%Guide for performing a linear-nonlinear model (LN-model)
%analysis from voltage recordings of bipolar cells (BC) under spatiotemporal white noise
%Check out the Documentation_Data.pdf, which contains relevant information
%for analysing the white noise stimulus
%check also the paper from Chichilnisky, E.J. (2001). 'A simple white noise 
%analysis of neuronal light responses', Network, 12, 199-213.
%it explains the LN-model for spiking neurons nicely! Here we adapted this
%method to non-spiking neurons (continous voltage signal).

clear;
close all;

%% First LN-Model Component: Filter (spatiotemporal receptive field =STA)
loadData='Z:\spatiotemporalWhiteNoise\'; %path of the BC files for spatiotemporal white noise (has to be adapted to your own path)
filename='155200029Comp.mat'; %BC file to analyse: for example here we use file 155200029Comp from cell 2, recorded in retina 1 on 150520
pixelSize=12; %is given in the excel file LogInfo_SpatioTemporalWhiteNoise.xlsx for the corresponding cell
seed=-10000; %see also documention_data.pdf
Hz=10; % given in the excel file LogInfo_SpatioTemporalWhiteNoise.xlsx for the corresponding cell (update rate)
[STA_output]=BC_STA(loadData,filename,pixelSize,seed,Hz);%example code receptive field analysis=linear filter

%% Second LN-Model Component: Output nonlinearity
[NL_output]=BC_NL(STA_output);%example code output nonlinearity analysis

%% Prediction of the response with the LN-Model (filter and output nonlinearity):
[pred_output]=BC_pred(STA_output);%example code prediction