Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2009-11-24

AUTHORS

Matthieu Gilson, Anthony N. Burkitt, David B. Grayden, Doreen A. Thomas, J. Leo van Hemmen

ABSTRACT

In neuronal networks, the changes of synaptic strength (or weight) performed by spike-timing-dependent plasticity (STDP) are hypothesized to give rise to functional network structure. This article investigates how this phenomenon occurs for the excitatory recurrent connections of a network with fixed input weights that is stimulated by external spike trains. We develop a theoretical framework based on the Poisson neuron model to analyze the interplay between the neuronal activity (firing rates and the spike-time correlations) and the learning dynamics, when the network is stimulated by correlated pools of homogeneous Poisson spike trains. STDP can lead to both a stabilization of all the neuron firing rates (homeostatic equilibrium) and a robust weight specialization. The pattern of specialization for the recurrent weights is determined by a relationship between the input firing-rate and correlation structures, the network topology, the STDP parameters and the synaptic response properties. We find conditions for feed-forward pathways or areas with strengthened self-feedback to emerge in an initially homogeneous recurrent network. More... »

PAGES

427

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00422-009-0346-1

DOI

http://dx.doi.org/10.1007/s00422-009-0346-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038215342

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/19937070


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