach(idx(valid_peak_index(k)),frame)+amp(k); + end + end + %transform index into frequencies + + end +end + + +%plot the result +if ~exist('plothandle'), plothandle=figure; end +maxfrequency = 4000; +[tmp,number_of_channels_to_display] = min(abs(frequency-maxfrequency)); +frequency = frequency(1:number_of_channels_to_display); + +set(gcf,'Currentaxes',plothandle); + +YTickIdx = 1:floor(numel(frequency)/6):numel(frequency); +XTickIdx = 1:floor(numel(every_3ms)/6):numel(every_3ms); +YTickIdxRev = numel(frequency)+1-YTickIdx; +if ~isempty(gca) + axes(gca); %#ok<MAXES> + imagesc(fach(1:number_of_channels_to_display,:)); + axis xy + set(gca, 'YTick', YTickIdx); + set(gca, 'YTickLabel', num2str( frequency(YTickIdx)', '%0.0f' )); + ylabel('frequency (Hz)') + set(gca, 'XTick', XTickIdx); + set(gca, 'XTickLabel', XTickIdx.*3); + xlabel('Time (ms)') +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/iloga2sloga.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,160 @@ +function [sOut] = iloga2sloga(iIn) +% string logatome to integer logatome +% Bernd Meyer / Medical Physik / University of Oldenburg +% bernd.meyer@uni-oldenburg.de; March 2005 + +switch iIn + case 001; sOut = 'adda'; + case 002; sOut = 'atta'; + case 003; sOut = 'agga'; + case 004; sOut = 'acka'; + case 005; sOut = 'affa'; + case 006; sOut = 'assa'; + case 007; sOut = 'abba'; + case 008; sOut = 'appa'; + case 009; sOut = 'awwa'; + case 010; sOut = 'azza'; + case 011; sOut = 'amma'; + case 012; sOut = 'anna'; + case 013; sOut = 'ascha'; + case 014; sOut = 'alla'; + case 015; sOut = 'edde'; + case 016; sOut = 'ette'; + case 017; sOut = 'egge'; + case 018; sOut = 'ecke'; + case 019; sOut = 'effe'; + case 020; sOut = 'esse'; + case 021; sOut = 'ebbe'; + case 022; sOut = 'eppe'; + case 023; sOut = 'ewwe'; + case 024; sOut = 'ezze'; + case 025; sOut = 'emme'; + case 026; sOut = 'enne'; + case 027; sOut = 'esche'; + case 027; sOut = 'escha'; + case 028; sOut = 'elle'; + case 029; sOut = 'iddi'; + case 030; sOut = 'itti'; + case 031; sOut = 'iggi'; + case 032; sOut = 'icki'; + case 033; sOut = 'iffi'; + case 034; sOut = 'issi'; + case 035; sOut = 'ibbi'; + case 036; sOut = 'ippi'; + case 037; sOut = 'iwwi'; + case 038; sOut = 'izzi'; + case 039; sOut = 'immi'; + case 040; sOut = 'inni'; + case 041; sOut = 'ischi'; + case 042; sOut = 'illi'; + case 043; sOut = 'oddo'; + case 044; sOut = 'otto'; + case 045; sOut = 'oggo'; + case 046; sOut = 'ocko'; + case 047; sOut = 'offo'; + case 048; sOut = 'osso'; + case 049; sOut = 'obbo'; + case 050; sOut = 'oppo'; + case 051; sOut = 'owwo'; + case 052; sOut = 'ozzo'; + case 053; sOut = 'ommo'; + case 054; sOut = 'onno'; + case 055; sOut = 'oscho'; + case 056; sOut = 'ollo'; + case 057; sOut = 'uddu'; + case 058; sOut = 'uttu'; + case 059; sOut = 'uggu'; + case 060; sOut = 'ucku'; + case 061; sOut = 'uffu'; + case 062; sOut = 'ussu'; + case 063; sOut = 'ubbu'; + case 064; sOut = 'uppu'; + case 065; sOut = 'uwwu'; + case 066; sOut = 'uzzu'; + case 067; sOut = 'ummu'; + case 068; sOut = 'unnu'; + case 069; sOut = 'uschu'; + case 070; sOut = 'ullu'; + case 071; sOut = 'dadd'; + case 072; sOut = 'tatt'; + case 073; sOut = 'gagg'; + case 074; sOut = 'kakk'; + case 075; sOut = 'faff'; + case 076; sOut = 'sass'; + case 077; sOut = 'babb'; + case 078; sOut = 'papp'; + case 079; sOut = 'dedd'; + case 080; sOut = 'tett'; + case 081; sOut = 'gegg'; + case 082; sOut = 'kekk'; + case 083; sOut = 'feff'; + case 084; sOut = 'sess'; + case 085; sOut = 'bebb'; + case 086; sOut = 'pepp'; + case 087; sOut = 'didd'; + case 088; sOut = 'titt'; + case 089; sOut = 'gigg'; + case 090; sOut = 'kikk'; + case 091; sOut = 'fiff'; + case 092; sOut = 'siss'; + case 093; sOut = 'bibb'; + case 094; sOut = 'pipp'; + case 095; sOut = 'dodd'; + case 096; sOut = 'tott'; + case 097; sOut = 'gogg'; + case 098; sOut = 'kokk'; + case 099; sOut = 'foff'; + case 100; sOut = 'soss'; + case 101; sOut = 'bobb'; + case 102; sOut = 'popp'; + case 103; sOut = 'dudd'; + case 104; sOut = 'tutt'; + case 105; sOut = 'gugg'; + case 106; sOut = 'kukk'; + case 107; sOut = 'fuff'; + case 108; sOut = 'suss'; + case 109; sOut = 'bubb'; + case 110; sOut = 'pupp'; + case 111; sOut = 'dahd'; + case 112; sOut = 'taht'; + case 113; sOut = 'gahg'; + case 114; sOut = 'kahk'; + case 115; sOut = 'fahf'; + case 116; sOut = 'sahs'; + case 117; sOut = 'bahb'; + case 118; sOut = 'pahp'; + case 119; sOut = 'dehd'; + case 120; sOut = 'teht'; + case 121; sOut = 'gehg'; + case 122; sOut = 'kehk'; + case 123; sOut = 'fehf'; + case 124; sOut = 'sehs'; + case 125; sOut = 'behb'; + case 126; sOut = 'pehp'; + case 127; sOut = 'died'; + case 128; sOut = 'tiet'; + case 129; sOut = 'gieg'; + case 130; sOut = 'kiek'; + case 131; sOut = 'fief'; + case 132; sOut = 'sies'; + case 133; sOut = 'bieb'; + case 134; sOut = 'piep'; + case 135; sOut = 'dohd'; + case 136; sOut = 'toht'; + case 137; sOut = 'gohg'; + case 138; sOut = 'kohk'; + case 139; sOut = 'fohf'; + case 140; sOut = 'sohs'; + case 141; sOut = 'bohb'; + case 142; sOut = 'pohp'; + case 143; sOut = 'duhd'; + case 144; sOut = 'tuht'; + case 145; sOut = 'guhg'; + case 146; sOut = 'kuhk'; + case 147; sOut = 'fuhf'; + case 148; sOut = 'suhs'; + case 149; sOut = 'buhb'; + case 150; sOut = 'puhp'; +otherwise + error(['no logatome ' num2str(iIn) ' found in list']); +end % of switch
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/map_iih_onto_log.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,48 @@ +function [mappediih,centerfreqs] = map_iih_onto_log(iih,numchannels,sfreq) + +%function to map an IPIH from the interval axis onto the frequency axis +%with no overlap and mean firing rate. +% +% Tim Juergens, September 2011 +% +% input: iih: IPIH with dimensions interval (1) and time step (2) +% the first dimension translates to time using the actual +% sampling frequency +% numchannels: number of desired channels +% sfreq: sampling frequency +% output: mappediih: IPIH with dimensions frequency channel (1) and time +% step (2) +% centerfreqs: center frequencies of the channels of mappediih + +ctr_intervals = [1/sfreq:1/sfreq:size(iih,1)/sfreq]; +lowestBF=1/ctr_intervals(end); +highestBF=10000; + +borderfreqs = logspace(log10(lowestBF),log10(highestBF),numchannels+1); + +for iCounter = 1:numchannels + centerfreqs(iCounter)=(borderfreqs(iCounter)+borderfreqs(iCounter+1))/2; +end + +for iCounter = 1:length(borderfreqs) %find the indices that correspond to the borderfrequencies of the BF filters + [tmp,channelbordersIPIindex(iCounter)]=min(abs(borderfreqs(iCounter)-1./ctr_intervals)); +end + +for iCounter = 1:length(centerfreqs) + %mapping with one interval sample overlap + mappediih(iCounter,:) = mean(iih(channelbordersIPIindex(end-iCounter+1):channelbordersIPIindex(end-iCounter),:)); +end + + + +% OPTIONAL PLOTTING + figure + YTickIdx = 1:floor(numel(centerfreqs)/6):numel(centerfreqs); + YTickIdxRev = numel(centerfreqs)+1-YTickIdx; + if ~isempty(gca) + axes(gca); %#ok<MAXES> + imagesc(mappediih); + set(gca, 'YTick', YTickIdx); + set(gca, 'YTickLabel', num2str( centerfreqs(YTickIdxRev)', '%0.0f' )); + ylabel('cf in Hz') + end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/mellin_trafo.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,45 @@ +function out = mellin_trafo(inx,iny) + +%This function computes the Mellin Transformation of a one-dimensional +%Signal in analytical terms given as +% +% S(p)=Integral from 0 to infty of s(t) t^(p-1)dt +% Which equals +% S(c) = Integral from -Infty to Infty s(t)*exp(j*c*log(t))d(log(t)) +% +% taken from Irino and Patterson, Speech Communication 2002 Eq. (1) and (3) +% Tim Juergens, September 2011 +% + +%Resample the Signal onto a log(t) axis +minimalx=min(inx); +maximalx=max(inx); +logarithmicx= exp([log(minimalx):(log(maximalx)-log(minimalx))/length(inx):log(maximalx)]); +logarithmicy= interp1(inx,iny,logarithmicx,'linear','extrap'); +%figure, semilogx(logarithmicx,logarithmicy); + +%Absolute of the inverse Fourier-Transform of the resampled signal +out = abs(ifft(logarithmicy)); +%figure, plot(out(1:40)); + + +%%just to show that the mellin transform results in invariable patterns if +%%the formants are a constant ratio +% frequencies_short = [1:8:8000]; +% frequencies_long = [1:10:10000]; +% Intervals_long = 1./frequencies_long; +% Intervals_short = 1./frequencies_short; +% contoursin = sin(2*pi*0.00015.*frequencies_long).^2 +% figure, plot(frequencies_long,contoursin), hold on, plot(frequencies_short,contoursin,'r') +% xlabel('Frequency (Hz)') +% ylabel('Rate (arb. units)') +% figure, semilogx(Intervals_long,contoursin), hold on, plot(Intervals_short,contoursin,'r') +% xlabel('Interval (s)') +% ylabel('Rate (arb. units)') +% m1 = mellin_trafo(Intervals_long,contoursin); +% m2 = mellin_trafo(Intervals_short,contoursin); +% figure, plot(m1(1:40)), hold on, plot(m2(1:40),'r'); +% xlabel('Mellin variable c'); +% ylabel('Magnitude'); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/pitchModel_RM.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,242 @@ +function pitchModel_RM +% Modification of testMAP_14 to replicate the pitch model published +% in JASA 2006. +% +% test_MAP1_14 is a general purpose test routine that can be adjusted to +% test a number of different applications of MAP1_14 +% +% A range of options are supplied in the early part of the program +% +% One use of the function is to create demonstrations; filenames <demoxx> +% to illustrate particular features +% +% #1 +% Identify the file (in 'MAPparamsName') containing the model parameters +% +% #2 +% Identify the kind of model required (in 'AN_spikesOrProbability'). +% A full brainstem model (spikes) can be computed or a shorter model +% (probability) that computes only so far as the auditory nerve +% +% #3 +% Choose between a tone signal or file input (in 'signalType') +% +% #4 +% Set the signal rms level (in leveldBSPL) +% +% #5 +% Identify the channels in terms of their best frequencies in the vector +% BFlist. +% +% Last minute changes to the parameters fetched earlier can be made using +% the cell array of strings 'paramChanges'. +% Each string must have the same format as the corresponding line in the +% file identified in 'MAPparamsName' +% +% When the demonstration is satisfactory, freeze it by renaming it <demoxx> + +dbstop if error +restorePath=path; +addpath (['..' filesep 'MAP'], ['..' filesep 'wavFileStore'], ... + ['..' filesep 'utilities']) + +% Pitch model modification here +global ICrate % used to collect rate profile from showMAP temporary +rates=[]; F0count=0; + +% F0s=[150 200 250]; % fundamental frequency +% harmonics= 3:5; + +% F0s=[3000]; % fundamental frequency +F0s=50:5:1000; +harmonics= 1; +% F0s=150; +for F0=F0s + F0count=F0count+1; + + +%% #1 parameter file name +MAPparamsName='Normal'; + + +%% #2 probability (fast) or spikes (slow) representation +AN_spikesOrProbability='spikes'; + +% or +% NB probabilities are not corrected for refractory effects +% AN_spikesOrProbability='probability'; + + +%% #3 pure tone, harmonic sequence or speech file input +signalType= 'tones'; +sampleRate= 50000; +duration=0.50; % seconds +% toneFrequency= 1000; % or a pure tone (Hz8 + +% F0=210; +toneFrequency= F0*harmonics; % harmonic sequence (Hz) + +rampDuration=.005; % raised cosine ramp (seconds) + +% or + +% signalType= 'file'; +% fileName='twister_44kHz'; + + +%% #4 rms level +% signal details +leveldBSPL= 50; % dB SPL + + +%% #5 number of channels in the model +% 21-channel model (log spacing) +numChannels=21; +lowestBF=250; highestBF= 8000; +BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels)); + +% or specify your own channel BFs +% numChannels=1; +BFlist=toneFrequency; +% BFlist=500; + + +%% #6 change model parameters +paramChanges=[]; + +% or +% Parameter changes can be used to change one or more model parameters +% *after* the MAPparams file has been read +% This example declares only one fiber type with a calcium clearance time +% constant of 80e-6 s (HSR fiber) when the probability option is selected. + +% paramChanges={'AN_IHCsynapseParams.ANspeedUpFactor=5;', ... +% 'IHCpreSynapseParams.tauCa=86e-6;'}; + +% paramChanges={'DRNLParams.rateToAttenuationFactorProb = 0;'}; + +% paramChanges={'IHCpreSynapseParams.tauCa=86e-6;', +% 'AN_IHCsynapseParams.numFibers= 1000;'}; + +% fixed MOC attenuation(using negative factor) +% paramChanges={'DRNLParams.rateToAttenuationFactorProb=-0.005;'}; + +% slow the CN chopping rate +% paramChanges={'IHCpreSynapseParams.tauCa= 70e-6;'...' +% 'MacGregorMultiParams.tauGk= [0.75e-3:.0001 : 3e-3];'... +% ' MacGregorParams.dendriteLPfreq=4000;'... +% 'MacGregorParams.tauGk= 1e-4;'... +% 'MacGregorParams.currentPerSpike=220e-8;'... +% }; +paramChanges={... + 'MacGregorMultiParams.currentPerSpike=25e-9;'... + 'MacGregorMultiParams.tauGk= [0.1e-3:.00005 : 1e-3];'... +'MacGregorParams.currentPerSpike=40e-9;'... +}; + +%% delare 'showMap' options to control graphical output + +showMapOptions.printModelParameters=0; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=0; % shows SACF (probability only) +showMapOptions.showEfferent=0; % tracks of AR and MOC +showMapOptions.surfProbability=0; % 2D plot of HSR response +showMapOptions.surfSpikes=0; % 2D plot of spikes histogram +showMapOptions.ICrates=1; % IC rates by CNtauGk + +% disable certain silly options +if strcmp(AN_spikesOrProbability, 'spikes') + % avoid nonsensical options + showMapOptions.surfProbability=0; + showMapOptions.showACF=0; +else + showMapOptions.surfSpikes=0; +end +if strcmp(signalType, 'file') + % needed for labeling plot + showMapOptions.fileName=fileName; +else + showMapOptions.fileName=[]; +end + +%% Generate stimuli + +switch signalType + case 'tones' + inputSignal=createMultiTone(sampleRate, toneFrequency, ... + leveldBSPL, duration, rampDuration); + + case 'file' + %% file input simple or mixed + [inputSignal sampleRate]=wavread(fileName); + dt=1/sampleRate; + inputSignal=inputSignal(:,1); + targetRMS=20e-6*10^(leveldBSPL/20); + rms=(mean(inputSignal.