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(Neural Computation. 2007;19:2739-2755.)
© 2007 The MIT Press

Letter

Competition Between Synaptic Depression and Facilitation in Attractor Neural Networks

J. J. Torres

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

J. M. Cortes

jesus.m.cortes{at}gmail.com Institute Carlos I for Theoretical and Computational Physics, and Department of Electromagnetism and Matter Physics, University of Granada, Granada E-18071, Spain, and Department of Physics, Radboud University of Nijmegen, 6525 EZ Nijmegen, Netherlands

J. Marro

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

H. J. Kappen

B.Kappen{at}science.ru.nl Department of Medical Physics and Biophysics, Radboud University of Nijmegen, 6525 EZ Nijmegen, Netherlands

We study the effect of competition between short-term synaptic depression and facilitation on the dynamic properties of attractor neural networks, using Monte Carlo simulation and a mean-field analysis. Depending on the balance of depression, facilitation, and the underlying noise, the network displays different behaviors, including associative memory and switching of activity between different attractors. We conclude that synaptic facilitation enhances the attractor instability in a way that (1) intensifies the system adaptability to external stimuli, which is in agreement with experiments, and (2) favors the retrieval of information with less error during short time intervals.






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Copyright © 2007 by The MIT Press.