NDLI: Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks V: self-organization schemes and weight dependence

Content Provider	Springer Nature Link
Author	Gilson, Matthieu Burkitt, Anthony N. Grayden, David B. Thomas, Doreen A. Hemmen, J. Leo
Copyright Year	2010
Abstract	Spike-timing-dependent plasticity (STDP) determines the evolution of the synaptic weights according to their pre- and post-synaptic activity, which in turn changes the neuronal activity on a (much) slower time scale. This paper examines the effect of STDP in a recurrently connected network stimulated by external pools of input spike trains, where both input and recurrent synapses are plastic. Our previously developed theoretical framework is extended to incorporate weight-dependent STDP and dendritic delays. The weight dynamics is determined by an interplay between the neuronal activation mechanisms, the input spike-time correlations, and the learning parameters. For the case of two external input pools, the resulting learning scheme can exhibit a symmetry breaking of the input connections such that two neuronal groups emerge, each specialized to one input pool only. In addition, we show how the recurrent connections within each neuronal group can be strengthened by STDP at the expense of those between the two groups. This neuronal self-organization can be seen as a basic dynamical ingredient for the emergence of neuronal maps induced by activity-dependent plasticity.
Starting Page	365
Ending Page	386
Page Count	22
File Format	PDF
ISSN	03401200
Journal	Biological Cybernetics
Volume Number	103
Issue Number	5
e-ISSN	14320770
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-09-29
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Learning Weight-dependent STDP Recurrent neuronal network Spike-time correlation Self-organization Statistical Physics, Dynamical Systems and Complexity Neurobiology Computer Application in Life Sciences Neurosciences Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Computer Science Biotechnology

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