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Spike-based reinforcement learning of navigation

  • Eleni Vasilaki1Email author,
  • Robert Urbanczik2,
  • Walter Senn2 and
  • Wulfram Gerstner1
BMC Neuroscience20089(Suppl 1):P72

Published: 11 July 2008


We have studied a spiking, reinforcement learning model derived from reward maximization [1, 2] where causal relations between pre-and postsynaptic activity set a synaptic eligibility trace [2, 3]. Neurons are modeled according to the "Integrate-and-Fire" model with escape noise. Synapses are binary and are modulated via the release probability. The synaptic release probability is updated when a global reward signal (such as dopamine) is received.

We have used the learning algorithm in a model of the Morris Water Maze task. The simulated rat explores the environment in random search. After only few trials the rat has learned to approach the goal from arbitrary start conditions, see Figure 1. The model features automatic generalization in state and action space due to coding by overlapping profiles of place cell and action cells [4].
Figure 1

Escape latency versus number of trials. Escape latency measures the time it takes the simulated rat to reach a hidden platform starting from arbitrary initial conditions. Learning is achieved in less than 20 trials. Error bars indicate 25% and 75% percentiles.

Authors’ Affiliations

Laboratory of Computational Neuroscience, School of Computer and Communications Sciences and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL)
Institute of Physiology, University of Bern


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© Vasilaki et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd.