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 Neuroscijournal_title
Journal of computational neuroscienceauthors
Friedrich J,Kinzel Wdoi
10.1007/s10827-008-0127-1subject
Has Abstractpub_date
2009-08-01 00:00:00pages
65-80issue
1eissn
0929-5313issn
1573-6873journal_volume
27pub_type
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