^2))^0.5; + amp=targetRMS/rms; + inputSignal=inputSignal*amp; + silence= zeros(1,round(0.1/dt)); + inputSignal= [silence inputSignal' silence]; +end + + +%% run the model +tic + +fprintf('\n') +disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)]) +disp([num2str(numChannels) ' channel model']) +disp([num2str(F0) ' F0']) +disp('Computing ...') + +MAP1_14(inputSignal, sampleRate, BFlist, ... + MAPparamsName, AN_spikesOrProbability, paramChanges); + + +% the model run is now complete. Now display the results +UTIL_showMAP(showMapOptions, paramChanges) + +%% pitch model Collect and analyse data +% ICrate is global and computed in showMAP +% a vector of 'stage4' rates; one value for each tauCNGk +rates=[rates; ICrate]; +figure(92), imagesc(rates) +ylabel ('F0 no'), xlabel('tauGk') +% figure(92), plot(rates), ylim([0 inf]) + +h=figure(99); CNmovie(F0count)=getframe(h); +figure(91), plot(rates'),ylim([0 inf]) +pause (0.1) +path(restorePath) + +end +%% show results +toc +figure(91), plot(F0s,rates'), xlabel('F0'), ylabel('rate'),ylim([0 inf]) +% figure(99),clf,movie(CNmovie,1,4) + + +function inputSignal=createMultiTone(sampleRate, toneFrequency, ... + leveldBSPL, duration, rampDuration) +% Create pure tone stimulus +dt=1/sampleRate; % seconds +time=dt: dt: duration; +inputSignal=sum(sin(2*pi*toneFrequency'*time), 1); +amp=10^(leveldBSPL/20)*28e-6; % converts to Pascals (peak) +inputSignal=amp*inputSignal; + +% apply ramps +% catch rampTime error +if rampDuration>0.5*duration, rampDuration=duration/2; end +rampTime=dt:dt:rampDuration; +ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; +inputSignal=inputSignal.*ramp; +ramp=fliplr(ramp); +inputSignal=inputSignal.*ramp; + +% add 10 ms silence +silence= zeros(1,round(0.005/dt)); +inputSignal= [silence inputSignal silence]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/plotIFRAN.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,54 @@ +function plotIFRAN(data_matrix,start_time,end_time,sfreq,BFlist,plothandle) + +%function that produces a plot similar to the plots in Secker-Walker, JASA +%1990 Fig.2 (Neurograms) from a IFRAN data matrix +%Tim Jürgens, January 2011 + +%plot 50ms right from the middle of the signal +time_axis = [0:1/sfreq:(size(data_matrix,2)-1)/sfreq]; +%start_time = size(data_matrix,2)/2/sfreq-0.025; %start time to plot in s +%temporal_length = min([0.05 size(data_matrix,2)/sfreq]); %length to plot in s, at least 50ms or the length of the stimulus +[tmp, start_time_index] = min(abs(start_time-time_axis)); +%end_time = start_time+temporal_length; +[tmp, end_time_index] = min(abs(end_time-time_axis)); + + +%smoothing using a 4-point hamming window +hamm_window = hamming(4); +for iCounter = 1:size(data_matrix,1) + for jCounter = 2:length(time_axis)-2 %start with 2 and end with 2 samples + %less the length of time_axis in order not to get in conflict with the length of + %the hamm_window + smoothed_data_matrix(iCounter,jCounter-1) = ... + data_matrix(iCounter,(jCounter-1):(jCounter+2))*hamm_window./sum(hamm_window); + end +end + +smoothed_time_axis = time_axis(2:end-2); + +if ~exist('plothandle'), plothandle=figure; end + +verticalshift = 1000; %vertical shift of single time series in z-coordinate units +Tickvector = []; +TickLabels = []; +for iCounter = 1:size(data_matrix,1) + set(gcf,'Currentaxes',plothandle); + + %multiply by 1000 to set abscissa to ms units + verticalposition = -verticalshift*(iCounter-1); + plot(1000.*smoothed_time_axis(start_time_index:end_time_index), ... + smoothed_data_matrix(size(data_matrix,1)-iCounter+1,start_time_index:end_time_index)+verticalposition, ... + 'k','LineWidth',0.5); + xlim([1000*start_time 1000*end_time]); + if mod(iCounter,5) == 1 %set best frequency as a label every 5 channels + Tickvector = [Tickvector verticalposition]; + TickLabels = [TickLabels; BFlist(size(data_matrix,1)-iCounter+1)]; + end + hold on; +end +set(gca,'yTick',Tickvector(end:-1:1),'yTickLabel',num2str(TickLabels(end:-1:1),'%4.0f')); +ylabel('Best Frequency of Filter (Hz)'); +xlabel('Stimulus Time (ms)'); +set(gca,'YLim',[-(size(data_matrix,1))*verticalshift verticalshift*1.5]); +box on; +hold off; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/poolIPI_across_channels.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,83 @@ +function [pooledIPIdata,ctr] = poolIPI_across_channels(IPIhisttime,IPIhistweight) + +%function that pools IPIdata across all channels in order to plot the data +%as in Secker-Walker JASA 1990 Fig. 6 + +%plots IPI-Histograms in a matrix display as in Secker-Walker JASA 1990 +%Fig. 4 +% +% Tim Jürgens, February 2011 +% +% input: IPIhisttime: matrix containing the interval times found in the +% analysis +% first dimension: frequency channel +% second dimension: time step (hop position) +% third dimension: interval dimension (max 3) +% IPIhistweight: matrix containing the interval weights found in the +% analysis, same dimensions as IPIhisttime +% output: pooledIPIdata: matrix containing the pooled and weighted IPI +% histograms as a function of time (hop position) +% first dimension: time step (hop position) +% second dimension: IPI intervals +% ctr: center values of classes of IPI intervals +% ATTENTION: depending on the amplitude of the signals the value +% 'verticalshift' might have to be adjusted in order to see structures in +% the plot + +verticalshift = 3000; %0.008;%vertical shift of single time series in z-coordinate units +ctr = [0:0.02:5].*1e-3; %classes (center-values) of 20 microsec width + +%preallocation of variables +pooledIPIdata = zeros(size(IPIhisttime,2),length(ctr)); +smoothed_pooledIPI = zeros(size(IPIhisttime,2),length(ctr)-5); + +for iCounter = 1:size(IPIhisttime,2) %one for time spacing + %cannot use matlabs hist function because weighting must be applied + + tmpIPIhisttime = squeeze(IPIhisttime(:,iCounter,:)); + tmpIPIhistweight = squeeze(IPIhistweight(:,iCounter,:)); + tmpIPIhisttime = tmpIPIhisttime(:); + tmpIPIhistweight = tmpIPIhistweight(:); + for jCounter = 1:length(tmpIPIhisttime) + %look which class + [tmp1,classindex] = min(abs(tmpIPIhisttime(jCounter)-ctr)); + pooledIPIdata(iCounter,classindex) = pooledIPIdata(iCounter,classindex)+tmpIPIhistweight(jCounter); + end + +end + +%smooth data using a 5-point hamming window +hamm_window = hamming(5); +for iCounter = 1:size(pooledIPIdata,1) + for jCounter = 3:length(ctr)-2 %start with 3 and end with 2 samples + %less the length of ctr in order not to get in conflict with the length of + %the hamm_window + smoothed_pooledIPI(iCounter,jCounter-2) = ... + pooledIPIdata(iCounter,(jCounter-2):(jCounter+2))*hamm_window./sum(hamm_window); + end +end +smoothed_ctr = ctr(3:end-2); + + +figure; + +Tickvector = []; +TickLabels = []; +for iCounter = 1:size(IPIhisttime,2) + hold on; + verticalposition = verticalshift*(iCounter-1); + %multiply by 1000 to set abscissa to ms units + plot(1000.*smoothed_ctr, ... + smoothed_pooledIPI(iCounter,:)+verticalposition, ... + 'k','LineWidth',2); + if mod(iCounter,5) == 1 %set best frequency as a label every 10 channels + Tickvector = [Tickvector verticalposition]; + TickLabels = [TickLabels; (iCounter-1)*3]; %time spacing is every 3ms + end +end +set(gca,'yTick',Tickvector,'yTickLabel',num2str(TickLabels,'%4.0f')); +ylabel('Stimulus Time (ms)'); +xlabel('Interval (ms)'); +xlim([min(1000*ctr) max(1000*ctr)]); +ylim([-verticalshift verticalposition+3*verticalshift]); +box on;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/runningACF.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,97 @@ +function sampledacf = runningACF(inputSignalMatrix, sfreq, params) +% runningACF computes within-channel, running autocorrelations (acfs) +% and samples it at given time steps +% +% INPUT +% inputSignalMatrix: a matrix (channel x time) of AN activity +% sfreq: sampling frequency +% +% params: a list of parmeters to guide the computation: +% params.lags: an array of lags to be computed (seconds) +% params.acfTau: time constant (sec) of the running acf calculations +% +% +% +% OUTPUT +% sampledacf: (time x lags) + +%% +boundaryValue=[]; +[nChannels inputLength]= size(inputSignalMatrix); +% list of BFs must be repeated is many fiber types used +%BFlist=method.nonlinCF; +%nfibertypes=nChannels/length(BFlist); +%BFlist=repmat(BFlist',2,1)'; + +dt=1/sfreq; + +samplelength=0.003; %sample length in ms +gridpoints=round([samplelength:samplelength:inputLength*dt]/dt); +gridpoints=[1 gridpoints]; + +lags=params.lags; + +nLags=length(lags); + + +% Establish matrix of lag time constants +% these are all the same if tau<1 + +if params.acfTau<1 % all taus are the same + acfTaus=repmat(params.acfTau, 1, nLags); + acfDecayFactors=ones(size(lags)).*exp(-dt/params.acfTau); +else + error('acfTau must be <1'); +end +% make acfDecayFactors into a (channels x lags) matrix for speedy computation +acfDecayFactors=repmat(acfDecayFactors,nChannels, 1); + +% P function lowpass filter decay (only one value needed) +%pDecayFactor=exp(-dt/params.lambda); + +% ACF +% lagPointers is a list of pointers relative to 'time now' +lagPointers=round(lags/dt); +lagWeights=ones(nChannels,nLags); +if max(lagPointers)+1>inputLength + error([' filteredSACF: not enough signal to evaluate ACF. Max(lag)= ' num2str(max(lags))]) +end + + + + acf=zeros(nChannels,nLags); + sampledacf = zeros(length(gridpoints),nChannels,nLags); + % create a runup buffer of signal + buffer= zeros(nChannels, max(lagPointers)); + +inputSignalMatrix=[buffer inputSignalMatrix]; +[nChannels inputLength]= size(inputSignalMatrix); + +timeCounter=0; biggestSACF=0; +gridcounter =1; +for timePointer= max(lagPointers)+1:inputLength + % acf is a continuously changing channels x lags matrix + % Only the current value is stored + % sacf is the vertical summary of acf ( a vector) and all values are kept and returned + % P is the smoothed version of sacf and all values are kept and returned + % lagWeights emphasise some BF/lag combinations and ignore others + % NB time now begins at the longest lag. + % E.g. if max(lags) is .04 then this is when the ACf will begin. + % AN AN delayed weights filtering + + % This is the ACF calculation + timeCounter=timeCounter+1; + acf = (repmat(inputSignalMatrix(:, timePointer), 1, nLags) .* inputSignalMatrix(:, timePointer-lagPointers)).*lagWeights *dt + acf.* acfDecayFactors; + + %just sample on certain samplepoints + if ~isempty(find(timeCounter==gridpoints)) + sampledacf(gridcounter,:,:)=acf; + gridcounter = gridcounter+1; + end + + end +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/sloga2iloga.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,169 @@ +function [iOut] = sloga2iloga(sIn, gVar) +% string logatome to integer logatome +% Bernd Meyer / Medical Physik / University of Oldenburg +% bernd.meyer@uni-oldenburg.de; March 2005 + +if ~exist('gVar') + gVar.iVerboseFlag = 1; +end + +sIn = strrep(sIn,' ',''); + +switch sIn + case 'adda'; iOut = 001; + case 'atta'; iOut = 002; + case 'agga'; iOut = 003; + case 'acka'; iOut = 004; + case 'affa'; iOut = 005; + case 'assa'; iOut = 006; + case 'abba'; iOut = 007; + case 'appa'; iOut = 008; + case 'awwa'; iOut = 009; + case 'azza'; iOut = 010; + case 'amma'; iOut = 011; + case 'anna'; iOut = 012; + case 'ascha'; iOut = 013; + case 'alla'; iOut = 014; + case 'edde'; iOut = 015; + case 'ette'; iOut = 016; + case 'egge'; iOut = 017; + case 'ecke'; iOut = 018; + case 'effe'; iOut = 019; + case 'esse'; iOut = 020; + case 'ebbe'; iOut = 021; + case 'eppe'; iOut = 022; + case 'ewwe'; iOut = 023; + case 'ezze'; iOut = 024; + case 'emme'; iOut = 025; + case 'enne'; iOut = 026; + case 'esche'; iOut = 027; + case 'escha'; iOut = 027; + case 'elle'; iOut = 028; + case 'iddi'; iOut = 029; + case 'itti'; iOut = 030; + case 'iggi'; iOut = 031; + case 'icki'; iOut = 032; + case 'iffi'; iOut = 033; + case 'issi'; iOut = 034; + case 'ibbi'; iOut = 035; + case 'ippi'; iOut = 036; + case 'iwwi'; iOut = 037; + case 'izzi'; iOut = 038; + case 'immi'; iOut = 039; + case 'inni'; iOut = 040; + case 'ischi'; iOut = 041; + case 'illi'; iOut = 042; + case 'oddo'; iOut = 043; + case 'otto'; iOut = 044; + case 'oggo'; iOut = 045; + case 'ocko'; iOut = 046; + case 'offo'; iOut = 047; + case 'osso'; iOut = 048; + case 'obbo'; iOut = 049; + case 'oppo'; iOut = 050; + case 'owwo'; iOut = 051; + case 'ozzo'; iOut = 052; + case 'ommo'; iOut = 053; + case 'onno'; iOut = 054; + case 'oscho'; iOut = 055; + case 'ollo'; iOut = 056; + case 'uddu'; iOut = 057; + case 'uttu'; iOut = 058; + case 'uggu'; iOut = 059; + case 'ucku'; iOut = 060; + case 'uffu'; iOut = 061; + case 'ussu'; iOut = 062; + case 'ubbu'; iOut = 063; + case 'uppu'; iOut = 064; + case 'uwwu'; iOut = 065; + case 'uzzu'; iOut = 066; + case 'ummu'; iOut = 067; + case 'unnu'; iOut = 068; + case 'uschu'; iOut = 069; + case 'ullu'; iOut = 070; + case 'dadd'; iOut = 071; + case 'tatt'; iOut = 072; + case 'gagg'; iOut = 073; + case 'kakk'; iOut = 074; + case 'faff'; iOut = 075; + case 'sass'; iOut = 076; + case 'babb'; iOut = 077; + case 'papp'; iOut = 078; + case 'dedd'; iOut = 079; + case 'tett'; iOut = 080; + case 'gegg'; iOut = 081; + case 'kekk'; iOut = 082; + case 'feff'; iOut = 083; + case 'sess'; iOut = 084; + case 'bebb'; iOut = 085; + case 'pepp'; iOut = 086; + case 'didd'; iOut = 087; + case 'titt'; iOut = 088; + case 'gigg'; iOut = 089; + case 'kikk'; iOut = 090; + case 'fiff'; iOut = 091; + case 'siss'; iOut = 092; + case 'bibb'; iOut = 093; + case 'pipp'; iOut = 094; + case 'dodd'; iOut = 095; + case 'tott'; iOut = 096; + case 'gogg'; iOut = 097; + case 'kokk'; iOut = 098; + case 'foff'; iOut = 099; + case 'soss'; iOut = 100; + case 'bobb'; iOut = 101; + case 'popp'; iOut = 102; + case 'dudd'; iOut = 103; + case 'tutt'; iOut = 104; + case 'gugg'; iOut = 105; + case 'kukk'; iOut = 106; + case 'fuff'; iOut = 107; + case 'suss'; iOut = 108; + case 'bubb'; iOut = 109; + case 'pupp'; iOut = 110; + case 'dahd'; iOut = 111; + case 'taht'; iOut = 112; + case 'gahg'; iOut = 113; + case 'kahk'; iOut = 114; + case 'fahf'; iOut = 115; + case 'sahs'; iOut = 116; + case 'bahb'; iOut = 117; + case 'pahp'; iOut = 118; + case 'dehd'; iOut = 119; + case 'teht'; iOut = 120; + case 'gehg'; iOut = 121; + case 'kehk'; iOut = 122; + case 'fehf'; iOut = 123; + case 'sehs'; iOut = 124; + case 'behb'; iOut = 125; + case 'pehp'; iOut = 126; + case 'died'; iOut = 127; + case 'tiet'; iOut = 128; + case 'gieg'; iOut = 129; + case 'kiek'; iOut = 130; + case 'fief'; iOut = 131; + case 'sies'; iOut = 132; + case 'bieb'; iOut = 133; + case 'piep'; iOut = 134; + case 'dohd'; iOut = 135; + case 'toht'; iOut = 136; + case 'gohg'; iOut = 137; + case 'kohk'; iOut = 138; + case 'fohf'; iOut = 139; + case 'sohs'; iOut = 140; + case 'bohb'; iOut = 141; + case 'pohp'; iOut = 142; + case 'duhd'; iOut = 143; + case 'tuht'; iOut = 144; + case 'guhg'; iOut = 145; + case 'kuhk'; iOut = 146; + case 'fuhf'; iOut = 147; + case 'suhs'; iOut = 148; + case 'buhb'; iOut = 149; + case 'puhp'; iOut = 150; + otherwise + if gVar.iVerboseFlag + disp(['no logatome ' sIn ' found in list']); + end + iOut = -1; +end % of switch
