Spike-Timing Dependent Plasticity in Recurrently Connected Networks with Fixed External Inputs View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2008

AUTHORS

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

ABSTRACT

This paper investigates spike-timing dependent plasticity (STDP) for recurrently connected weights in a network with fixed external inputs (homogeneous Poisson pulse trains). We use a dynamical system to model the network activity and predict its asymptotic evolution, which turns out to qualitatively depend on the learning parameters and the correlation structure of the inputs. Our predictions are supported by numerical simulations of Poisson neuron networks in general cases as well as for certain cases when using Integrate-And-Fire (IF) neurons. More... »

PAGES

102-111

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-69158-7_12

DOI

http://dx.doi.org/10.1007/978-3-540-69158-7_12

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027093452


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