Code natural movies

naturalMovies/code natural movies/BC_mainNaturalMovies.m

@@ -2,15 +2,18 @@
 %analysis from voltage recordings of bipolar cells (BC) under natural
 %stimuli
 %Check out the Documentation_Data.pdf, which contains relevant information
-%Check BC_mainWhiteNoise -> from here you get the filter and output
+%Check BC_mainWhiteNoise.m -> from here you get the filter and output
 %nonlinearity for performing the prediction to the movie.
 clear;
 close all;
 
-%% Prediction to the natural movies
-loadData='Z:\Manuscript\BC_XY_LN\Puplishing\DataAvailable\naturalMovies\'; %path of the BC files for spatiotemporal white noise (has to be adapted to your own path)
+%% Preprocessing of the natural movie contrast signal
+loadData='Z:\DataAvailable\naturalMovies\'; %path of the BC files for natural movies (has to be adapted to your own path)
 filename='16d200016Comp.mat'; %BC file to analyse: for example here we use file 16d200016Comp from cell 5, recorded in retina 2 on 161219
 Hz=25; % given in the excel file LogInfo_NaturalMovies.xlsx for the corresponding file
 movienbr=4; %given in the excel file LogInfo_NaturalMovies.xlsx for the corresponding file
-[naturalMovie_output]=BC_predNaturalMovies(loadData,filename,movieData,Hz,STA_output,NL_output);%example code to get the prediction to natural movies from the spatiotemporal white noise LN-model
+pixelSize=9; %given in the excel file LogInfo_SpatioTemporalWhiteNoise.xlsx from the white noise stimulus of the corresponding cell
+[movieSignal]=BC_getNaturalMovies(pixelSize);%example code to preprocess the natural movies
 
+%% Prediction of the response to the movie from the LN-model with spatiotemporal white noise
+[naturalMovie_output]=BC_predNaturalMovies(loadData,filename,movieSignal,STA_output,NL_output,Hz);%example code to get the prediction to natural movies from the spatiotemporal white noise LN-model

naturalMovies/code natural movies/BC_mainPatternedSpot.m

@@ -0,0 +1,14 @@
+%Guide for the patterned spot analysis
+clear;
+close all;
+
+%% Preprocessing of the natural movie contrast signal
+loadData='Z:\Manuscript\BC_XY_LN\Puplishing\DataAvailable\naturalMovies\'; %path of the BC files for natural movies (has to be adapted to your own path)
+filename='16d200016Comp.mat'; %BC file to analyse: for example here we use file 16d200016Comp from cell 5, recorded in retina 2 on 161219
+Hz=25; % given in the excel file LogInfo_NaturalMovies.xlsx for the corresponding file
+movienbr=4; %given in the excel file LogInfo_NaturalMovies.xlsx for the corresponding file
+pixelSize=9; %given in the excel file LogInfo_SpatioTemporalWhiteNoise.xlsx from the white noise stimulus of the corresponding cell
+[movieSignal]=BC_getNaturalMovies(pixelSize);%example code to preprocess the natural movies
+
+%% Prediction of the response to the movie from the LN-model with spatiotemporal white noise
+[naturalMovie_output]=BC_predNaturalMovies(loadData,filename,movieSignal,STA_output,NL_output,Hz);%example code to get the prediction to natural movies from the spatiotemporal white noise LN-model

naturalMovies/code natural movies/BC_predNaturalMovies.m

@@ -0,0 +1,115 @@
+function [naturalMovie_output]=BC_predNaturalMovies(loadData,filename,Hz,STA_output,NL_output)
+%[naturalMovie_output]=BC_predNaturalMovies(loadData,filename,pixelSize,Hz,STA_output,NL_output)
+%this function shows the prinicpal calculation for the prediction of the 
+%response to the natural movie, from the linear-nonlinear model (LN-model)
+%computed with white noise stimulus.
+%Inputs:
+%               loadData = the folder path of the natural movie files
+%               filename = the filename of the BC of interest
+%               movieData
+%               Hz = stimulus update rate
+%               STA_output =  output of the BC_STA function; containing the
+%               STA (filter) of the white noise stimulus  
+%               NL_output =  output of the BC_NL function; containing the
+%               output nonlinearity of the white noise stimulus                 
+%
+%Outpus:        naturalMovie_output = structure containing the output of the
+%               prediction
+%
+%
+
+%-------------------------------------------------------------------------------
+%% 1. Get the movie contrast values
+%the movies can be downloaded under the following
+%link:https://zenodo.org/record/46481. See documentation.data.pdf for
+%further instructions, also which frames to take.
+%they have to be prepared as follow:
+
+
+
+%-------------------------------------------------------------------------------
+%% 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

naturalMovies/code natural movies/avgPerFrameNatMovies.m

@@ -0,0 +1,27 @@
+function [membranPotAVG]=avgPerFrameNatMovies ...
+    (membranePot,ttls_ms,MovieFrames,Hz)
+%function [membranPotAVG]=avgPerFrameNatMovies ...
+%         (membranePot,ttls_ms_correctedPulse)
+%builds the average membrane potential between frames for the natural movie
+% INPUT:        membranePos -> membrane Potential
+%               ttls_ms -> pulses in ms
+%               MovieFrames -> number of frames per movie
+%
+% OUTPUT:       membranPotAVG -> average membrane potential per frames
+
+%make the average membrane potential between two stimulus frames
+membranPotAVG=zeros(MovieFrames,size(ttls_ms,2));
+for jj=1:size(ttls_ms,2) %nbr of trials
+    for kk=1:size(ttls_ms,1) % nbr of frames
+        if kk==size(ttls_ms,1) %for the last frame
+            membranPotAVG(kk,jj)=mean(membranePot(ttls_ms(kk,jj) ...
+                :ttls_ms(kk,jj)+floor(1000/Hz)-1)); %because you dont have the pulse for when the frame ends, take the 40ms after the last frame to include the response to that frame.
+        else
+            membranPotAVG(kk,jj)=mean(membranePot(ttls_ms(kk,jj) ...
+                :(ttls_ms(kk+1,jj)-1)));  %detail: -1ms because the next ms is already the start of the new stimulus frame
+        end
+    end
+end
+
+end
+