Stability of asynchronous firing states in networks with synaptic adaptation

Sergio Solinas, John Hertz

Research output: Contribution to journalArticle

2 Citations (Scopus)

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

Fingerprint

Neurons
Population
Mean field theory
Synapses
Fires

Keywords

  • Asynchronous firing
  • Cortical dynamics
  • Synaptic adaptation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Cellular and Molecular Neuroscience

Cite this

Stability of asynchronous firing states in networks with synaptic adaptation. / Solinas, Sergio; Hertz, John.

In: Neurocomputing, Vol. 38-40, 06.2001, p. 915-920.

Research output: Contribution to journalArticle

Solinas, Sergio ; Hertz, John. / Stability of asynchronous firing states in networks with synaptic adaptation. In: Neurocomputing. 2001 ; Vol. 38-40. pp. 915-920.
@article{6ac7e2c841454b59ab0d5705125f5383,
title = "Stability of asynchronous firing states in networks with synaptic adaptation",
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.",
keywords = "Asynchronous firing, Cortical dynamics, Synaptic adaptation",
author = "Sergio Solinas and John Hertz",
year = "2001",
month = "6",
doi = "10.1016/S0925-2312(01)00425-8",
language = "English",
volume = "38-40",
pages = "915--920",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier Science B.V.",

}

TY - JOUR

T1 - Stability of asynchronous firing states in networks with synaptic adaptation

AU - Solinas, Sergio

AU - Hertz, John

PY - 2001/6

Y1 - 2001/6

N2 - 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.

AB - 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.

KW - Asynchronous firing

KW - Cortical dynamics

KW - Synaptic adaptation

UR - http://www.scopus.com/inward/record.url?scp=0035383939&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035383939&partnerID=8YFLogxK

U2 - 10.1016/S0925-2312(01)00425-8

DO - 10.1016/S0925-2312(01)00425-8

M3 - Article

VL - 38-40

SP - 915

EP - 920

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

ER -