Title :
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
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;
Conference_Titel :
SICE 2002. Proceedings of the 41st SICE Annual Conference
Print_ISBN :
0-7803-7631-5
DOI :
10.1109/SICE.2002.1196569
