Higher Order Effects on Rate Reduction for Networks of Hodgkin-Huxley Neurons(Cross-disciplinary physics and related areas of science and technology)
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概要
- 論文の詳細を見る
We propose a systematic method of rate reduction for a Hodgkin-Huxley type neural network model. In this context, Shriki et al. assumed that the threshold of the f-I curve for the reduced rate model depends linearly on the leak conductance of the Hodgkin-Huxley equation, while its gain remains constant. First, we show that the threshold and gain have second order dependence on the leak conductance. Second, we show that the Hodgkin-Huxley type network with second order interaction can be naturally reduced to an analog type neural network model with higher order interaction based on this finding. Finally, we construct statistical mechanics for the Hodgkin-Huxley type network with the Mexican-hat interaction through our rate reduction technique.
- 社団法人日本物理学会の論文
- 2007-04-15
著者
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OKADA MASATO
Graduate School of Frontier Sciences, The University of Tokyo
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OKADA Masato
University of Tokyo
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Miyawaki Yoichi
Nict Computational Neuroscience Laboratories:atr Computational Neuroscience Laboratories
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Okada Masato
Division Of Transdisciplinary Of Sciences Graduate School Of Frontier Sciences The University Of Tok
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Okada Masato
Graduate School Of Engineering Science Osaka University
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OIZUMI Masafumi
Graduate School of Frontier Sciences, The University of Tokyo
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Oizumi Masafumi
Graduate School Of Frontier Sciences The University Of Tokyo
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OKADA Masato
Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute:"Intelligent Cooperation and Control", PRESTO, JST, co RIKEN BSI:Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, the University of To
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