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- %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
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