Competition Between Synaptic Depression and Facilitation in Attractor Neural Networks

Authors:
J. J. Torres;J. M. Cortes;J. Marro;H. J. Kappen
Affiliations:
Institute Carlos I for Theoretical and Computational Physics, and Department of Electromagnetism and Matter Physics, University of Granada, Granada E-18071, Spain jtorres@onsager.ugr.es;Inst. Carlos I for Theor. and Computnl. Physics, and Dept. of Electromag. and Matter Physics, Univ. of Granada, Granada E-18071, Spain, and Dept. of Physics, Radboud Univ. of Nijmegen, 6525 EZ Nij ...;Institute Carlos I for Theoretical and Computational Physics, and Department of Electromagnetism and Matter Physics, University of Granada, Granada E-18071, Spain jmarro@ugr.es;Department of Medical Physics and Biophysics, Radboud University of Nijmegen, 6525 EZ Nijmegen, Netherlands B.Kappen@science.ru.nl
Venue:
Neural Computation
Year:
2007

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Abstract

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.