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Spike-Based Learning Rules and Stabilization of Persistent Neural Activity

By Xiao-hui Xie and H. Sebastian Seung

Abstract

We analyze the conditions under which synaptic learning rules based on action potential timing can be approximated by learning rules based on ring rates. In particular, we consider a form of plasticity in which synapses depress when a presynaptic spike is followed by a postsynaptic spike, and potentiate with the opposite temporal ordering. Such differential anti-Hebbian plasticity can be approximated under certain conditions by a learning rule that depends on the time derivative of the postsynaptic ring rate. Such a learning rule acts to stabilize persistent neural activity patterns in recurrent neural networks

Publisher: MIT Press
Year: 2000
OAI identifier: oai:CiteSeerX.psu:10.1.1.19.8350
Provided by: CiteSeerX
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