Learning flexible sensori-motor mappings in a complex network

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)147-158
Number of pages12
JournalBiological cybernetics
Issue number2
StatePublished - Feb 2009


  • Hebbian
  • Learning
  • Multilayer
  • Reward-modulated
  • Visuomotor task

ASJC Scopus subject areas

  • Biotechnology
  • General Computer Science


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