Clustering in small networks of excitatory neurons with heterogeneous coupling strengths

Document Type

Article

Publication Date

1-1-2003

Abstract

Excitatory coupling with a slow rise time destabilizes synchrony between coupled neurons. Thus, the fully synchronous state is usually unstable in networks of excitatory neurons. Phase-clustered states, in which neurons are divided into multiple synchronized clusters, have also been found unstable in numerical studies of excitatory networks in the presence of noise. The question arises as to whether synchrony is possible in networks of neurons coupled through slow, excitatory synapses. In this paper, we show that robust, synchronous clustered states can occur in such networks. The effects of non-uniform distributions of coupling strengths are explored. Conditions for the existence and stability of clustered states are derived analytically. The analysis shows that a multi-cluster state can be stable in excitatory networks if the overall interactions between neurons in different clusters are stabilizing and strong enough to counter-act the destabilizing interactions between neurons within each cluster. When heterogeneity in the coupling strengths strengthens the stabilizing inter-cluster interactions and/or weakens the destabilizing in-cluster interactions, robust clustered states can occur in excitatory networks of all known model neurons. Numerical simulations were carried out to support the analytical results.

Identifier

0037333816 (Scopus)

Publication Title

Journal of Computational Neuroscience

External Full Text Location

https://doi.org/10.1023/A:1021902717424

ISSN

09295313

PubMed ID

12567014

First Page

139

Last Page

159

Issue

2

Volume

14

Fund Ref

Natural Sciences and Engineering Research Council of Canada

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