Learning flexible sensori-motor mappings in a complex network

Vasilaki, E; Fusi, S; Wang, X.J.; Senn, Walter (2009). Learning flexible sensori-motor mappings in a complex network. Biological cybernetics, 100(2), pp. 147-58. Berlin: Springer-Verlag 10.1007/s00422-008-0288-z

Preview

Text 422_2008_Article_288.pdf - Published Version Available under License Publisher holds Copyright. Download (582kB) | Preview

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.

Interest & Impact

Downloads

246 since deposited on 04 Oct 2013
33 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item