Comparing correlation responses to motion estimation models (Salazar-Gatzimas et al. 2016)

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Accession:206310
Code to generate responses of HRC-like and BL-like model elementary motion detectors to correlated noise stimuli, including two models with more realistic temporal filtering.
Reference:
1 . Salazar-Gatzimas E, Chen J, Creamer MS, Mano O, Mandel HB, Matulis CA, Pottackal J, Clark DA (2016) Direct Measurement of Correlation Responses in Drosophila Elementary Motion Detectors Reveals Fast Timescale Tuning. Neuron 92:227-239 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism: Drosophila;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Motion Detection; Invertebrate;
Implementer(s): Creamer, Matthew [Matthew.Creamer at yale.edu]; Mano, Omer [Omer.Mano at yale.edu]; Clark, Damon [Damon.Clark at yale.edu];
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neuron2016Models
utils
getModelResponse.m
makeTernaryStim.m
                            
function [sboth] = makeTernaryStim(T,X,pixelRate,pixelWidth,dtinstance,parity,spatialFilterType)
% This function simulates the input to two photoreceptors that are
% observing a correlated noise stimulus.
%
% Inputs:
% T: The amount of time in seconds to simulate
% X: The amount of space in degrees to simulate
% pixelRate: The number of frames per second
% pixelWidth: The size of the pixels in degrees
% dtinstance: The time interval in frames on which correlations will exist
% dtinstance=-1 simulates no correlations
% parity: 1 for positive correlations, -1 for negative correlations
% spatialFilterType: 0 for delta function spatial filters, 1 for 5 degree
% fwhm gaussian filters
%
% Output: the simulated inputs in a matrix with dimensions
% time (in milliseconds) by cell (left and right)

    % A correlated noise stimulus is generated by averaging a random binary
    % stimulus with a version of itself which has been shifted in time and
    % space
    c1 = (rand(ceil(T*pixelRate/1000)+2,ceil(X/pixelWidth)+2)>0.5)*2 - 1;

    % this simulates no correlations in the stimulus
    if dtinstance == -1
        c2 = (rand(ceil(T*pixelRate/1000)+2,ceil(X/pixelWidth)+2)>0.5)*2 - 1;
        xttemp = c1/2 + c2/2;
    else
        xttemp = c1/2 + parity*circshift(c1,[dtinstance,1])/2;
    end

    if X/pixelWidth == 2 && spatialFilterType == 0
        sboth = xttemp(:,2:3);
    else
        % interpolate in space to simulate pixel width
        xt = interp1([0:size(xttemp,2)-1]*pixelWidth,xttemp',[0:X-1]','previous')';
        % Get filter weights and apply them
        filts = make_spatial_filters(spatialFilterType,X);
        sboth = xt*filts;
    end

    % interpolate in time to simulate the duration of a frame
    sboth = interp1([0:size(sboth,1)-1]/pixelRate*1000,sboth,[0:T-1]','previous');
end

function F = make_spatial_filters(spatialFilterType,X)

    dX = 1;
    centers = [12.5 17.5]; %Location of the centers of the receptive fields in degrees

    fwhm = 5;
    sigma = fwhm/sqrt(8*log(2)); % convert to sigma
    xvals = [0.5:dX:X]'; % vector of pixel center locations in degrees

    switch spatialFilterType
        case 0 % delta functions
            F = zeros(length(xvals),2);
            F(round(centers(1)),1)=1;
            F(round(centers(2)),2)=1;
        case 1 % Gaussian filters
            % note: weights are computed for the center of the pixel, not 
            % integrated over the pixel. This approximation is less correct
            % when fwhm is small.
            f1 = exp(-(xvals-centers(1)).^2/2/sigma^2);
            f2 = exp(-(xvals-centers(2)).^2/2/sigma^2);
            f1 = f1/sum(f1);
            f2 = f2/sum(f2);
            F = [f1(:),f2(:)];
    end
end

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