A population density method for large-scale modeling of neuronal networks with realistic synaptic kinetics

E. Haskell, D. Q. Nykamp, D. Tranchina

Research output: Contribution to journalArticlepeer-review

Abstract

Population density function (PDF) methods have been used as both a time-saving alternative to direct Monte-Carlo simulation of neuronal network activity and as a tool for the analytic study of neuronal networks. Computational efficiency of the PDF method is dependent on a low-dimensional state space for the underlying individual neuron. Many previous implementations have assumed that the time scale of the synaptic kinetics is very fast on the scale of the membrane time constant in order to obtain a one-dimensional state space. Here, we extend our previous PDF methods for synapses with realistic kinetics; synaptic current injection for inhibition is replaced with more realistic conductance modulation.

Original languageEnglish (US)
Pages (from-to)627-632
Number of pages6
JournalNeurocomputing
Volume38-40
DOIs
StatePublished - Jun 2001

Keywords

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

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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