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@@ -1,73 +0,0 @@
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-function [NL_output]=BC_NL(STA_output)
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-%[NL_output]=BC_NL(STA_output)
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-%this function computes the output nonlinearity (NL) from the linear-nonlinear model (LN-model)
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-%for continous voltage signals of bipolar cells (BC).
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-%Inputs:
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-% STA_output = output of the BC_STA function; containing the
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-% STA and stimulus signal
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-%
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-%Outpus: NL_output = structure containing the output nonlinearity
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-%
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-%
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-% Note: the function is built only for cells recorded without frozen frames.
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-% But additional comments are made for cells with frozen frames.
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-% code by HS, last modified Feb 2021
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-
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-%-------------------------------------------------------------------------------
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-%% 1. convolve zoom STA with stimulus (to get the generator signal)
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-%get the linear filter (normalized, see Methods of Schreyer&Gollisch,2021)
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-linearFiler=normSTA(STA_output.STA2D_zoom);
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-%get stimulus signal
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-stimulus=STA_output.stimulus2D_zoom;
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-%%if you have frozen frames, only the stimulus signal of the running frames
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-%%is used for the convolution. Also the stimulus signal has to be convolved
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-%%for each trial of the running noise separately (because it is not a
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-%%continous signal -> it was interupted by the frozen noise).
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-
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-%do the convolution with the stimulus signal
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-generator_signal=filter2(linearFiler,stimulus,'valid');
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-%%IMPORTANT: if you have a best spatial and temporal component as described in Point 7
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-%in the function BC_STA.m, this part hast to be done in two lines, e.g.:
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-%NL_PredXaxis2=filter2(sp_bestnorm,stimulus,'valid'); %first
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-%convolve the spatial dimension
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-%generator_signal=filter2(temp_bestnorm,NL_PredXaxis2,'valid'); %then the
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-%temporal dimension
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-%%Also note, to avoid noise contributions from pixels outside the receptive
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-%%field,it is important to cut the STA into a smaller region around the maximum pixel, this
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-%%gives a much cleaner NL-> see BC_STA.m and Methods in Schreyer&Gollisch (2021).
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-
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-%-------------------------------------------------------------------------------
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-%% 2. get the y axis of the NL
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-%get the voltage responses
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-y_axisNL_unsorted=STA_output.membranPotAVG(STA_output.STA_fnbr:end); %sthe first value is at the length of the filter
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-
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-%-------------------------------------------------------------------------------
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-%% 3. sort the axis and do the binning
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-%sort the x axis
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-[NL_xAxis,indx]=sort(generator_signal);
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-%sort the corresponding y axis
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-NL_yAxis=y_axisNL_unsorted(indx);
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-%do a binning with percentile to have same amount of data in each bin
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-%make 40 bins
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-nbrbins=40;
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-bins= doBin(NL_xAxis,NL_yAxis,nbrbins);
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-
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-%-------------------------------------------------------------------------------
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-%% 4. plot the nonlinearity
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-figure;
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-plot(NL_xAxis,NL_yAxis,'.','color','k') %plot non-binned signal
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-hold on;
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-plot(bins.NL_xbin,bins.NL_ybin,'o','markerfacecolor','r','markeredgecolor','r') %plot binned signal
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-title(['NL: filename: ' STA_output.filename])
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-axis tight
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-xlabel('generator signal')
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-ylabel('voltage (mV)')
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-
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-%-------------------------------------------------------------------------------
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-%% 5. add variables into the output
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-NL_output.NL_xAxis=NL_xAxis;
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-% STA_output.STA=STA; %not normalized
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-NL_output.NL_yAxis=NL_yAxis;
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-NL_output.bins=bins; %for reshapeing to 3D structure if needed
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-
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-end
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