Learning flexible sensori-motor mappings in a complex network View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2009-01-20

AUTHORS

Eleni Vasilaki, Stefano Fusi, Xiao-Jing Wang, Walter Senn

ABSTRACT

Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex problem. More... »

PAGES

147

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00422-008-0288-z

DOI

http://dx.doi.org/10.1007/s00422-008-0288-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1033537872

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/19153762


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