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Effects of Fast Presynaptic Noise in Attractor Neural Networks

jcortes{at}ugr.es

J. J. Torres

jtorres{at}onsager.ugr.es

J. Marro

jmarro{at}ugr.es

P. L. Garrido

garrido{at}onsager.ugr.es Institute Carlos I for Theoretical and Computational Physics and Department of Electromagnetism and Physics of Matter, University of Granada, 18071 Granada, Spain

H. J. Kappen

B.kappen{at}science.ru.nl Department of Biophysics, Radboud University of Nijmegen, 6525 EZ Nijmegen, The Netherlands

We study both analytically and numerically the effect of presynaptic noise on the transmission of information in attractor neural networks. The noise occurs on a very short timescale 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 timescale depending on presynaptic activity. We thus describe a mechanism by which fast presynaptic noise enhances the neural network sensitivity to an external stimulus. The reason is that, in general, presynaptic noise induces nonequilibrium behavior and, consequently, the space of fixed points is qualitatively modified in such a way that the system can easily escape from the attractor. As a result, the model shows, in addition to pattern recognition, class identification and categorization, which may be relevant to the understanding of some of the brain complex tasks.

This article has been cited by other articles:

				J. J. Torres, J. M. Cortes, J. Marro, and H. J. Kappen Competition Between Synaptic Depression and Facilitation in Attractor Neural Networks Neural Comput., October 1, 2007; 19(10): 2739 - 2755. [Abstract] [Full Text] [PDF]