Coding explains development of binocular vision and its failure in Amblyopia (Eckmann et al 2020)

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This is the MATLAB code for the Active Efficient Coding model introduced in Eckmann et al 2020. It simulates an agent that self-calibrates vergence and accommodation eye movements in a simple visual environment. All algorithms are explained in detail in the main manuscript and the supplementary material of the paper.
1 . Eckmann S, Klimmasch L, Shi BE, Triesch J (2020) Active efficient coding explains the development of binocular vision and its failure in amblyopia. Proc Natl Acad Sci U S A [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Predictive Coding Network; Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract rate-based neuron;
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Action Selection/Decision Making; Reinforcement Learning; Unsupervised Learning; Amblyopia;
Implementer(s): Eckmann, Samuel [ec.sam at]; Klimmasch, Lukas [klimmasch at];
%%% This function generates i.i.d. laplacian distributed random numbers
%%% with mean mu and standard deviation sigma.
%%% The dimension of the output vector is [m, n].
function y  = laprnd(m, n, mu, sigma)

if nargin == 2
    mu = 0; sigma = 1;

if nargin == 3
    sigma = 1;

u = rand(m, n)-0.5;
b = sigma / sqrt(2);
y = mu - b * sign(u).* log(1- 2* abs(u));