Competition Between Synaptic Depression and Facilitation in Attractor Neural Networks

2007/08/23 by Joaquı́n J. Torres, J. J. Torres, Jesús M. Cortés +5 · 2 citations
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #Neural Networks and Applications #Neural dynamics and brain function

paper · doi:10.1162/neco.2007.19.10.2739

openalex publication_date 2007/08/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

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.

Citations

• Sensitivity, Itinerancy and Chaos in Partly-Synchronized Weighted Networks
• Short-Term Synaptic Plasticity
• Neural Networks with Dynamic Synapses

Cited by

• Dynamic Synaptic Modulation of LMG Qubits populations in a Bio-Inspired Quantum Brain
• Maximum Memory Capacity on Neural Networks with Short-Term Synaptic Depression and Facilitation

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