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view testPrograms/testLibermanMOC_DPOAE.m @ 38:c2204b18f4a2 tip
End nov big change
| author | Ray Meddis <rmeddis@essex.ac.uk> |
|---|---|
| date | Mon, 28 Nov 2011 13:34:28 +0000 |
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| children |
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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)