2008年6月
Rate reduction for associative memory model in Hodgkin-Huxley-type network
JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN
- ,
- ,
- 巻
- 77
- 号
- 6
- 開始ページ
- 064802:1
- 終了ページ
- 6
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1143/JPSJ.77.064802
- 出版者・発行元
- PHYSICAL SOC JAPAN
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.
- リンク情報
- ID情報
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- DOI : 10.1143/JPSJ.77.064802
- ISSN : 0031-9015
- Web of Science ID : WOS:000256893800042