2009年
Is the Langevin phase equation an efficient model for oscillating neurons?
INTERNATIONAL WORKSHOP ON STATISTICAL-MECHANICAL INFORMATICS 2009 (IW-SMI 2009)
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- 巻
- 197
- 号
- 012016
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1088/1742-6596/197/1/012016
- 出版者・発行元
- IOP PUBLISHING LTD
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
- リンク情報
- ID情報
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- DOI : 10.1088/1742-6596/197/1/012016
- ISSN : 1742-6588
- Web of Science ID : WOS:000280342700016