Spike-based learning rules and stabilization of persistent neural activity

Xiaohui Xie, H. Sebastian Seung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations

Abstract

We analyze the conditions under which synaptic learning rules based on action potential timing can be approximated by learning rules based on firing 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-Hebbianplasticity can be approximated under certain conditions by a learning rule that depends on the time derivative of the postsynaptic firing rate. Such a learning rule acts to stabilize persistent neural activity patterns in recurrent neural networks.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999
PublisherNeural information processing systems foundation
Pages199-205
Number of pages7
ISBN (Print)0262194503, 9780262194501
StatePublished - 2000
Externally publishedYes
Event13th Annual Neural Information Processing Systems Conference, NIPS 1999 - Denver, CO, United States
Duration: Nov 29 1999Dec 4 1999

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other13th Annual Neural Information Processing Systems Conference, NIPS 1999
CountryUnited States
CityDenver, CO
Period11/29/9912/4/99

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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  • Cite this

    Xie, X., & Seung, H. S. (2000). Spike-based learning rules and stabilization of persistent neural activity. In Advances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999 (pp. 199-205). (Advances in Neural Information Processing Systems). Neural information processing systems foundation.