Parameter estimation of various Hodgkin-Huxley-type neuronal models using a gradient-descent learning method

Author

DOI, Shinji ; Onoda, Yuichi ; Kumagai, Sadatoshi

Author_Institution

Osaka Univ., Japan

Volume

3

fYear

2002

fDate

5-7 Aug. 2002

Firstpage

1685

Abstract

The automatic parameter identification method proposed by Doya et al. (1994) of the Hodgkin-Huxley-type equations (1952) is investigated in detail. The Hodgkin-Huxley-type equations describe membrane currents and conduction and excitation in nerves. An improved estimation method is proposed and it is shown that our method resolves the difficulties in estimating parameters of such equations with complicated membrane potential waveforms such as a chaotic bursting and also much improves the parameter estimation (learning) speed.

Keywords

bioelectric phenomena; biomembranes; gradient methods; neurophysiology; parameter estimation; physiological models; Hodgkin-Huxley-type neuronal models; automatic parameter identification method; chaotic bursting; complicated membrane potential waveforms; gradient-descent learning method; learning speed; membrane current; nerve conduction; nerve excitation; parameter estimation; squid giant axon; Biological system modeling; Biomembranes; Cells (biology); Chaos; Differential equations; Learning systems; Mathematical model; Nerve fibers; Neurons; Parameter estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE 2002. Proceedings of the 41st SICE Annual Conference

Print_ISBN

0-7803-7631-5

Type

conf

DOI

10.1109/SICE.2002.1196569

Filename

1196569