Learning in neural networks by reinforcement of irregular spiking

Xiaohui Xie, H. Sebastian Seung

Research output: Contribution to journalArticlepeer-review

92 Scopus citations


A synaptic update rule for learning in networks of spiking neurons was presented. It was shown that irregular spiking similar to that observed in biological neurons could be used as the basis for a learning rule. The learning rule was derived based on a special class of model networks in which neurons fire spike trains. The learning was found to be compatible with forms of synaptic dynamics.

Original languageEnglish (US)
Article number041909
Pages (from-to)041909-1-041909-10
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Issue number4 1
StatePublished - Apr 2004

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics


Dive into the research topics of 'Learning in neural networks by reinforcement of irregular spiking'. Together they form a unique fingerprint.

Cite this