Stability of asynchronous firing states in networks with synaptic adaptation

Sergio Solinas, John Hertz

Research output: Contribution to journalArticle

Abstract

We construct a mean field theory for low-rate asynchronous firing states in networks consisting of excitatory and inhibitory populations of integrate-and-fire neurons with synaptic depression or facilitation. The theory is exact when each neuron receives input from K randomly chosen ones, with 1 ≪ K ≪ N, where N is the total number of neurons. Changes in firing rates produce changes in synaptic strengths and vice-versa, potentially leading to instabilities. We prove that depression of synapses within a population (excitatory or inhibitory) always tends to stabilize the asynchronous state against such fluctuations, while depression acting between populations destabilizes it. Facilitation has the opposite effect.

Original languageEnglish
Pages (from-to)915-920
Number of pages6
JournalNeurocomputing
Volume38-40
DOIs
Publication statusPublished - Jun 2001

Keywords

  • Asynchronous firing
  • Cortical dynamics
  • Synaptic adaptation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Cellular and Molecular Neuroscience

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