update functions

spatiotemporalWhiteNoise/code spatiotemporal white noise/BC_pred.m

@@ -1,142 +0,0 @@
-function [pred_output]=BC_pred(STA_output)
-%[pred_output]=BC_pred(STA_output)
-%this function shows the prinicpal calculation for the the prediction to 
-%the white noise stimulus with the linear-nonlinear model (LN-model)for 
-%continous voltage signals of bipolar cells (BC).
-%Inputs:
-%               STA_output =  output of the BC_STA function; containing the
-%               STA and stimulus signal      
-%
-%Outpus:        pred_output = structure containing the output of the
-%               prediction
-%
-%
-% Note: the function is built only for cells recorded without frozen frames.
-% But additional comments are made for cells with frozen frames.
-% code by HS, last modified Feb 2021
-
-%-------------------------------------------------------------------------------
-%% 1. recompute STA and nonlinearity without the stimulus signal that we want to predict
-%%Important we want to predict a stimulus sequence that was not used to
-%%build the model (filter and nonlinearity). Therefore we have to redo the
-%%filter(STA) and nonlinearity without the stimulus-sequence that we want 
-%to predict the response(see Methods of Schreyer&Gollisch,2021).
-%If we recorded Frozen frames, we predict the response to the frozen
-%frames from the model (filter and nonlinearity) we build from the running
-%frames.
-
-%% 1.1 STA
-%E.g. to make it easy: we want to predict the response to the last 300frames of the white
-%noise stimulus
-%take out the stimulus sequence you want to predict
-stimPred=STA_output.stimulus2D_zoom(:,end-299:end); %the last 300frames
-%voltage trace to predict
-voltPred=STA_output.membranPotAVG(end-299:end); %the response to the last 300 frames
-%build the STA without the last 300 frames
-[STA_norm]=buildSTA ...
-    (STA_output.stimulus2D_zoom(:,1:end-300),...
-    STA_output.membranPotAVG(1:end-300),STA_output.STA_fnbr);
-STA3D_norm=reshape(STA_norm,size(STA_output.rangeY_zoom,2),size(STA_output.rangeX_zoom,2), ...
-    size(STA_norm,2));% Y-zoom checkers (monitor axis), X-zoom checkers (monitor axis) and Z (STA_fnbr)
-
-%% 1.2 Nonlinearity
-%build the NL from the data without the last 300 frames
-generator_signal=filter2(STA_norm,STA_output.stimulus2D_zoom(:,1:end-300),'valid');
-%get the corresponding membrane potential responses
-y_axisNL_unsorted=STA_output.membranPotAVG(STA_output.STA_fnbr:end-300); %the first value is at the length of the filter
-
-%sort the x axis
-[NL_xAxis,indx]=sort(generator_signal);
-%sort the corresponding y axis
-NL_yAxis=y_axisNL_unsorted(indx);
-%do a binning with percentile to have same amount of data in each bin
-%make 40 bins
-nbrbins=40;
-bins= doBin(NL_xAxis,NL_yAxis,nbrbins);
-
-%-------------------------------------------------------------------------------
-%% 2. compute the prediction
-%% 2.1 convolution
-%convolve the new stimulus sequence (not used for prediciton) with the
-%normalized STA
-stim2pred=filter2(STA_norm,stimPred,'valid');
-
-%% 2.2 compute the prediction through linear interpolation
-NL_x=bins.NL_xbin;
-NL_y=bins.NL_ybin;
-%example of how to make the prediction through linear interpolation
-predMemP_y=nan(1,size(stim2pred,2));
-for kk=1:size(stim2pred,2)
-    nbrTemp=stim2pred(kk); %first x-axis value to predict
-    [xIndxS,xIndxL]= getIndex(NL_x,nbrTemp,1); %get those values that are on left and rigth
-    xValueS=NL_x(xIndxS);
-    xValueL=NL_x(xIndxL);
-    %do the linear interpolation
-    [polyCoeff] = polyfit([xValueS;xValueL],[NL_y(xIndxS);...
-        NL_y(xIndxL)],1);
-    predMemP_y(kk)=polyCoeff(1)*nbrTemp+polyCoeff(2);
-end
-
-%-------------------------------------------------------------------------------
-%% 3. evaluate the prediction performance
-%pearson correlation
-corrcoeff_temp=corrcoef(voltPred(STA_output.STA_fnbr:end),predMemP_y); %the first value is at the length of the filter
-pearCorrSqrt=corrcoeff_temp(2)^2;
-
-%-------------------------------------------------------------------------------
-%% 4. plot the output
-figure;
-%STA
-subplot(2,2,1)
-[~,indx_Zoom]=max(abs(STA3D_norm(:))); %max intensity value of STA
-[~,~,posZ]=ind2sub(size(STA3D_norm),indx_Zoom); %get the frame position
-imagesc(STA3D_norm(:,:,posZ))
-colormap('gray'); %how the sitmulus was shown
-axis equal; axis tight;
-title('STA best frame')
-%NL
-subplot(2,2,2)
-plot(NL_xAxis,NL_yAxis,'.','color','k') %plot non-binned signal
-hold on;
-plot(bins.NL_xbin,bins.NL_ybin,'o','markerfacecolor','r','markeredgecolor','r') %plot binned signal
-title('NL')
-axis tight
-xlabel('generator signal')
-ylabel('voltage (mV)')
-%prediction
-subplot(2,2,3:4)
-plot(voltPred(STA_output.STA_fnbr:end),'k') %original membrane potential
-hold on;
-plot(predMemP_y,'r')%predicted membrane potential
-title(['pCorr:' mat2str(round(pearCorrSqrt,2)) ' Filename: ' STA_output.filename])
-xlabel('stimulus frames')
-ylabel('voltage (ms)')
-legend('original response','predicted response')
-
-%-------------------------------------------------------------------------------
-%% 5. add variables into the output
-pred_output.pearCorrSqrt=pearCorrSqrt;
-
-end
-
-function [IndS,IndL]= getIndex(x,valConv,Points)
-IndS=find(x<valConv,Points,'last'); %find the last 5 closest smaller values
-IndL=find(x>=valConv,Points);
-if isempty(IndS)|| length(IndS)<Points %if you are on the left border
-    if isempty(IndS) %take value from 1:10
-        IndL=find(x>=valConv,Points*2);
-        IndS=IndL(1);
-        IndL=IndL(2:end);
-    else
-        IndL=find(x>=valConv,Points*2-length(IndS));
-    end
-elseif isempty(IndL)|| length(IndL)<Points %if you are on the right border
-    if isempty(IndL) %take value from 1:10
-        IndS=find(x<valConv,Points*2,'last');
-        IndL=IndS(end);
-        IndS=IndS(1:end-1);
-    else
-        IndS=find(x<valConv,Points*2-length(IndL));
-    end
-end
-end