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Coding explains development of binocular vision and its failure in Amblyopia (Eckmann et al 2020)
 
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Accession:
261483
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.
Reference:
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
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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;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment:
MATLAB;
Model Concept(s):
Action Selection/Decision Making;
Reinforcement Learning;
Unsupervised Learning;
Amblyopia;
Implementer(s):
Eckmann, Samuel [ec.sam at outlook.com];
Klimmasch, Lukas [klimmasch at fias.uni-frankfurt.de];
Download the displayed file
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code
results
textures
README.html
BaseGenerator.m
calculateCorrelation.m
calculateRightBinocularity.m
config-aniso.m
config-hc.m
config-mono.m
Gabor.m
laprnd.m
Model.m
PatchGenerator.m
ReinforcementLearning.m
Sim.m
SparseCoding.m
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