Dynamics of recurrent neural networks with delayed unreliable synapses: metastable clustering.

Abstract:

:The influence of unreliable synapses on the dynamic properties of a neural network is investigated for a homogeneous integrate-and-fire network with delayed inhibitory synapses. Numerical and analytical calculations show that the network relaxes to a state with dynamic clusters of identical size which permanently exchange neurons. We present analytical results for the number of clusters and their distribution of firing times which are determined by the synaptic properties. The number of possible configurations increases exponentially with network size. In addition to states with a maximal number of clusters, metastable ones with a smaller number of clusters survive for an exponentially large time scale. An externally excited cluster survives for some time, too, thus clusters may encode information.

journal_name

J Comput Neurosci

authors

Friedrich J,Kinzel W

doi

10.1007/s10827-008-0127-1

subject

Has Abstract

pub_date

2009-08-01 00:00:00

pages

65-80

issue

1

eissn

0929-5313

issn

1573-6873

journal_volume

27

pub_type

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