Schreyer_and_Gollisch_2021_salamander_retinal_bipolar_cell_recordings/spatiotemporalWhiteNoise/code spatiotemporal white noise/buildSTA.m

function [STA_mean_normalized,STA_mean]=buildSTA ...
    (stim_signal,membraneP,STA_fnbr)
% [STA_mean_normalized,STA_mean]=buildSTA ...
%    (stim_signal,membraneP,STA_fnbr)
%is getting the contrast value that happen before each response
%and multiplies them with the average membrane Potential per frame.
% INPUT:        stim_signal -> stimulus signal containing the contrast values in 2D
%               membraneP -> membrane potential average per frame
%               STA_fnbr -> nbr of frames you go back in time
% OUTPUT:       STA_mean_normalized -> normalized STA, relevant for
%               computing the NL
%               STA_mean -> not normalized STA

%------------------------------------------------------------------------------------------
%% 1. Loop over all membrane potential values
STA_fnbr=STA_fnbr-1; %the first value is one frame, so to take exactly 10 frames, substract one here.
for kk=1:length(membraneP)-(STA_fnbr)
    if kk==1
        signal1=membraneP(kk+(STA_fnbr))*stim_signal(:,kk:kk+(STA_fnbr)); %multiply the average values with the stimulus signal before; it start now from 1 and goes until the element kk
    else
        signal2=membraneP(kk+(STA_fnbr))*stim_signal(:,kk:kk+(STA_fnbr)); %multiply the average values with the stimulus signal before
        signal1=signal1+signal2; %make the sum to get the average at the end
        clear signal2;
    end
end
%build the average
STA_mean=signal1/length(1:length(membraneP)-(STA_fnbr)); %not normalized

%normalize
STA_mean_normalized = STA_mean(:) / sqrt(sum(STA_mean(:).*STA_mean(:))); %relevant for computing the NL
STA_mean_normalized=reshape(STA_mean_normalized,size(STA_mean));


end