Rate Reduction for Associative Memory Model n Hodgkin-Huxley-Type Network(Cross-disciplinary physics and related areas of science and technology)

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概要

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• We proposed a systematic method for reducing Hodgkin-Huxley-type networks to networks of rate equations on the basis of Shriki et al.'s formulation. Our rate model predicts the results of numerical simulations of the Hodgkin-Huxley-type network model very precisely over a broad range of synaptic conductances. However, in the proposed framework, we ad hoc assumed that the firing threshold and the gain of the f-I curve of the Hodgkin-Huxley-type conductance-based model have a second-order dependence on leak conductance. Here, we discuss optimal model selection with respect to the dependence of the threshold and the gain on the f-I curve, using the Akaike information criterion. We then apply our rate reduction method to an associative memory model of Hodgkin-Huxley neurons. Most associative memory models have been studied using two-state neurons or graded-response neurons. We check the correspondence between an associative memory model of Hodgkin-Huxley neurons and that of graded-response neurons, particularly in terms of phase diagrams. We store correlated patterns in the network and investigate the phase transition between the memory state and the mixed state. We demonstrate that our rate model, which is obtained by the reduction method, explains the phase diagram of the Hodgkin-Huxley-type network very well.

• 社団法人日本物理学会の論文
• 2008-06-15

著者

• OKADA MASATO

  Graduate School of Frontier Sciences, The University of Tokyo

• OKADA Masato

  University of Tokyo

• Miyawaki Yoichi

  Nict Computational Neuroscience Laboratories:atr Computational Neuroscience Laboratories

• Okada Masato

  Division Of Transdisciplinary Of Sciences Graduate School Of Frontier Sciences The University Of Tok

• Okada Masato

  Graduate School Of Engineering Science Osaka University

• OIZUMI Masafumi

  Graduate School of Frontier Sciences, The University of Tokyo

• MIYAWAKI Yoichi

  NICT, Computational Neuroscience Laboratories

• Oizumi Masafumi

  Graduate School Of Frontier Sciences The University Of Tokyo

• 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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