Short-term synaptic facilitation improves information retrieval in noisy neural networks
Department of Physics and Centre for Neural Dynamics, University of Ottawa - K1N 6N5 Ottawa, Canada
2 Department of Electromagnetism and Physics of the Matter, University of Granada - 18071 Granada, Spain, EU
Accepted: 19 January 2012
Short-term synaptic depression and facilitation have been found to greatly influence the performance of autoassociative neural networks. However, only partial results, focused, for instance, on the computation of the maximum storage capacity at zero temperature, have been obtained to date. In this work, we extended the study of the effect of these synaptic mechanisms on autoassociative neural networks to more realistic and general conditions, including the presence of noise in the system. In particular, we characterized the behavior of the system by means of its phase diagrams, and we concluded that synaptic facilitation significantly enlarges the region of good retrieval performance of the network. We also found that networks with facilitating synapses may have critical temperatures substantially higher than those of standard autoassociative networks, thus allowing neural networks to perform better under high-noise conditions.
PACS: 87.19.lv – Learning and memory / 87.19.lj – Neuronal network dynamics / 64.60.De – Statistical mechanics of model systems (Ising model, Potts model, field-theory models, Monte Carlo techniques, etc.)
© EPLA, 2012