Stochastic neuron model with dynamic synapses and evolution equation of its density function

Wentao Huang, Licheng Jiao, Yuelei Xu, Maoguo Gong

Research output: Contribution to journalConference articlepeer-review

Abstract

In most neural network models, neurons are viewed as the only computational units, while the synapses are treated as passive scalar parameters (weights). It has, however, long been recognized that biological synapses can exhibit rich temporal dynamics. These dynamics may have important consequences for computing and learning in biological neural systems. This paper proposes a novel stochastic model of single neuron with synaptic dynamics, which is characterized by several stochastic differential equations. From this model, we obtain the evolution equation of their density function. Furthermore, we give an approach to cut the evolution equation of the high dimensional function down to the evolution equation of one dimension function.

Original languageEnglish (US)
Pages (from-to)449-455
Number of pages7
JournalLecture Notes in Computer Science
Volume3610
Issue numberPART I
StatePublished - Oct 24 2005
EventFirst International Conference on Natural Computation, ICNC 2005 - Changsha, China
Duration: Aug 27 2005Aug 29 2005

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Stochastic neuron model with dynamic synapses and evolution equation of its density function'. Together they form a unique fingerprint.

Cite this