Monthly
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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

October 2007, Vol. 19, No. 10, Pages 2739-2755
(doi: 10.1162/neco.2007.19.10.2739)
© 2007 Massachusetts Institute of Technology
Competition Between Synaptic Depression and Facilitation in Attractor Neural Networks
Article PDF (498.46 KB)
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.