Is the Langevin phase equation an efficient model for oscillating neurons?

Keisuke Ota, Takamasa Tsunoda, Toshiaki Omori, Shigeo Watanabe, Hiroyoshi Miyakawa, Masato Okada, Toru Aonishi

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

The Langevin phase model is an important canonical model for capturing coherent oscillations of neural populations. However, little attention has been given to verifying its applicability. In this paper, we demonstrate that the Langevin phase equation is an efficient model for neural oscillators by using the machine learning method in two steps: (a) Learning of the Langevin phase model. We estimated the parameters of the Langevin phase equation, i.e., a phase response curve and the intensity of white noise from physiological data measured in the hippocampal CA1 pyramidal neurons. (b) Test of the estimated model. We verified whether a Fokker-Planck equation derived from the Langevin phase equation with the estimated parameters could capture the stochastic oscillatory behavior of the same neurons disturbed by periodic perturbations. The estimated model could predict the neural behavior, so we can say that the Langevin phase equation is an efficient model for oscillating neurons.

Original languageEnglish
Article number012016
JournalJournal of Physics: Conference Series
Volume197
DOIs
Publication statusPublished - 2009

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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