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-%Guide for performing a linear-nonlinear model (LN-model)
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-%analysis from voltage recordings of bipolar cells (BC) under spatiotemporal white noise
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-%Check out the Documentation_Data.pdf, which contains relevant information
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-%for analysing the white noise stimulus
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-%check also the paper from Chichilnisky, E.J. (2001). 'A simple white noise
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-%analysis of neuronal light responses', Network, 12, 199-213.
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-%it explains the LN-model for spiking neurons nicely! Here we adapted this
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-%method to non-spiking neurons (continous voltage signal).
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-
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-clear;
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-close all;
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-
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-%% First LN-Model Component: Filter (spatiotemporal receptive field =STA)
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-loadData='Z:\Manuscript\BC_XY_LN\Puplishing\DataAvailable\spatiotemporalWhiteNoise\'; %path of the BC files for spatiotemporal white noise (has to be adapted to your own path)
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-filename='16d200015Comp.mat'; %BC file to analyse: for example here we use file 155200029Comp from cell 2, recorded in retina 1 on 150520
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-pixelSize=9; %is given in the excel file LogInfo_SpatioTemporalWhiteNoise.xlsx for the corresponding cell
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-seed=-10000; %see also documention_data.pdf
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-Hz=25; % given in the excel file LogInfo_SpatioTemporalWhiteNoise.xlsx for the corresponding cell
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-[STA_output]=BC_STA(loadData,filename,pixelSize,seed,Hz);%example code receptive field analysis=linear filter
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-
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-%% Second LN-Model Component: Output nonlinearity
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-[NL_output]=BC_NL(STA_output);%example code output nonlinearity analysis
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-
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-%% Prediction of the response with the LN-Model (filter and output nonlinearity):
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-[pred_output]=BC_pred(STA_output);%example code prediction
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