Binocular energy model set for binocular neurons in optic lobe of praying mantis (Rosner et al 2019)

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This is a version of the binocular energy model with parameters chosen to reproduce individual cells in praying mantis optic lobe. The receptive fields are very coarsely sampled (6 different horizontal locations only) to match the coarse sampling of the data given very limited recording time.
1 . Rosner R, von Hadeln J, Tarawneh G, Read JCA (2019) A neuronal correlate of insect stereopsis. Nat Commun 10:2845 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Optic lobe/Praying Mantis;
Cell Type(s):
Gap Junctions:
Simulation Environment: MATLAB; MATLAB (web link to model);
Model Concept(s): Binocular energy model/Stereopsis; Simplified Models;
Implementer(s): Read, Jenny [ at];
% This function loads the cell specified in "cellname", plots its data and
% fits the model to it.

clear all
close all
% !!! Change following 2 lines to load different cells/conditions!!
cellname = 'rr171019'; % identifies cell.
condition = 'brightbar_on'; % specifies condition

format short

% First look up which eye had the green filter:
load([cellname filesep 'GreenFilter.mat']);

% Now load the cell data itself
load([cellname filesep cellname '_' condition '.mat']);


% Let's place them all into a 7x7xnreps matrix
% Background response goes in (7,7,...):
ronnymatrix(7,7,:) = background;
if green_filter_left_eye==1
    ronnymatrix(7,1:6,:) = Buffer_blue; % left eye has green filter, sees the bars which appear blue when dark
    ronnymatrix(1:6,7,:)  = Buffer_green;
    ronnymatrix(1:6,1:6,:) = permute(spikeCount_binoc,[2 1 3]);
    ronnymatrix(7,1:6,:) = Buffer_green; % right eye has green filter, sees the bars which appear blue when dark
    ronnymatrix(1:6,7,:)  = Buffer_blue;
    ronnymatrix(1:6,1:6,:) = spikeCount_binoc;

% Now we normalise by the highest mean rate within trials seen in any condition:
for k = 1 : numel(ronnymatrix(1,1,:))
    max_of_all = max(max(ronnymatrix(:,:,k)));
    ronnymatrix(:,:,k) = ronnymatrix(:,:,k)./max_of_all;
% Now let's average over repetitions
mn = mean(ronnymatrix,3);
% and calculate the standard deviation too:
sd = std(ronnymatrix,[],3);

% Now we rewrite this information into a structure with clearer names:
neuronresponse.background  =     mn(7,7);
neuronresponse.reps.background  =    squeeze(ronnymatrix(7,7,:));
neuronresponse.SEM.background = sd(7,7)/sqrt(nreps);
neuronresponse.monocL =      mn(7,1:6);
neuronresponse.reps.monocL =  squeeze(ronnymatrix(7,1:6,:));
neuronresponse.SEM.monocL =  sd(7,1:6)/sqrt(nreps);
neuronresponse.monocR =     mn(1:6,7)';
neuronresponse.reps.monocR =  squeeze(ronnymatrix(1:6,7,:));
neuronresponse.SEM.monocR = sd(1:6,7)'/sqrt(nreps);
neuronresponse.binoc =     mn(1:6,1:6)';
neuronresponse.reps.binoc =     mn(1:6,1:6,:);
neuronresponse.SEM.binoc = sd(1:6,1:6)'/sqrt(nreps);
% indiv reps
neuronresponse.monocL_Alltrials = squeeze(ronnymatrix(7,1:6,:))';
neuronresponse.monocR_Alltrials = squeeze(ronnymatrix(1:6,7,:))';

% Now we plot the neuronal data:

% Use ANOVA to test whether bar position in left and/or right eye has a significant main
% effect, and whether the interaction is significant:

% Now it's time to fit this data with our model.

% FitModel does the fitting. You can call it with just neuronresponse as a sole argument, but the optimisation is non-convex so the initial parameters are rather critical. I therefore did it this way - first fitted the L and R RFs without fitting the output exponent (so 12 free parameters), and set the initial guesses for the RFs to 1 at all bar locations:
model12 = FitModel(neuronresponse,ones(1,12))

% Then I used the RFs thus found as the initial guess when fitting a 13-parameter model including the output exponent:
model13 = FitModel(neuronresponse,[model12.RFL model12.RFR 1])

% If you want to fit all 14 parameters at once to all the data, you can do this. It will give very similar results:
% model14 = FitModel(neuronresponse,[model13.RFL model13.RFR model13.outputexponent model13.tonicinput])

% Now plot the final  model:


fprintf('% variance explained by 13-parameter model = %f %%\n ',model13.percentVarianceExplained)

clear model
model.RFL = model13.RFL;
model.RFR = model13.RFR;
model.outputexponent = model13.outputexponent;
model.threshold = model13.threshold;
model.tonicinput = model13.tonicinput;

modelfilename = sprintf('model_of_cell_%s_%s.mat',cellname,condition)

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