PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Competition between synaptic depression and facilitation in attractor neural networks
J.J. Torres, J.M. Cortes, J. Marro and Bert Kappen
Neural Computation Volume 19, pp. 2739-2755, 2007.

Abstract

We study both analytically and numerically the ef- fect of presynaptic noise on the transmission of in- formation in attractor neural networks. The noise occurs on a very short–time scale compared to that for the neuron dynamics and it produces short– time synaptic depression. This is inspired in recent neurobiological findings that show that synaptic strength may either increase or decrease on a short– time scale depending on presynaptic activity. We thus describe a mechanism by which fast presynap- tic noise enhances the neural network sensitivity to an external stimulus. The reason for this is that, in general, the presynaptic noise induces nonequi- librium behavior and, consequently, the space of fixed points is qualitatively modified in such a way that the system can easily scape from the attrac- tor. As a result, the model shows, in addition to pattern recognition, class identification and catego- rization, which may be relevant to the understand- ing of some of the brain complex tasks.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4880
Deposited By:Bert Kappen
Deposited On:24 March 2009