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             y= nonlinOutput.*(MOC* DRNLa);  % linear section.
             % compress those parts of the signal above the compression
             % threshold
             abs_x = abs(nonlinOutput);
             abs_x = abs(y);
             idx=find(abs_x>DRNLcompressionThreshold);
             if ~isempty(idx)>0
                 y(idx)=sign(nonlinOutput(idx)).*...
                 y(idx)=sign(y(idx)).*...
                     (DRNLb*abs_x(idx).^DRNLc);
             end
             nonlinOutput=y;

     % levels= 50;   nLevels=length(levels);
     relativeFrequencies=[0.25    .5   .75  1  1.25 1.5    2];
     % relativeFrequencies=1;
     relativeFrequencies=1;
     % refBMdisplacement is the displacement of the BM at threshold
     % 1 nm disp at  threshold (9 kHz, Ruggero)

     % DRNL nonlinear path
     DRNLParams.a=3e4;     % nonlinear path gain (below compression threshold)
     % DRNLParams.a=3e2;     % DRNL.a=0 means no OHCs (no nonlinear path)
     DRNLParams.a=3e2;     % DRNL.a=0 means no OHCs (no nonlinear path)
     DRNLParams.b=8e-6;    % *compression threshold raised compression
     % DRNLParams.b=1;    % b=1 means no compression
-...
     DRNLParams.MOCdelay = efferentDelay;            % must be < segment length!
     % 'spikes' model: MOC based on brainstem spiking activity (HSR)
     DRNLParams.rateToAttenuationFactor = .009;  % strength of MOC
     DRNLParams.rateToAttenuationFactor = .009;  % strength of MOC
     DRNLParams.rateToAttenuationFactor = .004;  % strength of MOC
     %      DRNLParams.rateToAttenuationFactor = 0;  % strength of MOC
     % 'probability' model: MOC based on AN spiking activity (HSR)
     DRNLParams.rateToAttenuationFactorProb = .007;  % strength of MOC
     DRNLParams.rateToAttenuationFactorProb = .0;  % strength of MOC
     DRNLParams.rateToAttenuationFactorProb = .002;  % strength of MOC
     % DRNLParams.rateToAttenuationFactorProb = .0;  % strength of MOC
     DRNLParams.MOCtau =.03;                         % smoothing for MOC
     DRNLParams.MOCrateThreshold =50;                % set to AN rate threshold

     function showMAP (options)
     function showMAP ()
     % defaults
     % options.showModelParameters=1;
     % options.showModelOutput=1;
-...
         CNoutput  ICoutput ICmembraneOutput ICfiberTypeRates MOCattenuation
     global OMEParams DRNLParams IHC_cilia_RPParams IHCpreSynapseParams
     global AN_IHCsynapseParams MacGregorParams MacGregorMultiParams
     global showMapOptions
     restorePath=path;
-...
         colorbar
     end
     if options.surfProbability
     %% surface plot of probability
     if options.surfProbability
         figure(97), clf
         % select only HSR fibers at the bottom of the matrix
         ANprobRateOutput= ANprobRateOutput(end-length(savedBFlist)+1:end,:);
         [nY nX]=size(ANprobRateOutput);
         time=dt*(1:nX);
         surf(time, savedBFlist, ANprobRateOutput)
         PSTHbinWidth=0.001;
         PSTH=UTIL_PSTHmakerb(ANprobRateOutput, ANdt, PSTHbinWidth);
         [nY nX]=size(PSTH);
         PSTH=PSTH(:,200:end);
         [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])
         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])
         title ([options.fileName ':   ' num2str(signalRMSdb,'% 3.0f') ' dB'])
     end

     signalType= 'tones';
     duration=0.100;                 % seconds
     % duration=0.020;                 % seconds
     sampleRate= 64000;
     sampleRate= 100000;
     % toneFrequency= 250:250:8000;    % harmonic sequence (Hz)
     toneFrequency= 2000;            % or a pure tone (Hz8
     rampDuration=.005;              % seconds
     % or
     signalType= 'file';
     fileName='twister_44kHz';
     % fileName='new-da-44khz';
     % % or
     % signalType= 'file';
     % fileName='twister_44kHz';
     % % fileName='new-da-44khz';
     % mix with an optional second file?
     mixerFile=[];
     %or
     mixerFile='babble';
     leveldBSPL2=-60;
     % mixerFile='babble';
     % leveldBSPL2=30;
     %% #4 rms level
     % signal details
-...
     %     'DRNLParams.rateToAttenuationFactorProb = 0;'};
     %% delare showMap options
     showMapOptions=[];  % use defaults
     global showMapOptions
     % or (example: show everything including an smoothed SACF output
     showMapOptions.showModelParameters=1;
-...
     showMapOptions.showEfferent=1;
     if strcmp(AN_spikesOrProbability, 'probability')
         showMapOptions.surfProbability=1;
     else
         showMapOptions.surfProbability=0;
     end
     if strcmp(signalType, 'file')
         showMapOptions.fileName=fileName;
     else
         showMapOptions.fileName=[];
     end
     %% Generate stimuli
-...
             rms=(mean(inputSignal.^2))^0.5;
             amp=targetRMS/rms;
             inputSignal=inputSignal*amp;
             silence=zeros(10000,1);
             inputSignal=[silence; inputSignal];
             if ~isempty(mixerFile)
                 [inputSignal2 sampleRate]=wavread(mixerFile);
                 inputSignal2=inputSignal2(:,1);
-...
     toc
     % the model run is now complete. Now display the results
     showMAP(showMapOptions)
     showMAP
     for i=1:length(paramChanges)
     disp(paramChanges{i})
     end

     function [PSTH ]=UTIL_PSTHmakerb(inputData, dt, PSTHbinWidth)
     % UTIL_PSTHmakerb averages mean values into bins.
     % No corrections are applied
     % usage:
     %	PSTH=UTIL_PSTHmaker(inputData, method)
+    %
     % arguments
     %	inputData is a channel x time matrix
     %	PSTH is the reduced matrix, the sum of all elements in the bin
+    %
     [numChannels numDataPoints]= size(inputData);
     % Multiple fibers is the same as repeat trials
     % Consolidate data into a histogram
     dataPointsPerBin=round(PSTHbinWidth/dt);
     if dataPointsPerBin<=1;
     % 	Too few data points
     	PSTH=inputData;
     	return
     end
     numBins=floor(numDataPoints/dataPointsPerBin);
     PSTH=zeros(numChannels,numBins);
     % take care that signal length is an integer multiple of bin size
     %  by dropping the last unuseable values
     useableDataLength=numBins*dataPointsPerBin;
     inputData=inputData(:,1:useableDataLength);
     for ch=1:numChannels
     	% Convert each channel into a matrix where each column represents
     	% the content of a single PSTH bin
     	PSTH2D=reshape (inputData(ch,:), dataPointsPerBin, numBins );
     	% and sum within each bin (across the rows
     	PSTH(ch,:)=mean (PSTH2D,1);
     end

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MAP

Revision 21:c489ebada16e