Presentation 2014-01-20
Hodgkin Huxley Type Model Parameter Estimation for Single Neuron via Massively Parallel RCGA : Application to the Silkmoth Antennal Lobe Neuron
Akihiko GOTO, Tomoki KAZAWA, Daisuke MIYAMOTO, Stephan Shuichi HAUPT,
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Abstract(in English) It is necessary to estimate model parameters of a neuron to simulate electrical activity of the neuron on a computer. We previously estimated model parameters of virtual neurons via massively parallel Real-Coded Genetic Algorithm (RCGA) implemented on K computer. In this paper, this algorithm was applied to a real projection neuron of a silkmoth antennal lobe and model parameters were estimated. As a result of the experiments, we confirmed the advantage of multi-compartment model over single compartment model.
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Keyword(in English) Real-coded genetic algorithm / Insect brain simulation / Antennal lobe of silkmoth / Massively parallel computing / Multi-compartment model
Paper # NC2013-68
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Committee NC
Conference Date 2014/1/13(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Hodgkin Huxley Type Model Parameter Estimation for Single Neuron via Massively Parallel RCGA : Application to the Silkmoth Antennal Lobe Neuron
Sub Title (in English)
Keyword(1) Real-coded genetic algorithm
Keyword(2) Insect brain simulation
Keyword(3) Antennal lobe of silkmoth
Keyword(4) Massively parallel computing
Keyword(5) Multi-compartment model
1st Author's Name Akihiko GOTO
1st Author's Affiliation Graduate School of Information Science and Technology, The University of Tokyo()
2nd Author's Name Tomoki KAZAWA
2nd Author's Affiliation Research Center for Advanced Science and Technology, The University of Tokyo
3rd Author's Name Daisuke MIYAMOTO
3rd Author's Affiliation Graduate School of Information Science and Technology, The University of Tokyo
4th Author's Name Stephan Shuichi HAUPT
4th Author's Affiliation Department of Biological Cybernetics, Faculty of Biology Bielefeld University
Date 2014-01-20
Paper # NC2013-68
Volume (vol) vol.113
Number (no) 382
Page pp.pp.-
#Pages 6
Date of Issue