Maximum-entropy closures for kinetic theories of neuronal network dynamics

Aaditya Rangan, David Cai

Research output: Contribution to journalArticle

Abstract

We analyze (1+1)D kinetic equations for neuronal network dynamics, which are derived via an intuitive closure from a Boltzmann-like equation governing the evolution of a one-particle (i.e., one-neuron) probability density function. We demonstrate that this intuitive closure is a generalization of moment closures based on the maximum-entropy principle. By invoking maximum-entropy closures, we show how to systematically extend this kinetic theory to obtain higher-order, (1+1)D kinetic equations and to include coupled networks of both excitatory and inhibitory neurons.

Original languageEnglish (US)
Article number178101
JournalPhysical Review Letters
Volume96
Issue number17
DOIs
StatePublished - May 2 2006

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Entropy
kinetic theory
closures
entropy
neurons
kinetic equations
Neurons
probability density functions
moments

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Medicine(all)

Cite this

Maximum-entropy closures for kinetic theories of neuronal network dynamics. / Rangan, Aaditya; Cai, David.

In: Physical Review Letters, Vol. 96, No. 17, 178101, 02.05.2006.

Research output: Contribution to journalArticle

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