comparison testPrograms/testPeriphery.m @ 29:b51bf546ca3f

physiologyProb

+function testPeriphery (BMlocations, paramsName)
+% 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.
+%
+
+global    DRNLParams
+
+savePath=path;
+
+addpath (['..' filesep 'utilities'],['..' filesep 'MAP'])
+
+levels=-10:10:90;   nLevels=length(levels);
+% levels= 50;   nLevels=length(levels);
+
+% relativeFrequencies=[0.25    .5   .75  1  1.25 1.5    2];
+relativeFrequencies=1;
+
+% refBMdisplacement is the displacement of the BM at threshold
+% 1 nm disp at  threshold (9 kHz, Ruggero)
+refBMdisplacement= 1e-8;            % adjusted for 10 nm at 1 kHz
+
+toneDuration=.200;
+rampDuration=0.01;
+silenceDuration=0.01;
+
+sampleRate=30000;
+
+dbstop if error
+figure(3), clf
+% set(gcf,'position',[276    33   331   645])
+set(gcf,'name','DRNL - BM')
+
+finalSummary=[];
+nBFs=length(BMlocations);
+BFno=0; plotCount=0;
+for BF=BMlocations
+    BFno=BFno+1;
+    plotCount=plotCount+nBFs;
+    stimulusFrequencies=BF* relativeFrequencies;
+    nFrequencies=length(stimulusFrequencies);
+
+    peakAmpBM=zeros(nLevels,nFrequencies);
+    peakAmpBMdB=NaN(nLevels,nFrequencies);
+    peakEfferent=NaN(nLevels,nFrequencies);
+    peakAREfferent=NaN(nLevels,nFrequencies);
+
+
+    levelNo=0;
+    for leveldB=levels
+        disp(['level= ' num2str(leveldB)])
+        levelNo=levelNo+1;
+
+        freqNo=0;
+        for frequency=stimulusFrequencies
+            freqNo=freqNo+1;
+
+            % Generate stimuli
+            globalStimParams.FS=sampleRate;
+            globalStimParams.overallDuration=...
+                toneDuration+silenceDuration;  % s
+            stim.type='tone';
+            stim.phases='sin';
+            stim.toneDuration=toneDuration;
+            stim.frequencies=frequency;
+            stim.amplitudesdB=leveldB;
+            stim.beginSilence=silenceDuration;
+            stim.rampOnDur=rampDuration;
+            stim.rampOffDur=rampDuration;
+            doPlot=0;
+            inputSignal=stimulusCreate(globalStimParams, stim, doPlot);
+            inputSignal=inputSignal(:,1)';
+
+            %% run the model
+            MAPparamsName=paramsName;
+            AN_spikesOrProbability='probability';
+            % spikes are slow but can be used to study MOC using IC units
+            % AN_spikesOrProbability='spikes';
+
+            global DRNLoutput MOCattenuation ARattenuation IHCoutput
+            MAP1_14(inputSignal, sampleRate, BF, ...
+                MAPparamsName, AN_spikesOrProbability);
+
+            DRNLresponse=IHCoutput;
+            peakAmp=max(max(...
+                DRNLresponse(:, end-round(length(DRNLresponse)/2):end)));
+            peakAmpBM(levelNo,freqNo)=peakAmp;
+            if peakAmp>0
+            peakAmpBMdB(levelNo,freqNo)=...
+                20*log10(peakAmp/refBMdisplacement);
+            else
+                peakAmpBMdB(levelNo,freqNo)=peakAmp;
+            end
+            peakEfferent(levelNo,freqNo)=min(min(MOCattenuation));
+            peakAREfferent(levelNo,freqNo)=min(min(ARattenuation));
+
+        end  % tone frequency
+    end  % level
+
+    %% analyses results and plot
+
+    % BM I/O plot (top panel)
+    figure(3)
+    subplot(3,nBFs,BFno), cla
+    plot(levels,peakAmpBMdB, 'linewidth',2)
+    hold on, plot(levels, repmat(refBMdisplacement,1,length(levels)))
+    hold off
+    title(['BF=' num2str(BF,'%5.0f') ' - ' paramsName])
+    xlabel('level')
+    %     set(gca,'xtick',levels),  grid on
+    if length(levels)>1,xlim([min(levels) max(levels)]), end
+    ylabel(['dB re:' num2str(refBMdisplacement,'%6.1e') 'm'])
+    ylim([-20 50])
+    set(gca,'ytick',[-10 0 10 20 40])
+    %     legend({num2str(stimulusFrequencies')}, 'location', 'EastOutside')
+    UTIL_printTabTable([levels' peakAmpBMdB], ...
+        num2str([0 stimulusFrequencies]','%5.0f'), '%5.0f')
+    finalSummary=[finalSummary peakAmpBMdB];
+
+    % Tuning curve
+    if length(relativeFrequencies)>2
+        figure(3), subplot(3,nBFs, nBFs+BFno)
+        %         contour(stimulusFrequencies,levels,peakAmpBM,...
+        %             [refBMdisplacement refBMdisplacement],'r')
+        contour(stimulusFrequencies,levels,peakAmpBM,...
+            refBMdisplacement.*[1 5 10 50 100])
+        title(['tuning curve at ' num2str(refBMdisplacement) 'm']);
+        ylabel('level (dB) at reference')
+        xlim([100 10000])
+        hold on
+        set(gca,'xscale','log')
+    end
+
+
+    % MOC contribution
+    figure(3)
+    subplot(3,nBFs,2*nBFs+BFno), cla
+    plot(levels,20*log10(peakEfferent), 'linewidth',2)
+    ylabel('MOC (dB attenuation)'), xlabel('level')
+    title(['peak MOC: model= ' AN_spikesOrProbability])
+    grid on
+    if length(levels)>1, xlim([min(levels) max(levels)]), end
+
+    % AR contribution
+    hold on
+    plot(levels,20*log10(peakAREfferent), 'r')
+    hold off
+
+end % best frequency
+
+UTIL_showStructureSummary(DRNLParams, 'DRNLParams', 10)
+
+    UTIL_printTabTable([levels' finalSummary], ...
+        num2str([0 stimulusFrequencies]','%5.0f'), '%5.0f')
+
+path(savePath);