update functions

spatiotemporalWhiteNoise/code spatiotemporal white noise/BC_STA.m

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-function [STA_output]=BC_STA(loadData,filename,pixelSize,seed,Hz)
-%[STA_output]=BC_STA(loadData,filename,pixelSize,seed)
-%this function loads the voltage trace of the selected bipolar cell (BC)
-%and perorms a response-weighted average analysis analogous to the commonn
-%spike-triggered average (STA). For simplicity we refer to the analysis
-%STA,even though we work with a continous voltage signal and not spikes. 
-%Inputs:        
-%               loadData = the folder path of the white noise files
-%               filename = the filename of the BC of interest
-%               pixelSize = the size of a square (checker) in monitor
-%               pixels (see Documentation_Data.pdf)
-%               seed = seed for the random number generator
-%               Hz = stimulus update rate
-%
-%Outpus:        STA_output = structure containing the STA, stimulus signal,
-%               averaged membrane potential for calculating the output
-%               nonlinearity
-%
-%
-% 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
-
-%% constants
-%OLED dimensions for regenerating the stimulus
-monitorXPixels=800; %x pixels of OLED monitor; 
-monitorYPixels=600; %y pixels of OLED monitor; 
-microPerPix=2.5; %one pixel=2.5 micrometer in size
-STA_TimeBack=1000; %time in ms you want to go back for generating the STA (to get smaller data I use here 1000ms, in the manuscript we use 2000ms). 
-
-%-------------------------------------------------------------------------------
-%% 1.load cell
-data=load([loadData,filename]); %load the file
-
-%-------------------------------------------------------------------------------
-%% 2. regenerate the stimulus
-Nframes=length(data.ttls_ms); %number of frames shown (how many times the stimulus was updated) 
-Nx=ceil(monitorXPixels/pixelSize); %number of squares shown X dimension
-Ny=ceil(monitorYPixels/pixelSize); %number of squares shown Y dimension
-nbrCheckers_total=Nx*Ny; %total number of checkers/square presented on the screen
-stimulus = recreateBinaryWhiteNoiseStimulus(Nx, Ny, Nframes, seed); %the function is available under: https://github.com/gollischlab/RecreateWhiteNoiseStimuliWithMatlab 
-stimulus =stimulus(:,:,1:end-1);%take the last frame out
-%the output stimulus has 3 dimensions: Y,X (nbr of checkers on Y and X axis) and time. Easiest to understand the stimulus is by
-%plotting for example the first stimulus frame presented on the retina:
-%imagesc(stimulus(:,:,1));
-%Note: the stimulus values are drawn onto the
-%retina from the lower left corner, yet matlab displays it from the upper left corner.
-%Therefore the y axis has to be flipped when displaying.
-%set(gca,'YDir','normal');
-%For me the easiest is to directly flip the stimulus y dimension (and then I dont flip the axis when displaying):
-stimulus_origD = flipdim(stimulus,1);
-clear stimulus
-%Yet that might be personal preferences.Note: For generating the STA it does not
-%matter whether it is flipped or not. However if the STA is convolved with the natural movies it matters.
-%Further, if you have frozen and running frames, regenerate the stimulus separatly
-%for running and frozen frames (see Documentation_Data.pdf for the corresponding seeds).
-
-%-------------------------------------------------------------------------------
-%% 3. make average per stimulus frame
-membranPotAVG=avgPerFrame(data.membPot,data.ttls_ms); % weights needed for computing the STA
- 
-%-------------------------------------------------------------------------------
-%% 4. Computing the STA
-%decide for numbers of frames you want to go back in time
-STA_fnbr=(ceil(STA_TimeBack/(1000/Hz))); % define the number of frame you want to go back in time
-%here I prefer to work with a 2D signal of the stimulus
-stimulus_2D = reshape(stimulus_origD, nbrCheckers_total, []);
-tic
-[~,STA_2D]=buildSTA ...
-    (stimulus_2D,membranPotAVG,STA_fnbr);
-toc
-%if you like the STA as a 3D e.g.:
-STA_3D=reshape(STA_2D,Ny,Nx, ...
