Fast neural network simulations with population density methods

Duane Q. Nykamp, Daniel Tranchina

Research output: Contribution to journalConference article

Abstract

The complexity of neural networks of the brain makes studying these networks through computer simulation challenging. Conventional methods, where one models thousands of individual neurons, can take enormous amounts of computer time even for models of small cortical areas. An alternative is the population density method in which neurons are grouped into large populations and one tracks the distribution of neurons over state space for each population. We discuss the method in general and illustrate the technique for integrate-and-fire neurons. (C) 2000 Elsevier Science B.V. All rights reserved.

Original languageEnglish (US)
Pages (from-to)487-492
Number of pages6
JournalNeurocomputing
Volume32-33
DOIs
StatePublished - Jun 2000
EventThe 8th Annual Computational Neuroscience Meeting (CNS'99) - Pittsburgh, PA, USA
Duration: Jul 18 1999Jul 22 1999

Keywords

  • Computer simulation
  • Network modeling
  • Populations
  • Probability density function

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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