Short-term synaptic facilitation improves information retrieval in noisy neural networks

Abstract

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.

6 pages, 3 figures, to appear in EPL

References in corpus (6)

• Irregular dynamics in up and down cortical states
• Can intrinsic noise induce various resonant peaks?
• Spontaneous Formation of Dynamical Groups in an Adaptive Networked System
• Unstable Dynamics, Nonequilibrium Phases and Criticality in Networked Excitable Media
• Formation of Modularity in a Model of Evolving Networks
• Influence of synaptic depression on memory storage capacity

Cited by in corpus (1)

• Double Inverse Stochastic Resonance with Dynamic Synapses