Clustering in globally coupled inhibitory neurons

David Golomb, John Rinzel

Research output: Contribution to journalArticle

Abstract

A model of a large population of identical excitable neurons with a global slowly decaying inhibitory coupling is studied and its patterns of synchrony are examined. In addition to converging to a homogeneous fixed point and a homogeneous limit cycle, the system exhibits cluster states, in which it breaks spontaneously into a few macroscopically big clusters, each of which is fully synchronized. A method for calculating the stability of cluster states is described and used for investigating the dynamical behavior of the network versus the parameters that describe the neurons and synapses. Effects of stochastic noise on the network dynamics are discussed. At large enough noise the system goes to a globally stationary state. Low levels of noise preserve the cluster-like neuron trajectories. In the regime where a noiseless system converges to a fully synchronized periodic state, relatively low noise levels cause neurons to burst only every two or more consecutive time periods.

Original languageEnglish (US)
Pages (from-to)259-282
Number of pages24
JournalPhysica D: Nonlinear Phenomena
Volume72
Issue number3
DOIs
StatePublished - Apr 15 1994

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neurons
Neurons
Neuron
Clustering
Cluster State
synapses
Network Dynamics
Synchrony
Synapse
Stationary States
Burst
Dynamical Behavior
Limit Cycle
low noise
Consecutive
bursts
Fixed point
Trajectories
trajectories
Trajectory

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistical and Nonlinear Physics

Cite this

Clustering in globally coupled inhibitory neurons. / Golomb, David; Rinzel, John.

In: Physica D: Nonlinear Phenomena, Vol. 72, No. 3, 15.04.1994, p. 259-282.

Research output: Contribution to journalArticle

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