Optimal Hebbian learning: a probabilistic point of view

Authors:
Jean-Pascal Pfister;David Barber;Wulfram Gerstner
Affiliations:
Laboratory of Computational Neuroscience, EPFL, Lausanne, Switzerland;Institute for Adaptive and Neural Computation, Edinburgh University, Edinburgh, U.K.;Laboratory of Computational Neuroscience, EPFL, Lausanne, Switzerland
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

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Abstract

Many activity dependent learning rules have been proposed in order to model long-term potentiation (LTP). Our aim is to derive a spike time dependent learning rule from a probabilistic optimality criterion. Our approach allows us to obtain quantitative results in terms of a learning window. This is done by maximising a given likelihood function with respect to the synaptic weights. The resulting weight adaptation is compared with experimental results.