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/speech_stimuli_check.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,168 @@ +%skript to test several stimuli with MAP1_14 +% Tim Juergens, September 2011 + +%addpath('C:\Documents and Settings\tjurgens\My Documents\Dropbox\matlab'); +addpath('C:\MAP\userPrograms'); +parameterfile='Normal'; + +%Read the wavfiles +[da,sfreq] = wavread('..\wavFileStore\new-da-44khz.wav'); %artificial stimulus +ba = wavread('..\wavFileStore\new-ba-44khz.wav'); %artificial stimulus + +%set level of speech stimuli (RMS 1 equals 94 dB) +da_69dB = da./sqrt(mean(da.^2)).*10^(-(94-69)/20); +da_49dB = da./sqrt(mean(da.^2)).*10^(-(94-49)/20); +ba_69dB = ba./sqrt(mean(ba.^2)).*10^(-(94-69)/20); +%20*log10(sqrt(mean(ba_69dB.^2))/20e-6) %reference pressure: 20uPa + + +cd .. +cd MAP + +%MAP and store AN output of HSR fibers in variables +MAP1_14(da_69dB,sfreq,-1,parameterfile,'probability'); +global ANprobRateOutput +AN_da_69dB = ANprobRateOutput(1:size(ANprobRateOutput,1)/2,:); + + MAP1_14(da_49dB,sfreq,-1,parameterfile,'probability'); + global ANprobRateOutput + AN_da_49dB = ANprobRateOutput(1:size(ANprobRateOutput,1)/2,:); +% + MAP1_14(ba_69dB,sfreq,-1,parameterfile,'probability'); + global ANprobRateOutput + global savedBFlist + AN_ba_69dB = ANprobRateOutput(1:size(ANprobRateOutput,1)/2,:); + +%Do the IPIH analysis + [iih_da_69dB,IPIhisttime_da_69dB,IPIhistweight_da_69dB] = track_formants_from_IPI_guy(AN_da_69dB,sfreq); + %[iih_da_69dB,IPIhisttime_da_69dB,IPIhistweight_da_69dB] = IPIHextract(AN_da_69dB,sfreq); + poolIPI_across_channels(IPIhisttime_da_69dB,IPIhistweight_da_69dB); + map_iih_onto_log(iih_da_69dB,30,sfreq); + [iih_da_49dB,IPIhisttime_da_49dB,IPIhistweight_da_49dB] = track_formants_from_IPI_guy(AN_da_49dB,sfreq); +% poolIPI_across_channels(IPIhisttime_da_49dB,IPIhistweight_da_49dB); +% + [iih_ba_69dB,IPIhisttime_ba_69dB,IPIhistweight_ba_69dB] = track_formants_from_IPI_guy(AN_ba_69dB,sfreq); +% [tmp,ctr]=poolIPI_across_channels(IPIhisttime_ba_69dB,IPIhistweight_ba_69dB); +% + + + + %%%%%% OLLO stimuli %%%%%%%%%%%%%%%%% +% OLLOwavfiles = {'dahd','bahb','dehd','behb','died','bieb','dohd','bohb','duhd','buhb','atta','ascha','assa'}; +% OLLO_level = 69; %level of OLLO files in dB SPL +% paramChanges = [];%{'DRNLParams.rateToAttenuationFactorProb = 0;'}; +% +% for iCounter = 1:length(OLLOwavfiles) +% +% %read the stimuli +% eval(['[' OLLOwavfiles{iCounter} ',sfreq_OLLO] = wavread([''..\wavFileStore\S02M_L' ... +% sprintf('%3.3i',sloga2iloga(OLLOwavfiles{iCounter})) '_V6_M1_N2_CS0.wav'']);']) +% +% %delete preceding and subsequent silence and resample to 44100 Hz +% %sampling frequency +% eval([OLLOwavfiles{iCounter} ' = cutsignal(' OLLOwavfiles{iCounter} ',sfreq_OLLO,''d_d'');']); +% eval([OLLOwavfiles{iCounter} ' = resample(' OLLOwavfiles{iCounter} ',sfreq,sfreq_OLLO);']); +% +% %set level +% partfilename = [OLLOwavfiles{iCounter} num2str(OLLO_level) 'dB']; +% eval([partfilename ' = ' OLLOwavfiles{iCounter} ... +% './sqrt(mean(' OLLOwavfiles{iCounter} '.^2)).*10^(-(94-' num2str(OLLO_level) ')/20);']); +% +% %use MAP +% eval(['MAP1_14(' partfilename ',sfreq,-1,parameterfile,''probability'', paramChanges );']); +% global ANprobRateOutput +% eval(['AN_' partfilename ' = ANprobRateOutput(1:size(ANprobRateOutput,1)/2,:);']); +% +% %Do the IPIH analysis +% eval(['[iih_' partfilename ',IPIhisttime_' partfilename ',IPIhistweight_' ... +% partfilename '] = track_formants_from_IPI_guy(AN_' partfilename ',sfreq);']); +% eval(['poolIPI_across_channels(IPIhisttime_' partfilename ',IPIhistweight_' partfilename ');']); +% title(partfilename); +% %set(gca,'Title',partfilename); +% xlabel('Interval (ms)') +% ylabel('Stimulus time (ms)'); +% +% eval(['map_iih_onto_log(iih_' partfilename ',30,sfreq);']); +% title(partfilename); +% end + + +%%%%%%%%%%%%% OLLO stimuli from different speakers +% OLLOwavfiles = {'S01F_L111_V6_M1_N2_CS0.wav','S02M_L111_V6_M1_N2_CS0.wav'}; +% OLLO_level = 69; %level of OLLO files in dB SPL +% for iCounter = 1:length(OLLOwavfiles) +% +% partfilename = [OLLOwavfiles{iCounter}(1:end-4) num2str(OLLO_level) 'dB']; +% %read the stimuli +% eval(['[' partfilename ',sfreq_OLLO] = wavread([''..\wavFileStore\' OLLOwavfiles{iCounter} ''']);']) +% +% %delete preceding and subsequent silence and resample to 44100 Hz +% %sampling frequency +% eval([partfilename ' = cutsignal(' partfilename ',sfreq_OLLO,''d_d'');']); +% eval([partfilename ' = resample(' partfilename ',sfreq,sfreq_OLLO);']); +% +% %set level +% +% eval([partfilename ' = ' partfilename ... +% './sqrt(mean(' partfilename '.^2)).*10^(-(94-' num2str(OLLO_level) ')/20);']); +% +% %use MAP +% eval(['MAP1_14(' partfilename ',sfreq,-1,parameterfile,''probability'');']); +% global ANprobRateOutput +% eval(['AN_' partfilename ' = ANprobRateOutput(1:size(ANprobRateOutput,1)/2,:);']); +% +% %Do the IPIH analysis +% eval(['[iih_' partfilename ',IPIhisttime_' partfilename ',IPIhistweight_' ... +% partfilename '] = track_formants_from_IPI_guy(AN_' partfilename ',sfreq);']); +% eval(['poolIPI_across_channels(IPIhisttime_' partfilename ',IPIhistweight_' partfilename ');']); +% title(partfilename); +% %set(gca,'Title',partfilename); +% xlabel('Interval (ms)') +% ylabel('Stimulus time (ms)'); +% +% eval(['map_iih_onto_log(iih_' partfilename ',30,sfreq);']); +% title(partfilename); +% end + + + +%%%%%% da stimuli with different pitches %%%%%%%%%%%%%%%%% + + dawavfiles = { '200ms_da_080Hz.wav','200ms_da_100Hz.wav','200ms_da_120Hz.wav','200ms_da_140Hz.wav', ... + '200ms_da_160Hz.wav','200ms_da_180Hz.wav','200ms_da_200Hz.wav','200ms_da_220Hz.wav', ... + '200ms_da_240Hz.wav', ... + 'noise.wav', ... + 'da_whispered.wav', ... + }; + + da_level = 69; %level of OLLO files in dB SPL +for iCounter = 1:length(dawavfiles) + + %read the stimuli + partfilename = ['da' dawavfiles{iCounter}(1:end-4)]; + eval([ partfilename ' = wavread(''..\wavFileStore\' dawavfiles{iCounter} ''');']) + + %set level + eval([partfilename ' = ' partfilename ... + './sqrt(mean(' partfilename '.^2)).*10^(-(94-' num2str(da_level) ')/20);']); + + %use MAP + eval(['MAP1_14(' partfilename ',sfreq,-1,parameterfile,''probability'');']); + global ANprobRateOutput + eval(['AN_' partfilename ' = ANprobRateOutput(1:size(ANprobRateOutput,1)/2,:);']); + + %Do the IPIH analysis + eval(['[iih_' partfilename ',IPIhisttime_' partfilename ',IPIhistweight_' ... + partfilename '] = IPIHextract(AN_' partfilename ',sfreq);']);%partfilename '] = track_formants_from_IPI_guy(AN_' partfilename ',sfreq);']);% + eval(['poolIPI_across_channels(IPIhisttime_' partfilename ',IPIhistweight_' partfilename ');']); + title(partfilename); + %set(gca,'Title',partfilename); + xlabel('Interval (ms)') + ylabel('Stimulus time (ms)'); + + eval(['map_iih_onto_log(iih_' partfilename ',30,sfreq);']); + title(partfilename); +end +% +% +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/speechmanipulation.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,714 @@ +function varargout = speechmanipulation(varargin) +% SPEECHMANIPULATION MATLAB code for speechmanipulation.fig +% SPEECHMANIPULATION, by itself, creates a new SPEECHMANIPULATION or raises the existing +% singleton*. +% +% H = SPEECHMANIPULATION returns the handle to a new SPEECHMANIPULATION or the handle to +% the existing singleton*. +% +% SPEECHMANIPULATION('CALLBACK',hObject,eventData,handles,...) calls the local +% function named CALLBACK in SPEECHMANIPULATION.M with the given input arguments. +% +% SPEECHMANIPULATION('Property','Value',...) creates a new SPEECHMANIPULATION or raises the +% existing singleton*. Starting from the left, property value pairs are +% applied to the GUI before speechmanipulation_OpeningFcn gets called. An +% unrecognized property name or invalid value makes property application +% stop. All inputs are passed to speechmanipulation_OpeningFcn via varargin. +% +% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one +% instance to run (singleton)". +% +% See also: GUIDE, GUIDATA, GUIHANDLES + +% Edit the above text to modify the response to help speechmanipulation + +% Last Modified by GUIDE v2.5 05-Oct-2011 11:21:58 + +% Begin initialization code - DO NOT EDIT +gui_Singleton = 1; +gui_State = struct('gui_Name', mfilename, ... + 'gui_Singleton', gui_Singleton, ... + 'gui_OpeningFcn', @speechmanipulation_OpeningFcn, ... + 'gui_OutputFcn', @speechmanipulation_OutputFcn, ... + 'gui_LayoutFcn', [] , ... + 'gui_Callback', []); +if nargin && ischar(varargin{1}) + gui_State.gui_Callback = str2func(varargin{1}); +end + +if nargout + [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); +else + gui_mainfcn(gui_State, varargin{:}); +end +% End initialization code - DO NOT EDIT + + +% --- Executes just before speechmanipulation is made visible. +function speechmanipulation_OpeningFcn(hObject, eventdata, handles, varargin) +% This function has no output args, see OutputFcn. +% hObject handle to figure +% eventdata reserved - to be defined in a future version of MATLAB +% handles structure with handles and user data (see GUIDATA) +% varargin command line arguments to speechmanipulation (see VARARGIN) +global wavfilescell; +global rateaxeshandle; %handle to axis6 is needed, if radiobuttons12 and 13 change + + +% Choose default command line output for speechmanipulation +handles.output = hObject; + +% Update handles structure +guidata(hObject, handles); + +% UIWAIT makes speechmanipulation wait for user response (see UIRESUME) +% uiwait(handles.figure1); +wavfiles = dir(['..' filesep 'wavFileStore' filesep '*.wav']); +for iCounter = 1:length(wavfiles) + wavfilescell{iCounter} = wavfiles(iCounter).name; +end +set(handles.popupmenu1,'String',wavfilescell); +set(handles.popupmenu2,'String',wavfilescell); +set(handles.uipanel8,'SelectionChangeFcn',@radiobuttonselected); +rateaxeshandle = handles.axes6; + +% --- Outputs from this function are returned to the command line. +function varargout = speechmanipulation_OutputFcn(hObject, eventdata, handles) +% varargout cell array for returning output args (see VARARGOUT); +% hObject handle to figure +% eventdata reserved - to be defined in a future version of MATLAB +% handles structure with handles and user data (see GUIDATA) + +% Get default command line output from handles structure +varargout{1} = handles.output; + +function edit1_Callback(hObject, eventdata, handles) +function edit2_Callback(hObject, eventdata, handles) +function edit3_Callback(hObject, eventdata, handles) +function edit4_Callback(hObject, eventdata, handles) +function edit5_Callback(hObject, eventdata, handles) +function edit6_Callback(hObject, eventdata, handles) +function edit7_Callback(hObject, eventdata, handles) +function edit8_Callback(hObject, eventdata, handles) +function edit9_Callback(hObject, eventdata, handles) +function edit10_Callback(hObject, eventdata, handles) +function edit11_Callback(hObject, eventdata, handles) +function edit12_Callback(hObject, eventdata, handles) + + +% --- Executes on selection change in popupmenu1. +function popupmenu1_Callback(hObject, eventdata, handles) +% hObject handle to popupmenu1 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles structure with handles and user data (see GUIDATA) + +% Hints: contents = cellstr(get(hObject,'String')) returns popupmenu1 contents as cell array +% contents{get(hObject,'Value')} returns selected item from popupmenu1 +%% CHOOSE AND LOAD WAVEFORM +global wavfilescell +global originalsignal +global actualsignal + +%get the number from the popupmenu +selectedfileno = get(handles.popupmenu1,'Value'); + +%load it +[waveform,sfreq] = wavread(['..' filesep 'wavFileStore' filesep wavfilescell{selectedfileno}]); + +%if it is from OLLO then cut it +if regexp(wavfilescell{selectedfileno}, 'S[0-9]*[A-Z]_L[0-9]*_V[0-9]_M[0-9]_N[0-9]_CS0.wav') %pattern for OLLO-files + waveform = cutsignal(waveform,sfreq,'a_a'); +end + +%store it in global variables +actualsignal.waveform = waveform; +actualsignal.sfreq = sfreq; +originalsignal.waveform = waveform; +originalsignal.sfreq = sfreq; + +%plot it +multipleplot(actualsignal,handles); + +% --- Executes during object creation, after setting all properties. +function popupmenu1_CreateFcn(hObject, eventdata, handles) +% hObject handle to popupmenu1 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles empty - handles not created until after all CreateFcns called + +% Hint: popupmenu controls usually have a white background on Windows. +% See ISPC and COMPUTER. +if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) + set(hObject,'BackgroundColor','white'); +end + + +% --- Executes on button press in pushbutton1. +function pushbutton1_Callback(hObject, eventdata, handles) +% hObject handle to pushbutton1 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles structure with handles and user data (see GUIDATA) +%% LOAD WAVEFORM +global wavfilescell +global originalsignal +global actualsignal + +%get the number from the popupmenu +selectedfileno = get(handles.popupmenu1,'Value'); + +%load it +[waveform,sfreq] = wavread(['..' filesep 'wavFileStore' filesep wavfilescell{selectedfileno}]); + +%if it is from OLLO then cut it +if regexp(wavfilescell{selectedfileno}, 'S[0-9]*[A-Z]_L[0-9]*_V[0-9]_M[0-9]_N[0-9]_CS0.wav') %pattern for OLLO-files + waveform = cutsignal(waveform,sfreq,'a_a'); +end + +%store it in global variables +actualsignal.waveform = waveform; +actualsignal.sfreq = sfreq; +originalsignal.waveform = waveform; +originalsignal.sfreq = sfreq; + +%plot it +multipleplot(actualsignal,handles) + + +% --- Executes during object creation, after setting all properties. +function edit1_CreateFcn(hObject, eventdata, handles) +% hObject handle to edit1 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles empty - handles not created until after all CreateFcns called + +% Hint: edit controls usually have a white background on Windows. +% See ISPC and COMPUTER. +if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) + set(hObject,'BackgroundColor','white'); +end + + +% --- Executes on button press in pushbutton2. +function pushbutton2_Callback(hObject, eventdata, handles) +% hObject handle to pushbutton2 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles structure with handles and user data (see GUIDATA) +%% PLAY THE ACTUAL (MANIPULATED) SIGNAL +global actualsignal +global toplay + +toplay = audioplayer(actualsignal.waveform,actualsignal.sfreq); +play(toplay); + + +% --- Executes on button press in pushbutton3. +function pushbutton3_Callback(hObject, eventdata, handles) +% hObject handle to pushbutton3 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles structure with handles and user data (see GUIDATA) + +%% MANIPULATE SIGNAL +global wavfilescell +global originalsignal +global actualsignal + +actualsignal = originalsignal; %reset + +%% speed factor change +speedfactor = str2num(get(handles.edit1,'String')); +newfakesfreq=actualsignal.sfreq*speedfactor; +if speedfactor == 1 +else + actualsignal.waveform = resample(actualsignal.waveform,actualsignal.sfreq,newfakesfreq); +end + +%% low and high pass filter (butterworth) +% get the desired filter from radio buttons +radiofilterhandles=get(handles.uipanel2,'Children'); +for iCounter = 1:length(radiofilterhandles) + if get(radiofilterhandles(iCounter),'Value') == 1 + filtertype = get(radiofilterhandles(iCounter),'String'); + end +end +% read parameters +tmp = get(handles.edit3,'String'); +cutofffreq=str2num(tmp{1}); +tmp = get(handles.edit4,'String'); +filterorder=str2num(tmp{1}); +%do the filter calculations +switch filtertype + case 'none' + case 'High pass' + if isempty(cutofffreq) || isempty(filterorder) + error('Please specify cutoff frequency and/or filter order') + else + [a,b] = butter(filterorder,cutofffreq*2/actualsignal.sfreq,'high'); + actualsignal.waveform=filter(a,b,actualsignal.waveform); + end + case 'Low pass' + if isempty(cutofffreq)|| isempty(filterorder) + error('Please specify cutoff frequency') + else + [a,b] = butter(filterorder,cutofffreq*2/actualsignal.sfreq,'low'); + actualsignal.waveform=filter(a,b,actualsignal.waveform); + end +end + +%additive noise +% get the desired filter from radio buttons +radionoisehandles=get(handles.uipanel4,'Children'); +for iCounter = 1:length(radionoisehandles) + if get(radionoisehandles(iCounter),'Value') == 1 + noisetype = get(radionoisehandles(iCounter),'String'); + end +end +% read parameters +tmp = get(handles.edit5,'String'); +snr=str2num(tmp{1}); +%do the noise calculations +switch noisetype + case 'none' + case 'White noise' + if isempty(snr) + error('Please specify SNR') + else + whitenoise = rand(length(actualsignal.waveform),1)-0.5; + %set level + levelsignal = 20*log10(sqrt(mean(actualsignal.waveform.^2))); + levelnoise = levelsignal-snr; + whitenoise = whitenoise./sqrt(mean(whitenoise.^2)).*10^(levelnoise/20); + %20*log10(sqrt(mean(whitenoise.^2))) + actualsignal.waveform=actualsignal.waveform+whitenoise; + end + case 'From file' + if isempty(snr) + error('Please specify SNR') + else + selectedfileno = get(handles.popupmenu2,'Value'); + + [noisewaveform,noisesfreq] = wavread(['..' filesep 'wavFileStore' filesep wavfilescell{selectedfileno}]); + if noisesfreq == actualsignal.sfreq + else + noisewaveform = resample(noisewaveform,actualsignal.sfreq,noisesfreq); + noisesfreq = actualsignal.sfreq; + end + if length(noisewaveform) < length(actualsignal.waveform) + warning('Noise waveform too short. Noise is looped without fading at the endings!'); + noisewaveform = repmat(noisewaveform, ceil(length(actualsignal.waveform)/length(noisewaveform)),1); + end + noisewaveform=noisewaveform(1:length(actualsignal.waveform)); + %set level + levelsignal = 20*log10(sqrt(mean(actualsignal.waveform.^2))); + levelnoise = levelsignal-snr; + noisewaveform = noisewaveform./sqrt(mean(noisewaveform.^2)).*10^(levelnoise/20); + %20*log10(sqrt(mean(noisewaveform.^2))) + %finally add the two signals + actualsignal.waveform = actualsignal.waveform + noisewaveform; + end +end + +%plot the resulting manipulated waveform +multipleplot(actualsignal,handles); + +% --- Executes during object creation, after setting all properties. +function edit3_CreateFcn(hObject, eventdata, handles) +% hObject handle to edit3 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles empty - handles not created until after all CreateFcns called + +% Hint: edit controls usually have a white background on Windows. +% See ISPC and COMPUTER. +if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) + set(hObject,'BackgroundColor','white'); +end + + +% --- Executes during object creation, after setting all properties. +function edit4_CreateFcn(hObject, eventdata, handles) +% hObject handle to edit4 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles empty - handles not created until after all CreateFcns called + +% Hint: edit controls usually have a white background on Windows. +% See ISPC and COMPUTER. +if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) + set(hObject,'BackgroundColor','white'); +end + + +% --- Executes during object creation, after setting all properties. +function edit5_CreateFcn(hObject, eventdata, handles) +% hObject handle to edit5 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles empty - handles not created until after all CreateFcns called + +% Hint: edit controls usually have a white background on Windows. +% See ISPC and COMPUTER. +if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) + set(hObject,'BackgroundColor','white'); +end + + +% --- Executes during object creation, after setting all properties. +function popupmenu2_CreateFcn(hObject, eventdata, handles) +% hObject handle to popupmenu2 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles empty - handles not created until after all CreateFcns called + +% Hint: popupmenu controls usually have a white background on Windows. +% See ISPC and COMPUTER. +if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) + set(hObject,'BackgroundColor','white'); +end + + +% --- Executes on button press in pushbutton4. +function pushbutton4_Callback(hObject, eventdata, handles) +% hObject handle to pushbutton4 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles structure with handles and user data (see GUIDATA) +%% PLAY THE ORIGINAL WAVEFORM +global originalsignal +global toplay +toplay = audioplayer(originalsignal.waveform,originalsignal.sfreq); +play(toplay) + + +% --- Executes on button press in pushbutton5. +function pushbutton5_Callback(hObject, eventdata, handles) +% hObject handle to pushbutton5 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles structure with handles and user data (see GUIDATA) +global toplay +stop(toplay); + + +function edit6_Callback(hObject, eventdata, handles) +% hObject handle to edit6 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles structure with handles and user data (see GUIDATA) + +% Hints: get(hObject,'String') returns contents of edit6 as text +% str2double(get(hObject,'String')) returns contents of edit6 as a double + + +% --- Executes during object creation, after setting all properties. +function edit6_CreateFcn(hObject, eventdata, handles) +% hObject handle to edit6 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles empty - handles not created until after all CreateFcns called + +% Hint: edit controls usually have a white background on Windows. +% See ISPC and COMPUTER. +if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) + set(hObject,'BackgroundColor','white'); +end + +% --- Executes during object creation, after setting all properties. +function edit7_CreateFcn(hObject, eventdata, handles) +% hObject handle to edit7 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles empty - handles not created until after all CreateFcns called + +% Hint: edit controls usually have a white background on Windows. +% See ISPC and COMPUTER. +if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) + set(hObject,'BackgroundColor','white'); +end + + +% --- Executes during object creation, after setting all properties. +function edit8_CreateFcn(hObject, eventdata, handles) +% hObject handle to edit8 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles empty - handles not created until after all CreateFcns called + +% Hint: edit controls usually have a white background on Windows. +% See ISPC and COMPUTER. +if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) + set(hObject,'BackgroundColor','white'); +end + + +% --- Executes on button press in pushbutton6. +function pushbutton6_Callback(hObject, eventdata, handles) +% hObject handle to pushbutton6 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles structure with handles and user data (see GUIDATA) + +%% CALCULATE MAP 1.14 auditory nerve probability output +addpath(['..' filesep 'MAP']); +addpath(['..' filesep 'utilities']); +global actualsignal +global AN_HSRoutput + +tmp = get(handles.edit6,'String'); +level4MAP=str2num(tmp); +if isempty(level4MAP) + error('Please specify level'); +end + +tmp = get(handles.edit7,'String'); +parameterfile=tmp; +if isempty(parameterfile) + error('Please specify parameter file'); +end + +% identify model parameter changes if any +paramChanges=get(handles.edit8,'string'); +if ~strcmp(paramChanges, ';'), paramChanges=[paramChanges ';']; end +eval(paramChanges); + +%set level +actualsignal.waveform = actualsignal.waveform./sqrt(mean(actualsignal.waveform.^2)).*10^(-(94-level4MAP)/20); +%20*log10(sqrt(mean(actualsignal.waveform.^2))/20e-6) %reference pressure: 20uPa + +MAP1_14(actualsignal.waveform,actualsignal.sfreq,-1,parameterfile, ... + 'probability',paramChanges); +global ANprobRateOutput savedBFlist + +%take only the HSR fibers +AN_HSRoutput = ANprobRateOutput(size(ANprobRateOutput)/2+1:end,:); + +%plot the auditory nerve firing probability as line plot +start_time = size(AN_HSRoutput,2)/2/actualsignal.sfreq-0.025; %start time to plot in s +end_time = start_time+0.05; %plot 50ms +plotIFRAN(AN_HSRoutput,start_time,end_time,actualsignal.sfreq,savedBFlist,handles.axes4); +set(handles.edit11,'String',num2str(1000*size(AN_HSRoutput,2)/2/actualsignal.sfreq)); + +%plot the fourierhistogram as image plot +formantpattern = fourierautocorrelationhistogram_direct(AN_HSRoutput,actualsignal.sfreq,handles.axes5); +caxis([0 2000]); +colorbar; + +%plot the rate output +plotrateOutput(AN_HSRoutput,actualsignal.sfreq,handles.axes6,[33.7 300]); +set(handles.radiobutton12,'Value',1); + +%calculate the IPIH +ipih=track_formants_from_IPI_guy(AN_HSRoutput, actualsignal.sfreq); +%the following code assumes that the bin width is 1/actualsignal.sfreq and +%the temporal step size is 3ms + +ipihfreqaxis=1./[1/actualsignal.sfreq:1/actualsignal.sfreq:size(ipih,1)/actualsignal.sfreq]; +ipihtimeaxis=[0:3:size(ipih,2)*3]; +set(gcf,'CurrentAxes',handles.axes7); +YTickIdx = 1:floor(numel(ipihfreqaxis)/6):numel(ipihfreqaxis); +XTickIdx = 1:floor(numel(ipihtimeaxis)/6):numel(ipihtimeaxis); +imagesc(ipih); +set(gca, 'YTick', YTickIdx); +set(gca, 'YTickLabel', num2str( ipihfreqaxis(YTickIdx)', '%0.0f' )); +ylabel('best frequency (Hz)') +set(gca, 'XTick', XTickIdx); +set(gca, 'XTickLabel', XTickIdx.*3); +xlabel('Time (ms)'); +caxis([0 8e4]); %set color +colorbar; + +%calculate and plot summarized autocorrelation, code from Ray +method.dt=1/actualsignal.sfreq; +method.segmentNo=1; +method.nonlinCF=savedBFlist; + +minPitch= 80; maxPitch= 4000; numPitches=100; % specify lags +pitches=10.^ linspace(log10(minPitch), log10(maxPitch),numPitches); +pitches=fliplr(pitches); +filteredSACFParams.lags=1./pitches; % autocorrelation lags vector +filteredSACFParams.acfTau= .003; % time constant of running ACF +filteredSACFParams.lambda= 0.12; % slower filter to smooth ACF +filteredSACFParams.lambda= 0.01; % slower filter to smooth ACF + +filteredSACFParams.plotACFs=0; % special plot (see code) +filteredSACFParams.plotFilteredSACF=0; % 0 plots unfiltered ACFs +filteredSACFParams.plotMoviePauses=.5;%.3 % special plot (see code) + +filteredSACFParams.usePressnitzer=0; % attenuates ACF at long lags +filteredSACFParams.lagsProcedure= 'useAllLags'; +filteredSACFParams.criterionForOmittingLags=3; +filteredSACFParams.plotACFsInterval=50;%200; + +% compute ACF +%switch saveAN_spikesOrProbability +% case 'probability' + inputToACF=ANprobRateOutput.^0.5; +% otherwise +% inputToACF=ANoutput; +%end + +disp ('computing ACF...') +t=method.dt*(1:length(actualsignal.waveform)); +[P, BFlist, sacf, boundaryValue] = ... + filteredSACF(inputToACF, method, filteredSACFParams); +P = real(P); %dont know why sometimes P can be (very slightly) complex +disp(' ACF done.') + +% SACF +set(gcf,'CurrentAxes',handles.axes8); +imagesc(P) +ylabel('periodicities (Hz)') +xlabel('time (s)') +%title(['running smoothed (root) SACF. ' saveAN_spikesOrProbability ' input']) +pt=[1 get(gca,'ytick')]; % force top xtick to show +set(gca,'ytick',pt) +set(gca,'ytickLabel', round(pitches(pt))) +tt=get(gca,'xtick'); +tt=tt(tt<length(t)); +set(gca,'xtickLabel', round(100*t(tt))/100) +colorbar; + +% --- Executes on button press in pushbutton7. +function pushbutton7_Callback(hObject, eventdata, handles) +% hObject handle to pushbutton7 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles structure with handles and user data (see GUIDATA) + +%% SHOW THE (PREVIOUSLY STORED) AUDITORY PROFILE CORRESPONDING TO THE +%% PARAMETER FILE +addpath(['..' filesep 'multithreshold 1.46' filesep]); +% read the parameter file +tmp = get(handles.edit7,'String'); +parameterfile=tmp; +if isempty(parameterfile) + error('Please specify parameter file'); +end + +%what kind of profiles are in the profiles directory? +files_in_profiles = dir(['..' filesep 'profiles' filesep '*.m']);%if exist('',file) +done = 0; + +%try to map the specified parameter file to these found in the directory +%and +for iCounter = 1:length(files_in_profiles) + if strfind(files_in_profiles(iCounter).name,parameterfile) + %ask the user if this is the correct file - because you'll never + %know ;) + stringtoask = ['Is ' files_in_profiles(iCounter).name ' the correct profile file?']; + Answer = questdlg(stringtoask,'Possible match found','Yes','No','Yes'); + switch Answer, + case 'Yes' + plotprofile(files_in_profiles(iCounter).name(1:end-2),'profile_CMA_L'); + done = 1; + case 'No' + end %switch + end %if +end +if ~done + msgbox('No match for this parameter file found.'); +end + +function multipleplot(actualsignal,handles) +%% FUNCTION TO DO 3 PLOTS +time_axis = [0:1/actualsignal.sfreq:(length(actualsignal.waveform)-1)/actualsignal.sfreq]; + +%waveform plot (temporal) +plot(handles.axes1,time_axis,actualsignal.waveform); +set(get(handles.axes1,'XLabel'),'String','Time (s)'); +set(get(handles.axes1,'YLabel'),'String','Amplitude'); +set(handles.axes1,'XLim',[0 length(actualsignal.waveform)/actualsignal.sfreq]); +highestabsampl = max(abs(actualsignal.waveform))*1.05; +set(handles.axes1,'YLim',[-highestabsampl highestabsampl]); + +%average spectrum plot +complspectrum = fft(actualsignal.waveform)/(length(actualsignal.waveform)); +frequency = [0:1/time_axis(end):1/(2*(time_axis(2)-time_axis(1)))]; +plot(handles.axes2,frequency,20*log10(sqrt(2)*abs(complspectrum(1:round(length(complspectrum)/2))))); +set(get(handles.axes2,'XLabel'),'String','frequency (Hz)'); +set(get(handles.axes2,'YLabel'),'String','fourier amplitude (dB)'); +set(handles.axes2,'XLim',[100 10000]); +set(handles.axes2,'XScale','log'); + +%spectrogram plot (10 ms temporal resolution) +[s,f,t] = spectrogram(actualsignal.waveform,hann(round(0.01*actualsignal.sfreq)),[],[],actualsignal.sfreq); %10ms short term windows +set(gcf,'CurrentAxes',handles.axes3); +if max(f)>10000 + [tmp,idx] = min(abs(f-10000)); + imagesc(t,f(1:idx),20*log10(abs(s(1:idx,:)))); +else + imagesc(t,f,20*log10(abs(s))); +end +axis xy; +set(get(handles.axes3,'YLabel'),'String','frequency (Hz)'); +set(get(handles.axes3,'XLabel'),'String','Time (s)'); + + +function plotrateOutput(ratepattern,sfreq,axeshandle,colorrange) +%% calculate rate representation and plot, code from Nick +global savedBFlist +%calculate rate representation +ANsmooth = [];%Cannot pre-allocate a size as it is unknown until the enframing +hopSize = 10; %ms +winSize = 25; %ms +winSizeSamples = round(winSize*sfreq/1000); +hann = hanning(winSizeSamples); +hopSizeSamples = round(hopSize*sfreq/1000); +for chan = 1:size(ratepattern,1) + f = enframe(ratepattern(chan,:), hann, hopSizeSamples); + ANsmooth(chan,:) = mean(f,2)'; +end + +%plot rate representation +time_axis_rate=[0:hopSize/1000:size(ratepattern,2)/sfreq]; +set(gcf,'CurrentAxes',axeshandle); +YTickIdx = 1:floor(numel(savedBFlist)/6):numel(savedBFlist); +XTickIdx = 1:floor(numel(time_axis_rate)/6):numel(time_axis_rate); +imagesc(ANsmooth); +axis xy; +set(gca, 'YTick', YTickIdx); +set(gca, 'YTickLabel', num2str( savedBFlist(YTickIdx)', '%0.0f' )); +ylabel('best frequency (Hz)') +set(gca, 'XTick', XTickIdx); +set(gca, 'XTickLabel', XTickIdx.*10); +xlabel('Time (ms)'); +caxis(colorrange); %set color from average spontaneous rate to a maximum of 600 +colorbar; + + +% --- Executes if the radiobuttons hsr and lsr are changed +function radiobuttonselected(source, eventdata) + +global rateaxeshandle +global ANprobRateOutput +global actualsignal + +selected = get(get(source,'SelectedObject'),'String'); + +if isempty(ANprobRateOutput) + msgbox('Please calculate AN pattern first!'); +elseif strcmp(selected,'HSR') + AN_HSRoutput = ANprobRateOutput(size(ANprobRateOutput)/2+1:end,:); + colorrange = [33.7 300]; + plotrateOutput(AN_HSRoutput,actualsignal.sfreq,rateaxeshandle,colorrange); +else + AN_LSRoutput = ANprobRateOutput(1:size(ANprobRateOutput)/2,:); + colorrange = [10.7 100]; + plotrateOutput(AN_LSRoutput,actualsignal.sfreq,rateaxeshandle,colorrange); +end + + +% --- Executes during object creation, after setting all properties. +function edit11_CreateFcn(hObject, eventdata, handles) +% hObject handle to edit11 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles empty - handles not created until after all CreateFcns called + +% Hint: edit controls usually have a white background on Windows. +% See ISPC and COMPUTER. +if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) + set(hObject,'BackgroundColor','white'); +end + + +% --- Executes on button press in pushbutton8. +function pushbutton8_Callback(hObject, eventdata, handles) +% hObject handle to pushbutton8 (see GCBO) +% eventdata reserved - to be defined in a future version of MATLAB +% handles structure with handles and user data (see GUIDATA) +%plot the auditory nerve firing probability as line plot after grabbing the +%time value from edit11 +global AN_HSRoutput +global actualsignal +global savedBFlist + +middletime = str2num(get(handles.edit11,'String'))/1000; + +start_time = min([size(AN_HSRoutput,2)/actualsignal.sfreq-0.01 max([0 middletime-0.025])]); %start time to plot in s +end_time = max([start_time+0.01 min([start_time+0.05 size(AN_HSRoutput,2)/actualsignal.sfreq-0.01])]); %plot max 50ms +plotIFRAN(AN_HSRoutput,start_time,end_time,actualsignal.sfreq,savedBFlist,handles.axes4);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/test_autocorr.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,14 @@ +addpath('..\utilities'); +addpath('..\MAP') + +%% delare 'showMap' options to control graphical output +showMapOptions.printModelParameters=1; % prints all parameters +showMapOptions.showModelOutput=1; % plot of all stages +showMapOptions.printFiringRates=1; % prints stage activity levels +showMapOptions.showACF=1; % shows SACF (probability only) +showMapOptions.showEfferent=1; % tracks of AR and MOC +showMapOptions.surfProbability=1; % 2D plot of HSR response +showMapOptions.surfSpikes=1; % 2D plot of spikes histogram +showMapOptions.ICrates=0; % IC rates by CNtauGk + +UTIL_showMAP(showMapOptions, {})
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/track_formants_from_IPI_guy.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,182 @@ +function [iih,IPIhisttime,IPIhistweight]=track_formants_from_IPI_guy(IFRAN_pattern, sfreq) +% +% tracks the formants according to an analysis proposed in Secker-Walker +% JASA 1990, section V.A +% Tim Jürgens, February 2011, code from Guy Brown included +% +% input: IFRAN_pattern: pattern of the auditory model (dependend on the number of modules used) +% first dimension: frequency channel, +% second dimension: time (samples) +% sfreq: sampling frequency +% output: iih: interpeak-interval histogram, matrix very similar +% the plot 5 in the Secker-Walker paper +% +% +% + + +time_axis = 0:1/sfreq:(size(IFRAN_pattern,2)-1)/sfreq; + +%find how many samples of AN_pattern are 10ms and 3ms +%one_sample_is_a_time_of = time_axis(2); +[tmp, start_time_index] = min(abs(0-time_axis)); +[tmp, stop10_time_index] = min(abs(0.01-time_axis)); +number_of_samples10ms = stop10_time_index - start_time_index; + +[tmp, stop3_time_index] = min(abs(0.003-time_axis)); +number_of_samples3ms = stop3_time_index - start_time_index; +every_3ms = 1:number_of_samples3ms:size(IFRAN_pattern,2)-number_of_samples10ms; + +hamm_window = hamming(11); +halfHamming = (length(hamm_window)-1)/2; + +% window normalization + +norm = conv(ones(1,floor(number_of_samples10ms/2)),hamm_window); +norm = norm(5+1:end-5)'; +win_size = number_of_samples10ms; +half_win_size = floor(win_size/2); +hop_size = number_of_samples3ms; + + +%pre-allocation due to speed +%Acorr = zeros(size(IFRAN_pattern,1),size(every_3ms,2),number_of_samples10ms*2+1); +%RAcorr = zeros(size(IFRAN_pattern,1),size(every_3ms,2),number_of_samples10ms*2+1); +%SRAcorr = zeros(size(IFRAN_pattern,1),size(every_3ms,2),number_of_samples10ms*2+1-10); +IPIhisttime = zeros(size(IFRAN_pattern,1),size(every_3ms,2),3); +IPIhistweight = zeros(size(IFRAN_pattern,1),size(every_3ms,2),3); %maximum 3 peaks from the SRA +iih = zeros(half_win_size,size(every_3ms,2)+1); + + + + +for iCounter = 1:size(IFRAN_pattern,1) %each channel + fprintf('Channel No. %i\n',iCounter); + %time_counter = 1; + %for jCounter = every_3ms %every 3ms time segment + + + + %% Guy's code + % enframe this signal + + frames = enframe(IFRAN_pattern(iCounter,:),win_size,hop_size); + + % compute the autocorrelation + + acf = real(ifft(abs(fft(frames,[],2)).^2,[],2)); + acf(acf<0)=0; + acf = sqrt(acf(:,1:half_win_size)); + + % smooth with hamming window and take the root + + for frame=1:size(acf,1) + + %%debug + %if iCounter == 130 + % disp('here'); + %end + + + sra = conv(acf(frame,:),hamm_window); + sra = sra(halfHamming+1:end-halfHamming)./norm'; + df = [0 ; diff(sra')]; + idx = find((df(1:end-1)>=0)&(df(2:end)<0)); + % interpolate + a=df(idx); + b=df(idx+1); + idx = (idx-1+a./(a-b)); + % get rid of a zero peak, if it exists + idx = idx(idx>1); + % peak values corresponding to these intervals + amp = interp1(1:length(sra),sra,idx,'linear'); + % if required, remove peaks that lie below the mean sra + % note that we disregard the value at zero delay + %if (params.removePeaksBelowMean) + valid = find(amp>mean(sra(2:end))); + idx = idx(valid); + amp = amp(valid); + %end + % only use the first four peaks (three intervals) + idx = idx(1:min(4,length(idx))); + % find the intervals + interval = diff(idx); + % now histogram the intervals + if (~isempty(interval)) + for k=1:length(interval), + iih(round(interval(k)),frame) = iih(round(interval(k)),frame)+amp(k); + IPIhisttime(iCounter,frame,k) = interval(k)/sfreq; + IPIhistweight(iCounter,frame,k) = amp(k); + end + end + + end + + + + + %% end Guy's code + + + % %take the autocorrelation (ACF) of a 10ms-segment of each channel + % Acorr(iCounter,time_counter,:) = xcorr(IFRAN_pattern(iCounter,jCounter:number_of_samples10ms+jCounter),'biased'); %biased scales the ACF by the reciprocal of the length of the segment + % %root calculation + % RAcorr(iCounter,time_counter,:) = sqrt(abs(Acorr(iCounter,time_counter,:))); + % + % %smoothing using the 11-point hamming window + % for kCounter = 6:size(RAcorr(iCounter,time_counter,:),3)-5 %start with 6 and end with 5 samples + % %less the length of time_axis not to get in conflict with the length of + % %the hamm_window + % SRAcorr(iCounter,time_counter,kCounter-5) = ... + % squeeze(RAcorr(iCounter,time_counter,(kCounter-5):(kCounter+5)))'*hamm_window./sum(hamm_window); + % end + % + % %mean value of actual SRA + % SRA_mean = mean(SRAcorr(iCounter,time_counter,:)); + % + % %find signed zero-crossings of the first derivative (=difference) + % z_crossings_indices = find(diff(sign(diff(squeeze(SRAcorr(iCounter,time_counter,:))))) < 0)+1; %+1 is necessary, because diff shortens vector by 1 + % middle_index = ceil(size(SRAcorr(iCounter,time_counter,:),3)/2); + % + % validCounter = 1; + % valid_z_crossings_indices = []; + % %find valid zero-crossings (peak higher than meanvalue and within first 5 ms of SRA) + % for lCounter = 1:length(z_crossings_indices) + % if (SRAcorr(iCounter,time_counter,z_crossings_indices(lCounter)) > SRA_mean) && ... + % (abs(z_crossings_indices(lCounter)-middle_index) < round(number_of_samples10ms/2)); + % valid_z_crossings_indices(validCounter) = z_crossings_indices(lCounter); + % validCounter = validCounter+1; + % end + % end + % + % %find main peak in the ACF + % [tmp,index_of_z_crossings_main_index] = min(abs(middle_index-valid_z_crossings_indices)); + % if ~tmp == 0 + % disp('middle peak not appropriately found'); + % end + % + % %%% for debugging + % % if iCounter == 130 + % % disp('here'); + % % figure, plot(squeeze(SRAcorr(iCounter,time_counter,:))); + % % hold on, plot([1 length(squeeze(SRAcorr(iCounter,time_counter,:)))],[SRA_mean SRA_mean],'r-'); + % % end + % %%% + % + % %generate IPI-histogram: take the first 3 intervals of SRAcorr + % %(positive delay) in the first 5 ms + % histcounter = 1; + % for lCounter = index_of_z_crossings_main_index+1:min([length(valid_z_crossings_indices(index_of_z_crossings_main_index+1:end)) 3])+index_of_z_crossings_main_index + % sampledifference = abs(valid_z_crossings_indices(lCounter)-valid_z_crossings_indices(lCounter-1)); + % %the difference between two adjacent peaks in the SRA is taken + % %as IPI estimate + % IPIhisttime(iCounter,time_counter,histcounter) = sampledifference*one_sample_is_a_time_of; + % %the amplitude of the SRA at the start of the SRA interval is + % %taken as the IPIweight + % IPIhistweight(iCounter,time_counter,histcounter) = SRAcorr(iCounter,time_counter,valid_z_crossings_indices(lCounter-1)); + % histcounter = histcounter + 1; + % end + + %time_counter = time_counter+1; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/userProgramsTim/track_formants_from_IPI_guy_olde.