Parameter Estimation of Various Hodgkin-Huxley-Type Neuronal Models Using a Gradient-Descent Learning Method

Shinji Doi; Yuichi Onoda; Sadatoshi Kumagai

doi:10.11499/sicep.2002.0.372.0

SICE Annual Conference 2002

DOI https://doi.org/10.11499/sicep.2002.0.372.0

Conference information

Host: The Society of Instrument and Control Engineers

Co-host: IEEE/Industiral Electronic Society, IEEE/Robotics and Automation Society, IEEE/Control System Society

Parameter Estimation of Various Hodgkin-Huxley-Type Neuronal Models Using a Gradient-Descent Learning Method

Shinji Doi, Yuichi Onoda, Sadatoshi Kumagai

Author information

Keywords: neuron model, parameter estimation, learning, identification

CONFERENCE PROCEEDINGS FREE ACCESS

Pages 372

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Abstract

The automatic parameter identification method proposed by Doya et al. of the Hodgkin-Huxley-type equations is investigated in detail. An improved estimation method is proposed and it is shown that our method resolves the difficulties in estimating parameters of HH-type equations with complicated membrane potential waveforms such as a chaotic bursting and also much improves the parameter estimation (learning) speed.

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