Spike-timing-dependent plasticity for neurons with recurrent connections View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2007-04-06

AUTHORS

A. N. Burkitt, M. Gilson, J. L. van Hemmen

ABSTRACT

The dynamics of the learning equation, which describes the evolution of the synaptic weights, is derived in the situation where the network contains recurrent connections. The derivation is carried out for the Poisson neuron model. The spiking-rates of the recurrently connected neurons and their cross-correlations are determined self- consistently as a function of the external synaptic inputs. The solution of the learning equation is illustrated by the analysis of the particular case in which there is no external synaptic input. The general learning equation and the fixed-point structure of its solutions is discussed. More... »

PAGES

533-546

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00422-007-0148-2

DOI

http://dx.doi.org/10.1007/s00422-007-0148-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1010529867

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/17415586


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