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,184 @@ +function [iih,IPIhisttime,IPIhistweight]=track_formants_from_IPI_guy(IFRAN_pattern, sfreq) +% +% tracks the formants according to an analysis proposed in Secker-Walker +% JASA 1990, section V.A +% Tim Jürgens, February 2011, code from Guy Brown included +% +% input: IFRAN_pattern: pattern of the auditory model (dependend on the number of modules used) +% first dimension: frequency channel, +% second dimension: time (samples) +% sfreq: sampling frequency +% output: iih: interpeak-interval histogram, matrix very similar +% the plot 5 in the Secker-Walker paper +% +% +% + + +time_axis = 0:1/sfreq:(size(IFRAN_pattern,2)-1)/sfreq; + +%find how many samples of AN_pattern are 10ms and 3ms +%one_sample_is_a_time_of = time_axis(2); +[tmp, start_time_index] = min(abs(0-time_axis)); +[tmp, stop10_time_index] = min(abs(0.010-time_axis)); +number_of_samples10ms = stop10_time_index - start_time_index; + +[tmp, stop3_time_index] = min(abs(0.003-time_axis)); +number_of_samples3ms = stop3_time_index - start_time_index; +every_3ms = 1:number_of_samples3ms:size(IFRAN_pattern,2)-number_of_samples10ms; + +hamm_window = hamming(11); +halfHamming = (length(hamm_window)-1)/2; + +% window normalization + +norm = conv(ones(1,floor(number_of_samples10ms/2)),hamm_window); +norm = norm(5+1:end-5)'; +win_size = number_of_samples10ms; +half_win_size = floor(win_size/2); +hop_size = number_of_samples3ms; + + +%pre-allocation due to speed +%Acorr = zeros(size(IFRAN_pattern,1),size(every_3ms,2),number_of_samples10ms*2+1); +%RAcorr = zeros(size(IFRAN_pattern,1),size(every_3ms,2),number_of_samples10ms*2+1); +%SRAcorr = zeros(size(IFRAN_pattern,1),size(every_3ms,2),number_of_samples10ms*2+1-10); +IPIhisttime = zeros(size(IFRAN_pattern,1),size(every_3ms,2),3); +IPIhistweight = zeros(size(IFRAN_pattern,1),size(every_3ms,2),3); %maximum 3 peaks from the SRA +iih = zeros(half_win_size,size(every_3ms,2)); + + + + +for iCounter = 1:size(IFRAN_pattern,1) %each channel + fprintf('Channel No. %i\n',iCounter); + %time_counter = 1; + %for jCounter = every_3ms %every 3ms time segment + + + + %% Guy's code + % enframe this signal + + frames = enframe(IFRAN_pattern(iCounter,:),win_size,hop_size); + + % compute the autocorrelation + + acf = real(ifft(abs(fft(frames,[],2)).^2,[],2)); + acf(acf<0)=0; + acf = sqrt(acf(:,1:half_win_size)); + + % smooth with hamming window and take the root + + for frame=1:size(acf,1) + + %%debug + %if iCounter == 130 + % disp('here'); + %end + + + sra = conv(acf(frame,:),hamm_window); + sra = sra(halfHamming+1:end-halfHamming)./norm'; + df = [0 ; diff(sra')]; + idx = find((df(1:end-1)>=0)&(df(2:end)<0)); + % interpolate + a=df(idx); + b=df(idx+1); + idx = (idx-1+a./(a-b)); + % get rid of a zero peak, if it exists + idx = idx(idx>1); + %include the zeroth peak + %idx = [1 idx']'; + % peak values corresponding to these intervals + amp = interp1(1:length(sra),sra,idx,'linear'); + % if required, remove peaks that lie below the mean sra + % note that we disregard the value at zero delay + %if (params.removePeaksBelowMean) + valid = find(amp>1.2*mean(sra(1:end))); + idx = idx(valid); + amp = amp(valid); + %end + % only use the first four peaks (three intervals) + idx = idx(1:min(4,length(idx))); + % find the intervals + interval = diff(idx); + % now histogram the intervals + if (~isempty(interval)) + for k=1:length(interval), + iih(round(interval(k)),frame) = iih(round(interval(k)),frame)+amp(k); + IPIhisttime(iCounter,frame,k) = interval(k)/sfreq; + IPIhistweight(iCounter,frame,k) = amp(k); + end + end + + end + + + + + %% end Guy's code + + + % %take the autocorrelation (ACF) of a 10ms-segment of each channel + % Acorr(iCounter,time_counter,:) = xcorr(IFRAN_pattern(iCounter,jCounter:number_of_samples10ms+jCounter),'biased'); %biased scales the ACF by the reciprocal of the length of the segment + % %root calculation + % RAcorr(iCounter,time_counter,:) = sqrt(abs(Acorr(iCounter,time_counter,:))); + % + % %smoothing using the 11-point hamming window + % for kCounter = 6:size(RAcorr(iCounter,time_counter,:),3)-5 %start with 6 and end with 5 samples + % %less the length of time_axis not to get in conflict with the length of + % %the hamm_window + % SRAcorr(iCounter,time_counter,kCounter-5) = ... + % squeeze(RAcorr(iCounter,time_counter,(kCounter-5):(kCounter+5)))'*hamm_window./sum(hamm_window); + % end + % + % %mean value of actual SRA + % SRA_mean = mean(SRAcorr(iCounter,time_counter,:)); + % + % %find signed zero-crossings of the first derivative (=difference) + % z_crossings_indices = find(diff(sign(diff(squeeze(SRAcorr(iCounter,time_counter,:))))) < 0)+1; %+1 is necessary, because diff shortens vector by 1 + % middle_index = ceil(size(SRAcorr(iCounter,time_counter,:),3)/2); + % + % validCounter = 1; + % valid_z_crossings_indices = []; + % %find valid zero-crossings (peak higher than meanvalue and within first 5 ms of SRA) + % for lCounter = 1:length(z_crossings_indices) + % if (SRAcorr(iCounter,time_counter,z_crossings_indices(lCounter)) > SRA_mean) && ... + % (abs(z_crossings_indices(lCounter)-middle_index) < round(number_of_samples10ms/2)); + % valid_z_crossings_indices(validCounter) = z_crossings_indices(lCounter); + % validCounter = validCounter+1; + % end + % end + % + % %find main peak in the ACF + % [tmp,index_of_z_crossings_main_index] = min(abs(middle_index-valid_z_crossings_indices)); + % if ~tmp == 0 + % disp('middle peak not appropriately found'); + % end + % + % %%% for debugging + % % if iCounter == 130 + % % disp('here'); + % % figure, plot(squeeze(SRAcorr(iCounter,time_counter,:))); + % % hold on, plot([1 length(squeeze(SRAcorr(iCounter,time_counter,:)))],[SRA_mean SRA_mean],'r-'); + % % end + % %%% + % + % %generate IPI-histogram: take the first 3 intervals of SRAcorr + % %(positive delay) in the first 5 ms + % histcounter = 1; + % for lCounter = index_of_z_crossings_main_index+1:min([length(valid_z_crossings_indices(index_of_z_crossings_main_index+1:end)) 3])+index_of_z_crossings_main_index + % sampledifference = abs(valid_z_crossings_indices(lCounter)-valid_z_crossings_indices(lCounter-1)); + % %the difference between two adjacent peaks in the SRA is taken + % %as IPI estimate + % IPIhisttime(iCounter,time_counter,histcounter) = sampledifference*one_sample_is_a_time_of; + % %the amplitude of the SRA at the start of the SRA interval is + % %taken as the IPIweight + % IPIhistweight(iCounter,time_counter,histcounter) = SRAcorr(iCounter,time_counter,valid_z_crossings_indices(lCounter-1)); + % histcounter = histcounter + 1; + % end + + %time_counter = time_counter+1; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/utilities/UTIL_Butterworth.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,31 @@ +function x=UTIL_Butterworth (x, dt, fl, fu, order) +% UTIL_Butterworth (x, dt, fu, fl, order) +% Taken from Yuel and beauchamp page 261 +% NB error in their table for K (see their text) +% x is original signal +% x is the filtered output (approx 3 dB down at cutoff for first order filter) +% fu, fl upper and lower cutoff +% order is the number of times the filter is applied +% (approx 6 dB attenuation is corrected) + +sampleRate=1/dt; +if 4*fu>sampleRate + error(['UTIL_Butterworth: sample rate ' num2str(sampleRate) ' is too low. Sampling rate should be >' num2str(4*fu)]) +end + + +q=(pi*dt*(fu-fl)); +J=1/(1+ cot(q)); +K= (2*cos(pi*dt*(fu+fl)))/((1+tan(q))*cos(q)); +L= (tan(q)-1)/(tan(q)+1); +b=[J 0 -J]; +a=[1 -K -L]; + +[numChannels nDataPoints]=size(x); +for channelNumber=1:numChannels + for j=1:order + % x2 compensate for 6 dB attenuation + x(channelNumber,:)=2*filter(b, a, x(channelNumber,:)); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/utilities/UTIL_CAPgenerator.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,64 @@ +function wholeNerveCAP = UTIL_CAPgenerator... + (ANresponse, dt, BFlist, numANfibers, plotCAP) +% +% Generates a compound action potential by convolving an impulse repsonse, +% as defined by Mark Chertoff (2004, JASA), with the response of the +% auditory nerve. +% +% +% -e(-k*time)*SIN(2*PI()*f*time) +% +% mu(t) = e^-kt * sin(omega*t) +% omega = 2 * pi * f +% +% +% Robert T. Ferry +% 01st May 2008 +% +% + +nChannels=length(BFlist); +[r nSpikeEpochs]=size(ANresponse); + +wholeNerveCAP = []; +channelCAP = []; +e = exp(1); +k = 1000; +impulseDuration = 0.01; +impulseFrequency = 750; +impulseTime = dt:dt:impulseDuration; +impulseResponse = -e.^(-k*impulseTime).*sin(2*pi*impulseFrequency*impulseTime); +impulseResponse=impulseResponse-mean(impulseResponse); + +% WholeNerveCAP +ANoutput = sum(ANresponse, 1); +convolvedWholeNerveCAP = conv(ANoutput, impulseResponse(1,:)); +% truncate +convolvedWholeNerveCAP=convolvedWholeNerveCAP(1:nSpikeEpochs); + +% apply measurement time constant +sampleRate=1/dt; +upperFreq=sampleRate/4; +lowPassCutOff=40; +wholeNerveCAP = UTIL_Butterworth(convolvedWholeNerveCAP, dt, lowPassCutOff, upperFreq, 1); +% or do not do this +% wholeNerveCAP = convolvedWholeNerveCAP; + +% Plot output? + +CAPtime = dt:dt:dt*length(wholeNerveCAP); + +if (plotCAP == 1) + figure(9) + + subplot(3,1,1), plot(impulseTime, impulseResponse) + title('Impulse response') + xlabel('Time (s)'), ylabel('Amplitude (Pa)') + xlim([0 max(impulseTime)]), ylim([-inf inf]) + + subplot(3,1,2), plot(CAPtime, wholeNerveCAP) + title(['AN CAP (whole-nerve) ' num2str(length(BFlist)) ' channels' num2str(numANfibers) ' fibers/ch']) + xlabel('Time (s)'), ylabel('Amplitude (Pa)') + xlim([0 max(CAPtime)]), ylim([-inf inf]) + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/utilities/UTIL_FFT.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,39 @@ +function [fft_powerdB, fft_phase, frequencies, fft_ampdB]=... + UTIL_FFT(getFFTsignal, dt, referenceAmplitude) +% UTIL_FFT +% [fft_powerdB, fft_phase, frequencies, fft_ampdB]= UTIL_FFT(getFFTsignal, dt, referenceAmplitude) + +x=length(getFFTsignal); % adjust the length of the signal to a power of 2 for FFT +n=2; +while n<=x + n=n*2; +end +n=round(n/2); +getFFTsignal=getFFTsignal(1:n); +frequencies = (1/dt)*(1:n/2)/n; + +fft_result = fft(getFFTsignal, n); % Compute FFT of the input signal. +fft_power = fft_result .* conj(fft_result); % / n; % Compute power spectrum. +% Dividing by 'n' we get the power spectral density. +% fft_power = fft_result .* conj(fft_result) / n; % Compute power spectralDensity. + +% Prepare the FFT for display +fft_power=fft_power(1:length(fft_power)/2); % remove mirror frequencies +% fft_powerdB = UTIL_amp2dB (fft_power, max(fft_power)); % convert to dB +fft_powerdB = 10*log10(fft_power); % convert to dB + +% amplitude spectrum +if nargin<3 + referenceAmplitude=28e-6; % for SPL +end +% refAmpdB= referenceAmplitude^0.5; +fft_ampdB = 10*log10(fft_power/referenceAmplitude); % convert to dB +% peak_fft_powerdBSPL=max(fft_ampdB) + + +fft_phase = angle(fft_result); % Compute the phase spectrum. +fft_phase=fft_phase(1:length(fft_phase)/2); % remove mirror phases +jags=find(diff(fft_phase)>0); % unwrap phase +for i=jags, + fft_phase(i+1:end)=fft_phase(i+1:end)-2*pi; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/utilities/UTIL_cascadePlot.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,26 @@ +function UTIL_cascadePlot(toPlot, colValues) +% % useful code +[nChannels nLags]=size(toPlot); + +% cunning code to represent channels as parallel lines +[nRows nCols]=size(toPlot); +if nChannels<2 + error('UTIL_cascadePlot: only one row found') +end + +% a is the height to be added to each channel +a=max(max(toPlot))*(0:nRows-1)'; + +% peakGain emphasises the peak height +% peaks can be higher than the space between channels +peakGain=10; +x=peakGain*toPlot+repmat(a,1,nCols); +x=nRows*x/max(max(x)); + +for row=1:nRows-1 + x(row,:)=min(x(row,:), x(row+1,:)); +end + +plot(colValues, x','k') +ylim([0 nRows]) +
--- a/utilities/UTIL_plotMatrix.m Thu Oct 06 15:43:20 2011 +0100 +++ b/utilities/UTIL_plotMatrix.m Mon Nov 28 13:34:28 2011 +0000 @@ -2,80 +2,75 @@ % UTIL_plotMatrix general purpose plotting utility for plotting the results % of the MAP auditory model. % All plots are placed in subplots of a figure (default figure 1). +% % Input arguments: % 'toPlot' is matrix (either numeric or logical) % 'method' is a structure containing plot instructions % -% surface plots have log z-axis when all values are >1 -% -% when calling this function, always increment the subPlotNo in method -% method.subPlotNo=method.subPlotNo+1; -% method.subPlotNo=method.subPlotNo; -% % mandatory parameters: -% method.displaydt xValues spacing between data points -% method.numPlots total number of subPlots in the figure -% method.subPlotNo number of this plot -% method.yValues mandatory only for 3D plots +% method.displaydt xValues spacing between data points +% method.yValues yaxis labels mandatory only for 3D plots % % optional -% method.figureNo normally figure(1) -% method.zValuesRange [min max] value pair to define yaxis limits -% method.zValuesRange [min max] CLIMS for 3-D plot -% method.yLabel (string) y-axis label -% method.xLabel (string) x-axis label -% method.title (string) subplot title +% method.figureNo default figure(1) +% method.numPlots number of subPlots in the figure (default=1) +% method.subPlotNo number of this plot (default=1) +% method.zValuesRange [min max] value pair to define yaxis limits +% method.zValuesRange [min max] CLIMS for 3-D plot +% method.yLabel (string) y-axis label +% method.minyMaxy y-axis limits +% method.xLabel (string) x-axis label +% method.title (string) subplot title % method.bar =1, to force bar histogram (single channel only) -% method.view 3D plot 'view' settings e.g. [-6 40] -% method.axes (handle) used for writing to GUIs (specifies panel) -% method.maxPixels maximum number of pixels (used to speed plotting) -% method.blackOnWhite =1; % NB inverts display for 2D plots -% method.forceLog positive values are put on log z-scale -% method.rasterDotSize min value is 1 -% method.defaultFontSize -% method.timeStart default=dt -% method.defaultTextColor default ='w' -% method.defaultAxesColor default = 'w' -% method.nCols default=1 -% method.nRows default=method.numPlots +% method.view 3D plot 'view' settings e.g. [-6 40] +% method.axes (handle) where to plot (overules all others) +% method.maxPixels maximum number of pixels (used to speed plotting) +% method.blackOnWhite =1; inverts display for 2D plots +% method.forceLog positive values are put on log z-scale +% method.rasterDotSize min value is 1 +% method.defaultFontSize deafult= 12 +% method.timeStart default= dt +% method.defaultTextColor default ='k' +% method.defaultAxesColor default ='k' +% method.nCols default = 1 (layout for subplots) +% method.nRows default=method.numPlots (layout for subplots +% method.segmentNumber plot only this segment while 'hold on' % -% useful paste for calling program -% method.numPlots=method.numPlots; -% method.subPlotNo=method.subPlotNo+1; -% method.subPlotNo=method.subPlotNo; -% dt=dt; -% method.yValues=method.nonlinCF; % for 3D plots only -% -% method.figureNo=1; -% method.yLabel='useThisLabel'; -% method.xLabel='use this label'; -% method.title='myTitle'; -% +% e.g. % UTIL_plotMatrix(toPlot, method) dt=method.displaydt; +[r cols]=size(toPlot); +if cols==1 + % toPlot should be a wide matrix or a long vector + toPlot=toPlot'; +end + +if ~isfield(method,'numPlots') || isempty(method.numPlots) + method.numPlots =1; + method.subPlotNo =1; +end + if ~isfield(method,'figureNo') || isempty(method.figureNo) method.figureNo=99; end + % if ~isfield(method,'zValuesRange') || isempty(method.zValuesRange) % method.zValuesRange=[-inf inf]; % end % set some defaults -if ~isfield( method,'plotDivider') || isempty(method.plotDivider) - method.plotDivider=0; -end if ~isfield( method,'blackOnWhite') || isempty(method.blackOnWhite) - method.blackOnWhite=0; + method.blackOnWhite=0; end if ~isfield(method,'timeStart')|| isempty(method.timeStart) - method.timeStart=dt; + method.timeStart=dt; end if ~isfield(method,'objectDuration') || isempty(method.objectDuration) [nRows nCols]=size(toPlot); method.objectDuration=dt*nCols; end if ~isfield(method,'defaultFontSize') || isempty(method.defaultFontSize) - method.defaultFontSize=12; + method.defaultFontSize=12; end if ~isfield(method,'defaultTextColor') || isempty(method.defaultTextColor) method.defaultTextColor='k'; @@ -84,7 +79,7 @@ defaultTextColor='k'; end if ~isfield( method,'defaultAxesColor') || isempty(method.defaultAxesColor) - method.defaultAxesColor=defaultTextColor; + method.defaultAxesColor=defaultTextColor; end defaultAxesColor=method.defaultAxesColor; @@ -107,8 +102,11 @@ % if both are specified, 'axes' takes priority figure(method.figureNo) if isfield(method,'axes') && ~isempty(method.axes) - % select an axis in some location other than 'figure' - h=axes(method.axes); + % user defines where to plot it + axes(method.axes); + method.numPlots =1; + method.subPlotNo =1; + else % now using a regular figure if method.subPlotNo>method.numPlots; @@ -119,7 +117,7 @@ if isfield(method,'segmentNumber') && ~isempty(method.segmentNumber)... && method.segmentNumber>1 - % in multi-segment mode do not clear the image + % in multi-segment mode do not clear the image % from the previous segment hold on else @@ -139,6 +137,14 @@ yValues=1:numYvalues; end +if round(numYvalues/length(yValues))>1 + % case where the plot matrix is double height (e.g. LSR+HSR) + yValues=[yValues yValues]; + method.plotDivider=1; +else + method.plotDivider=0; +end + % Now start the plot. % 3D plotting for 4 or more channels % otherwise special cases for fewer channels @@ -195,11 +201,9 @@ end otherwise % >3 channels: surface plot - % add white line to separate HSR and LSR + % add line to separate HSR and LSR if method.plotDivider && size(toPlot,1) > 2 [r c]=size(toPlot); -% if isempty(method.zValuesRange), method.zValuesRange=0; end -% mm=method.zValuesRange(2); emptyLine=max(max(toPlot))*ones(2,c); halfway=round(r/2); toPlot=[toPlot(1:halfway,:); emptyLine; toPlot(halfway+1:end,:)]; @@ -225,28 +229,12 @@ % zValuesRange if isfield(method,'zValuesRange') ... && ~isempty(method.zValuesRange) - % zValuesRange gives the max and min values -% a=method.zValuesRange(1); -% b=method.zValuesRange(2); -% toPlot=(toPlot-a)/(b-a); -% clims=[0 1]; clims=(method.zValuesRange); imagesc(xValues, yValues, toPlot, clims), axis xy; %NB assumes equally spaced y-values else % automatically scaled imagesc(xValues, yValues, toPlot), axis xy; %NB assumes equally spaced y-values - % if ~isfield(method,'zValuesRange') - % method.zValuesRange=[-inf inf]; - % end - % - % if method.blackOnWhite - % % NB plotted values have negative sign for black on white - % caxis([-method.zValuesRange(2) -method.zValuesRange(1)]) - % else - % caxis(method.zValuesRange) - % end - if ~isfield(method,'zValuesRange')... || isempty(method.zValuesRange) method.zValuesRange=[-inf inf]; @@ -264,9 +252,8 @@ % NB segmentation may shorten signal duration [r c]=size(toPlot); imageDuration=c*method.displaydt; - xlim([0 imageDuration]) -% xlim([0 method.objectDuration]) - + xlim([0 imageDuration]) + % yaxis if isfield(method,'minyMaxy') && ~isempty(method.minyMaxy) ylim(method.minyMaxy) @@ -275,17 +262,29 @@ ylim([min(yValues) max(yValues)]) end end - if min(yValues)>1 % put channel array on a log scale + + % y-axis design yTickLabels + if min(yValues)>1 tickValues=[min(yValues) max(yValues)]; + tickLabels=num2str(tickValues'); + if method.plotDivider && size(toPlot,1) > 2 + % show min/max yvalues with slight shift + yList=yValues; + yValues=1:length(yValues); + tickValues=[1 halfway-1 halfway+2 length(yValues)]; + idx=[1 halfway halfway+1 length(yValues)]; + tickLabels=num2str(yList(idx)'); + imagesc(xValues, yValues, toPlot), axis xy; + end + set(gca,'ytick',tickValues) - set(gca,'ytickLabel', strvcat(num2str(tickValues'))) + set(gca,'ytickLabel', strvcat(tickLabels)) set(gca,'FontSize', method.defaultFontSize) end end -else % is logical - +else % is logical % logical implies spike array. Use raster plot [y,x]=find(toPlot); %locate all spikes: y is fiber number ie row x=x*dt+method.timeStart; % x is time @@ -314,6 +313,7 @@ plot([0 c*method.displaydt],[halfWayUp halfWayUp], 'b') hold off end + end set(gca, 'xcolor', defaultAxesColor) @@ -330,7 +330,7 @@ set(gca,'xtick',[],'FontSize', method.defaultFontSize) if method.subPlotNo==method.numPlots if isfield(method,'xLabel') && ~isempty(method.xLabel) -% set(gca,'ActivePositionProperty','outerposition') + % set(gca,'ActivePositionProperty','outerposition') % xlabel(method.xLabel) xlabel(method.xLabel, 'FontSize', method.defaultFontSize, 'color', defaultTextColor) end
--- a/utilities/UTIL_printTabTable.m Thu Oct 06 15:43:20 2011 +0100 +++ b/utilities/UTIL_printTabTable.m Mon Nov 28 13:34:28 2011 +0000 @@ -2,7 +2,7 @@ % printTabTable prints a matrix as a table with tabs %headers are optional %headers=strvcat('firstname', 'secondname') -% printTabTable([1 2; 3 4],strvcat('a1','a2')); +% UTIL_printTabTable([1 2; 3 4],strvcat('a1','a2')); if nargin<3 format='%g';
--- a/utilities/UTIL_showMAP.m Thu Oct 06 15:43:20 2011 +0100 +++ b/utilities/UTIL_showMAP.m Mon Nov 28 13:34:28 2011 +0000 @@ -1,62 +1,71 @@ -function UTIL_showMAP (showMapOptions, paramChanges) +function UTIL_showMAP (showMapOptions) % UTIL_showMAP produces summaries of the output from MAP's mmost recent run -% All MAP outputs are stored in global variables and UTIL_showMAP -% simply assumes that they are in place. +% All MAP_14 outputs are stored in global variables and UTIL_showMAP +% simply assumes that they are in place. It uses whatever it there. % -% showMapOptions +% showMapOptions: % showMapOptions.printModelParameters=1; % print model parameters % showMapOptions.showModelOutput=1; % plot all stages output % showMapOptions.printFiringRates=1; % mean activity at all stages % showMapOptions.showACF=1; % SACF (probabilities only) % showMapOptions.showEfferent=1; % plot of efferent activity -% showMapOptions.surfProbability=0; % HSR (probability) surf plot -% showMapOptions.fileName=[]; % parameter filename for plot title - +% showMapOptions.surfAN=0; % AN output surf plot +% showMapOptions.fileName=''; % parameter filename for plot title +% showMapOptions.PSTHbinwidth=0.001 % binwidth for surface plots +% showMapOptions.colorbar=1; % request color bar if appropriate +% showMapOptions.view=[0 90]; % dbstop if warning -global dt ANdt savedBFlist saveAN_spikesOrProbability saveMAPparamsName... - savedInputSignal OMEextEarPressure TMoutput OMEoutput ARattenuation ... - DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV... - IHCoutput ANprobRateOutput ANoutput savePavailable ANtauCas ... - CNtauGk CNoutput ICoutput ICmembraneOutput ICfiberTypeRates ... - MOCattenuation -global OMEParams DRNLParams IHC_cilia_RPParams IHCpreSynapseParams -global AN_IHCsynapseParams MacGregorParams MacGregorMultiParams +% Discover results left behind by MAP1_14 +global savedParamChanges savedBFlist saveAN_spikesOrProbability ... + saveMAPparamsName savedInputSignal dt dtSpikes ... + OMEextEarPressure TMoutput OMEoutput DRNLoutput... + IHC_cilia_output IHCrestingCiliaCond IHCrestingV... + IHCoutput ANprobRateOutput ANoutput savePavailable saveNavailable ... + ANtauCas CNtauGk CNoutput ICoutput ICmembraneOutput ICfiberTypeRates... + MOCattenuation ARattenuation + +% Find parameters created in MAPparams<name> +global inputStimulusParams OMEParams DRNLParams IHC_cilia_RPParams +global IHCpreSynapseParams AN_IHCsynapseParams +global MacGregorParams MacGregorMultiParams filteredSACFParams global ICrate restorePath=path; addpath ( ['..' filesep 'utilities'], ['..' filesep 'parameterStore']) -if nargin<1 - showMapOptions=[]; -end +if nargin<1, showMapOptions=[]; end +paramChanges=savedParamChanges; + % defaults (plot staged outputs and print rates only) if options omitted if ~isfield(showMapOptions,'printModelParameters') showMapOptions.printModelParameters=0; end -if ~isfield(showMapOptions,'showModelOutput'),showMapOptions.showModelOutput=1;end -if ~isfield(showMapOptions,'printFiringRates'),showMapOptions.printFiringRates=1;end +if ~isfield(showMapOptions,'showModelOutput'),showMapOptions.showModelOutput=0;end +if ~isfield(showMapOptions,'printFiringRates'),showMapOptions.printFiringRates=0;end +if ~isfield(showMapOptions,'surfAN'),showMapOptions.surfAN=0;end +if ~isfield(showMapOptions,'ICrates'),showMapOptions.ICrates=0;end if ~isfield(showMapOptions,'showACF'),showMapOptions.showACF=0;end if ~isfield(showMapOptions,'showEfferent'),showMapOptions.showEfferent=0;end -if ~isfield(showMapOptions,'surfProbability'),showMapOptions.surfProbability=0;end +if ~isfield(showMapOptions,'PSTHbinwidth'),showMapOptions.PSTHbinwidth=0.001;end if ~isfield(showMapOptions,'fileName'),showMapOptions.fileName=[];end -if ~isfield(showMapOptions,'surfSpikes'),showMapOptions.surfSpikes=0;end -if ~isfield(showMapOptions,'ICrates'),showMapOptions.ICrates=0;end +if ~isfield(showMapOptions,'colorbar'),showMapOptions.colorbar=1;end +if ~isfield(showMapOptions,'view'),showMapOptions.view=[-25 80];end +if ~isfield(showMapOptions,'ICrasterPlot'),showMapOptions.ICrasterPlot=0;end %% send all model parameters to command window if showMapOptions.printModelParameters % Read parameters from MAPparams<***> file in 'parameterStore' folder % and print out all parameters - if nargin>1 cmd=['MAPparams' saveMAPparamsName '(-1, 1/dt, 1, paramChanges);']; eval(cmd); - else - cmd=['MAPparams' saveMAPparamsName '(-1, 1/dt, 1);']; - eval(cmd); - disp(' no parameter changes found') - end end +% ignore zero elements in signal +signalRMS=mean(savedInputSignal(find(savedInputSignal)).^2)^0.5; +signalRMSdb=20*log10(signalRMS/20e-6); +nANfiberTypes=length(ANtauCas); + %% summarise firing rates in command window if showMapOptions.printFiringRates %% print summary firing rates @@ -64,7 +73,6 @@ disp('summary') disp(['AR: ' num2str(min(ARattenuation))]) disp(['MOC: ' num2str(min(min(MOCattenuation)))]) - nANfiberTypes=length(ANtauCas); if strcmp(saveAN_spikesOrProbability, 'spikes') nANfibers=size(ANoutput,1); nHSRfibers=nANfibers/nANfiberTypes; @@ -93,10 +101,6 @@ %% figure (99): display output from all model stages if showMapOptions.showModelOutput plotInstructions.figureNo=99; - - % ignore zero elements in signal - signalRMS=mean(savedInputSignal(find(savedInputSignal)).^2)^0.5; - signalRMSdb=20*log10(signalRMS/20e-6); % plot signal (1) plotInstructions.displaydt=dt; @@ -104,9 +108,7 @@ plotInstructions.subPlotNo=1; plotInstructions.title=... ['stimulus (Pa). ' num2str(signalRMSdb, '%4.0f') ' dB SPL']; - r=size(savedInputSignal,1); - if r==1, savedInputSignal=savedInputSignal'; end - UTIL_plotMatrix(savedInputSignal', plotInstructions); + UTIL_plotMatrix(savedInputSignal, plotInstructions); % stapes (2) plotInstructions.subPlotNo=2; @@ -119,17 +121,17 @@ [r c]=size(DRNLoutput); if r>1 plotInstructions.title= ['BM displacement']; - UTIL_plotMatrix(abs(DRNLoutput), plotInstructions); + UTIL_plotMatrix(abs(DRNLoutput), plotInstructions); else plotInstructions.title= ['BM displacement. BF=' ... num2str(savedBFlist) ' Hz']; - UTIL_plotMatrix(DRNLoutput, plotInstructions); + UTIL_plotMatrix(DRNLoutput, plotInstructions); end switch saveAN_spikesOrProbability case 'spikes' % AN (4) - plotInstructions.displaydt=ANdt; + plotInstructions.displaydt=dtSpikes; plotInstructions.title='AN'; plotInstructions.subPlotNo=4; plotInstructions.yLabel='BF'; @@ -146,7 +148,7 @@ UTIL_plotMatrix(ANoutput, plotInstructions); % CN (5) - plotInstructions.displaydt=ANdt; + plotInstructions.displaydt=dtSpikes; plotInstructions.subPlotNo=5; plotInstructions.title='CN spikes'; if sum(sum(CNoutput))<100 @@ -155,7 +157,7 @@ UTIL_plotMatrix(CNoutput, plotInstructions); % IC (6) - plotInstructions.displaydt=ANdt; + plotInstructions.displaydt=dtSpikes; plotInstructions.subPlotNo=6; plotInstructions.title='Brainstem 2nd level'; if size(ICoutput,1)>1 @@ -172,64 +174,79 @@ end otherwise % AN rate based on probability of firing - PSTHbinWidth=0.001; + PSTHbinWidth=0.0005; PSTH= UTIL_PSTHmakerb(ANprobRateOutput, dt, PSTHbinWidth); -% PSTH = makeANsmooth(PSTH, 1/PSTHbinWidth); + plotInstructions=[]; plotInstructions.displaydt=PSTHbinWidth; + plotInstructions.plotDivider=0; plotInstructions.numPlots=2; plotInstructions.subPlotNo=2; plotInstructions.yLabel='BF'; - plotInstructions.xLabel='time'; + plotInstructions.yValues=savedBFlist; + plotInstructions.xLabel='time (s)'; plotInstructions.zValuesRange= [0 300]; + if nANfiberTypes>1, - plotInstructions.yLabel='LSR HSR'; + plotInstructions.yLabel='LSR HSR'; plotInstructions.plotDivider=1; end