Statistical Mechanics for Neural Spike Data Analysis Using Log-Linear Model
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概要
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Recently, we can simultaneously record spike data from many neurons in the field of electrophysiology, and thus it is required to develop mathematical framework for extracting higher-order correlation of neural firings. The joint probability of neural spike can be represented using the log-linear model. From statistical-mechanical point of view, the loglinear model can be regarded as a multi-body interacted Ising spin model or the Boltzman machine with higher-order interactions. The estimation of higher-order correlation of neural firing corresponds to that of higher-order interations in this Ising spin system, and to the hyper-parameter estimation in the Bayesian inference. In this paper, we apply maximization of marginal likelihood (MML) method to this problem, and discuss the properties of MML analytically using statistical-mechanical method.
- 2005-04-30
著者
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SHOUNO Hayaru
Faculty of Engineering, Yamaguchi University
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WADA Koji
Kochi National College of Technology
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Okada Masato
Riken Brain Sci. Inst. Saitama
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Shouno Hayaru
Faculty Of Engineering Yamaguchi University
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Okada Masato
"Intelligent Coorperation and Control", PRESTO, JST
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OKADA Masato
RIKEN BSI:Japan Scientific Technology Corp.:Graduate School of Frontier Science, The University of Tokyo
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OKADA Masato
Graduate School of Frontier Sciences, The University of Tokyo:RIKEN Brain Science Institute:Intelligent Cooperation and Control
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