-    size(STA_2D,2));% Y (monitor axis), X (monitor axis) and Z (STA_fnbr)
-
-%-------------------------------------------------------------------------------
-%% 5. Make a zoom-in of the STA
-%cut a window of around 100 micrometer on each side from the maximum. The
-%zoom-in of the STA is better to avoid overfitting for the NL and
-%prediction.
-oneCheckInMicron=pixelSize*microPerPix;
-zoom_RF=ceil(100/oneCheckInMicron);
-
-%get the maximum of the STA to make a zoom-in
-[max_Color,indx_Zoom]=max(abs(STA_3D(:)));
-[posY,posX,posZ]=ind2sub(size(STA_3D),indx_Zoom);
-%cut a window around the STA ->make a zoom-in STA
-%zoom STA -> here you take the x-y coordinates
-rangeY_zoom = posY+(-zoom_RF:zoom_RF); rangeY_zoom = rangeY_zoom(rangeY_zoom>0 & rangeY_zoom<=size(STA_3D,1));
-rangeX_zoom = posX+(-zoom_RF:zoom_RF); rangeX_zoom = rangeX_zoom(rangeX_zoom>0 & rangeX_zoom<=size(STA_3D,2));
-STA_3D_zoom=STA_3D(rangeY_zoom,rangeX_zoom,:); %take the data from the 2000ms STA
-%reshape the STA
-STA2D_zoom=reshape(STA_3D_zoom,size(rangeY_zoom,2)*size(rangeX_zoom,2),size(STA_3D_zoom,3));
-%get the zoom in of the stimulus signal
-stimulus3D_zoom=stimulus_origD(rangeY_zoom,rangeX_zoom,:); %take the data from the 2000ms STA
-stimulus2D_zoom=reshape(stimulus3D_zoom,size(rangeY_zoom,2)*size(rangeX_zoom,2),size(stimulus3D_zoom,3));
-
-%-------------------------------------------------------------------------------
-%% 5. visualize STA: plot the frame with maximal intensity value
-%I also like to plot all/multiple STA frames around the maximum.
-colorLimits = [-max_Color,max_Color]; %mainly relevant if you plot multiple frames and want to keep the same colorbar
-%plot
-figure;
-imagesc(STA_3D(:,:,posZ))
-caxis(colorLimits);
-colormap('gray'); %how the sitmulus was shown
-axis equal; axis tight;
-title(['STA best frame, filename: ' filename]) 
-
-%-------------------------------------------------------------------------------
-%% 6. add variables into the output
-STA_output.STA_2D=STA_2D;
-STA_output.STA2D_zoom=STA2D_zoom;
-% STA_output.STA=STA; %not  normalized
-STA_output.stimulus2D_zoom=stimulus2D_zoom;
-STA_output.Nx=Nx; %for reshapeing to 3D structure if needed
-STA_output.Ny=Ny;
-STA_output.membranPotAVG=membranPotAVG;
-STA_output.STA_fnbr=STA_fnbr;
-STA_output.filename=filename;
-STA_output.rangeX_zoom=rangeX_zoom;
-STA_output.rangeY_zoom=rangeY_zoom;
-STA_output.pixelSize=pixelSize;
-
-%-------------------------------------------------------------------------------
-%% 7. additional comments:
-%From the zoom STA, I seperate the response into the highest-ranked spatial and temporal
-%components by singular-value decomposition (see Mansucript Schreyer & Gollisch, 2021)
-%example code for the SVD:
-%[sp,sigma,temp] = svd(STA,'econ'); %use the non-normalized STA here!
-%sp=spatial component and temp= the temporal component. 
-%the highest-ranked spatial and temporal component has to be defined e.g. by manually looking
-%at the components.
-%From here, I fitted a 2D Gaussian to the best spatial component.
-%Important for nonlinearity calculation:
-%To avoid noise contributions from pixels outside the receptive field, I reduced 
-%the number of elements in the STA by setting pixel values of the STA outside 
-%the 3-sigma contour of the Gaussian fit to zero and re-separating the spatiotemporal 
-%receptive field within this window into the highest-ranked spatial and temporal components 
-%by singular-value decomposition. Each component was then normalized to unit Euclidean norm. E.g. 
-%sp_bestnorm=normSTA(sp_best);
-%temp_bestnorm=normSTA(temp_best);
-
-end