plotInstructions.title='AN - spike rate'; UTIL_plotMatrix(PSTH, plotInstructions); shading interp - colorbar('southOutside') + if showMapOptions.colorbar, colorbar('southOutside'), end end set(gcf,'name','MAP output') end -if showMapOptions.surfProbability &&... + +%% surface plot of AN response +% probability +if showMapOptions.surfAN &&... strcmp(saveAN_spikesOrProbability,'probability') && ... length(savedBFlist)>2 - %% surface plot of probability - % select only HSR fibers - figure(97), clf - HSRprobOutput= ANprobRateOutput(end-length(savedBFlist)+1:end,:); - PSTHbinWidth=0.001; - PSTH=UTIL_PSTHmakerb(HSRprobOutput, ANdt, PSTHbinWidth); - [nY nX]=size(PSTH); - time=PSTHbinWidth*(1:nX); - surf(time, savedBFlist, PSTH) - shading interp - set(gca, 'yScale','log') - xlim([0 max(time)]) - ylim([0 max(savedBFlist)]) - zlim([0 1000]) - xlabel('time (s)') - ylabel('best frequency (Hz)') - zlabel('spike rate') - view([-20 60]) - % view([0 90]) - disp(['max max AN: ' num2str(max(max(PSTH)))]) - - title (['firing probability of HSR fibers only. Level= ' ... - num2str(signalRMSdb,'% 3.0f') ' dB']) - colorbar('southOutside') + % select only HSR fibers + figure(97), clf + PSTHbinWidth=showMapOptions.PSTHbinwidth; + PSTH= UTIL_PSTHmakerb(... + ANprobRateOutput(end-length(savedBFlist)+1:end,:), ... + dt, PSTHbinWidth); + [nY nX]=size(PSTH); + time=PSTHbinWidth*(1:nX); + surf(time, savedBFlist, PSTH) + caxis([0 500]) + shading interp + set(gca, 'yScale','log') + xlim([0 max(time)]) + ylim([0 max(savedBFlist)]) + zlim([0 500]) + myFontSize=10; + xlabel('time (s)', 'fontsize',myFontSize) + ylabel('BF (Hz)', 'fontsize',myFontSize) + zlabel('spike rate') + view(showMapOptions.view) + set(gca,'ytick',[500 1000 2000 8000],'fontSize',myFontSize) + title (['AN response. Level= ' ... + num2str(signalRMSdb,'% 3.0f') ' dB'... + ' binwidth= ' num2str(1000*PSTHbinWidth) ' s']) + if showMapOptions.colorbar, colorbar('southOutside'), end end -%% surface plot of AN spikes -if showMapOptions.surfSpikes ... - && strcmp(saveAN_spikesOrProbability, 'spikes') +%% surfAN ('spikes') +if showMapOptions.surfAN ... + && strcmp(saveAN_spikesOrProbability, 'spikes')... + && length(savedBFlist)>2 figure(97), clf + % combine fibers across channels + nFibersPerChannel=AN_IHCsynapseParams.numFibers; + [r nEpochs]=size(ANoutput); + nChannels=round(r/nFibersPerChannel); + x=reshape(ANoutput,nChannels,nFibersPerChannel,nEpochs); + x=squeeze(sum(x,2)); % select only HSR fibers at the bottom of the matrix - ANoutput= ANoutput(end-length(savedBFlist)+1:end,:); - PSTHbinWidth=0.005; % 1 ms bins - PSTH=UTIL_makePSTH(ANoutput, ANdt, PSTHbinWidth); + HSRoutput= x(end-length(savedBFlist)+1:end,:); + PSTH=HSRoutput; + PSTHbinWidth=showMapOptions.PSTHbinwidth; + PSTH=UTIL_makePSTH(HSRoutput, dtSpikes, PSTHbinWidth); [nY nX]=size(PSTH); time=PSTHbinWidth*(1:nX); surf(time, savedBFlist, PSTH) @@ -241,12 +258,42 @@ xlabel('time (s)') ylabel('best frequency (Hz)') zlabel('spike rate') - view([-20 60]) - % view([0 90]) + view(showMapOptions.view) title ([showMapOptions.fileName ': ' num2str(signalRMSdb,'% 3.0f') ' dB']) set(97,'name', 'spikes surface plot') end +%% IC raster plot +if showMapOptions.ICrasterPlot &&... + strcmp(saveAN_spikesOrProbability,'spikes') && ... + length(savedBFlist)>2 + figure(91), clf + plotInstructions=[]; + plotInstructions.numPlots=2; + plotInstructions.subPlotNo=2; + plotInstructions.title=... + ['IC raster plot']; + plotInstructions.figureNo=91; + plotInstructions.displaydt=dtSpikes; + plotInstructions.title='Brainstem 2nd level'; + plotInstructions.yLabel='BF'; + plotInstructions.yValues= savedBFlist; + + if size(ICoutput,1)>1 + if sum(sum(ICoutput))<100 + plotInstructions.rasterDotSize=3; + end + UTIL_plotMatrix(ICoutput, plotInstructions); + end + + % plot signal (1) + plotInstructions.displaydt=dt; + plotInstructions.subPlotNo=1; + plotInstructions.title=... + ['stimulus (Pa). ' num2str(signalRMSdb, '%4.0f') ' dB SPL']; + UTIL_plotMatrix(savedInputSignal, plotInstructions); + +end %% figure(98) plot efferent control values as dB if showMapOptions.showEfferent @@ -259,9 +306,9 @@ plotInstructions.zValuesRange= [-1 1]; plotInstructions.title= ['RMS level='... num2str(signalRMSdb, '%4.0f') ' dB SPL']; - UTIL_plotMatrix(savedInputSignal', plotInstructions); - - + UTIL_plotMatrix(savedInputSignal, plotInstructions); + + plotInstructions.subPlotNo=2; plotInstructions.zValuesRange=[ -25 0]; plotInstructions.title= ['AR stapes attenuation (dB); tau='... @@ -275,60 +322,37 @@ plotInstructions.yLabel= 'dB'; if strcmp(saveAN_spikesOrProbability,'spikes') rate2atten=DRNLParams.rateToAttenuationFactor; - plotInstructions.title= ['MOC atten; tau=' ... - num2str(DRNLParams.MOCtau) '; factor='... - num2str(rate2atten, '%6.4f')]; + plotInstructions.title= ['MOC atten; tau=' ... + num2str(DRNLParams.MOCtau) '; factor='... + num2str(rate2atten, '%6.4f')]; else rate2atten=DRNLParams.rateToAttenuationFactorProb; - plotInstructions.title= ['MOC atten; tauProb=' ... - num2str(DRNLParams.MOCtauProb) '; factor='... - num2str(rate2atten, '%6.4f')]; + plotInstructions.title= ['MOC atten; tauProb=' ... + num2str(DRNLParams.MOCtauProb) '; factor='... + num2str(rate2atten, '%6.4f')]; end plotInstructions.zValuesRange=[0 -DRNLParams.minMOCattenuationdB+5]; UTIL_plotMatrix(-20*log10(MOCattenuation), plotInstructions); hold on [r c]=size(MOCattenuation); - if r>2 - colorbar('southOutside') + if r>2 && showMapOptions.colorbar + colorbar('southOutside') end set(plotInstructions.figureNo, 'name', 'AR/ MOC') - + binwidth=0.1; [PSTH ]=UTIL_PSTHmaker(20*log10(MOCattenuation), dt, binwidth); PSTH=PSTH*length(PSTH)/length(MOCattenuation); t=binwidth:binwidth:binwidth*length(PSTH); fprintf('\n\n') -% UTIL_printTabTable([t' PSTH']) -% fprintf('\n\n') - + % UTIL_printTabTable([t' PSTH']) + % fprintf('\n\n') + end -%% ACF plot if required +%% ACF plot if showMapOptions.showACF tic - method.dt=dt; - method.segmentNo=1; - method.nonlinCF=savedBFlist; - - minPitch= 80; maxPitch= 4000; numPitches=100; % specify lags - pitches=10.^ linspace(log10(minPitch), log10(maxPitch),numPitches); - pitches=fliplr(pitches); - filteredSACFParams.lags=1./pitches; % autocorrelation lags vector - filteredSACFParams.acfTau= .003; % time constant of running ACF - filteredSACFParams.lambda= 0.12; % slower filter to smooth ACF - filteredSACFParams.lambda= 0.01; % slower filter to smooth ACF - - filteredSACFParams.plotACFs=0; % special plot (see code) - filteredSACFParams.plotFilteredSACF=0; % 0 plots unfiltered ACFs - filteredSACFParams.plotMoviePauses=.3; % special plot (see code) - - filteredSACFParams.usePressnitzer=0; % attenuates ACF at long lags - filteredSACFParams.lagsProcedure= 'useAllLags'; - % filteredSACFParams.lagsProcedure= 'useBernsteinLagWeights'; - % filteredSACFParams.lagsProcedure= 'omitShortLags'; - filteredSACFParams.criterionForOmittingLags=3; - filteredSACFParams.plotACFsInterval=200; - if filteredSACFParams.plotACFs % plot original waveform on ACF plot figure(13), clf @@ -339,6 +363,19 @@ title(['stimulus: ' num2str(signalRMSdb, '%4.0f') ' dB SPL']); end + % compute ACF + switch saveAN_spikesOrProbability + case 'probability' + inputToACF=ANprobRateOutput(end-length(savedBFlist)+1:end,:); + otherwise + inputToACF=ANoutput; + end + + disp ('computing ACF...') + [P, BFlist, sacf, boundaryValue] = ... + filteredSACF(inputToACF, dt, savedBFlist, filteredSACFParams); + disp(' ACF done.') + % plot original waveform on summary/smoothed ACF plot figure(96), clf subplot(2,1,1) @@ -347,29 +384,20 @@ xlim([0 t(end)]) title(['stimulus: ' num2str(signalRMSdb, '%4.0f') ' dB SPL']); - - % compute ACF - switch saveAN_spikesOrProbability - case 'probability' - inputToACF=ANprobRateOutput.^0.5; - otherwise - inputToACF=ANoutput; - end - - disp ('computing ACF...') - [P, BFlist, sacf, boundaryValue] = ... - filteredSACF(inputToACF, method, filteredSACFParams); - disp(' ACF done.') - - % SACF + % plot SACF + figure(96) subplot(2,1,2) imagesc(P) +% surf(filteredSACFParams.lags, t, P) ylabel('periodicities (Hz)') xlabel('time (s)') - title(['running smoothed (root) SACF. ' saveAN_spikesOrProbability ' input']) - pt=[1 get(gca,'ytick')]; % force top xtick to show + title(['running smoothed SACF. ' saveAN_spikesOrProbability ' input']) + pt=[1 get(gca,'ytick')]; % force top ytick to show set(gca,'ytick',pt) + pitches=1./filteredSACFParams.lags; % autocorrelation lags vector set(gca,'ytickLabel', round(pitches(pt))) + [nCH nTimes]=size(P); + t=dt:dt:dt*nTimes; tt=get(gca,'xtick'); set(gca,'xtickLabel', round(100*t(tt))/100) end @@ -395,22 +423,22 @@ % end function ANsmooth = makeANsmooth(ANresponse, sampleRate, winSize, hopSize) - if nargin < 3 - winSize = 25; %default 25 ms window - end - if nargin < 4 - hopSize = 10; %default 10 ms jump between windows - end - - winSizeSamples = round(winSize*sampleRate/1000); - hopSizeSamples = round(hopSize*sampleRate/1000); - - % smooth - hann = hanning(winSizeSamples); - - ANsmooth = [];%Cannot pre-allocate a size as it is unknown until the enframing - for chan = 1:size(ANresponse,1) - f = enframe(ANresponse(chan,:), hann, hopSizeSamples); - ANsmooth(chan,:) = mean(f,2)'; %#ok<AGROW> see above comment - end +if nargin < 3 + winSize = 25; %default 25 ms window +end +if nargin < 4 + hopSize = 10; %default 10 ms jump between windows +end + +winSizeSamples = round(winSize*sampleRate/1000); +hopSizeSamples = round(hopSize*sampleRate/1000); + +% smooth +hann = hanning(winSizeSamples); + +ANsmooth = [];%Cannot pre-allocate a size as it is unknown until the enframing +for chan = 1:size(ANresponse,1) + f = enframe(ANresponse(chan,:), hann, hopSizeSamples); + ANsmooth(chan,:) = mean(f,2)'; %#ok<AGROW> see above comment +end % end% ------ OF makeANsmooth
--- a/utilities/stimulusCreate.m Thu Oct 06 15:43:20 2011 +0100 +++ b/utilities/stimulusCreate.m Mon Nov 28 13:34:28 2011 +0000 @@ -399,7 +399,7 @@ stimulus=applyGaussianRamps(stimulus, -stim.rampOnDur, 1/dt); end - % Initial silence + % begin silence % start with a signal of the right length consisting of zeros signal=zeros(1, globalStimParams.nSignalPoints); % identify start of stimulus @@ -414,7 +414,7 @@ % time signature time=dt:dt:dt*length(signal); - % figure(22), plot(signal), title([num2str(ear) ' - ' num2str(componentNo)]),pause (1) + % figure(22), plot(signal), title([num2str(ear) ' - ' num2str(componentNo)]),pause (1) try % create a column vector and trap if no signal has been created @@ -574,33 +574,63 @@ function stimulus=makeOHIOtones(globalStimParams, stimComponents) % Generates a stimulus consisting of one or more 10-ms tones -% Tones are presented at 10-ms intervals +% The length of the list of frequencies determines the numberof tones +% Tones are either presented at 10-ms intervals or simultaneously +% all tones are individually ramped % Each tone has its own amplitude and its own ramp. frequencies=stimComponents.frequencies; amplitudesdB=stimComponents.amplitudesdB; nFrequencies=length(frequencies); +if amplitudesdB==-100 + % catch trial + amplitudesdB=repmat(-100,1,nFrequencies); +end + dt=globalStimParams.dt; + toneDuration=.010; time=dt:dt:toneDuration; -rampDuration=stimComponents.rampOnDur; +% fixed ramp, silenceDuration, toneDuration +rampDuration=0.005; rampTime=dt:dt:rampDuration; -ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ones(1,length(time)-length(rampTime))]; +ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; ramp=ramp.*fliplr(ramp); -stimulus= zeros(1,round((toneDuration+globalStimParams.beginSilences(end))/dt)+1); +silenceDuration=0.010; +silenceDurationLength=round(silenceDuration/dt); +initialSilence=zeros(1,silenceDurationLength); + +silenceToneDuration=toneDuration + silenceDuration; +silenceToneDurationLength=round(silenceToneDuration/dt); + +% OHIO spect produces simultaneous tones +if strcmp(stimComponents.OHIOtype,'OHIOspect') + totalDuration=silenceToneDuration; +else + totalDuration=silenceToneDuration*nFrequencies; +end + +totalDurationLength=round(totalDuration/dt); +stimulus=zeros(1,totalDurationLength); +toneBeginPTR=1; for i=1:nFrequencies - toneBeginPTR=round(globalStimParams.beginSilences(i)/dt)+1; - frequency=frequencies(i); dBSPL=amplitudesdB(i); amplitude=28e-6* 10.^(dBSPL/20); tone=amplitude*sin(2*pi*frequency*time); tone=tone.*ramp; - stimulus(toneBeginPTR:toneBeginPTR+length(tone)-1)=stimulus(toneBeginPTR:toneBeginPTR+length(tone)-1)+tone; + % stimulus is normally zeros except for OHIOspect + stimulus(toneBeginPTR:toneBeginPTR+silenceToneDurationLength-1)=... + [initialSilence tone]+... + stimulus(toneBeginPTR:toneBeginPTR+silenceToneDurationLength-1); + if ~strcmp(stimComponents.OHIOtype,'OHIOspect') + toneBeginPTR=toneBeginPTR+silenceToneDurationLength; + end end % figure(2), plot( stimulus')
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/utilities/temp.m Mon Nov 28 13:34:28 2011 +0000 @@ -0,0 +1,39 @@ +frequencies=[1000 1250]; +amplitudesdB=[20 23]; +nFrequencies=length(frequencies); + +dt=0.0001; + +toneDuration=.010; +time=dt:dt:toneDuration; + +% fixed ramp, silenceDuration, toneDuration +rampDuration=0.005; +rampTime=dt:dt:rampDuration; +ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ... + ones(1,length(time)-length(rampTime))]; +ramp=ramp.*fliplr(ramp); + +silenceDuration=0.010; +silenceDurationLength=round(silenceDuration/dt); +initialSilence=zeros(1,silenceDurationLength); + +silenceToneDuration=toneDuration + silenceDuration; +silenceToneDurationLength=round(silenceToneDuration/dt); + +totalDuration=silenceToneDuration*nFrequencies; +totalDurationLength=round(totalDuration/dt); +stimulus=zeros(1,totalDurationLength); +toneBeginPTR=1; + +for i=1:nFrequencies + frequency=frequencies(i); + dBSPL=amplitudesdB(i); + amplitude=28e-6* 10.^(dBSPL/20); + tone=amplitude*sin(2*pi*frequency*time); + tone=tone.*ramp; + stimulus(toneBeginPTR:toneBeginPTR+silenceToneDurationLength-1)=... + [initialSilence tone]; + toneBeginPTR=toneBeginPTR+silenceToneDurationLength; +end +figure(2), plot( stimulus')