DocumentCode :
2778172
Title :
Evaluation of approximate stochastic Hodgkin-Huxley models
Author :
Bruce, Ian C.
Author_Institution :
Dept. of Electr. & Comput. Eng., McMaster Univ., Hamilton, Ont.
fYear :
2007
fDate :
2-5 May 2007
Firstpage :
654
Lastpage :
658
Abstract :
Fox and colleagues (Fox, 1997; Fox ang Lu, 1994) derived an algorithm based on stochastic differential equations for approximating the kinetics of ion channel gating that is substantially simpler and faster than "exact" algorithms for simulating Markov process models of channel gating. However, Mino and colleagues (2002) argued that the approximation may not be sufficiently accurate in describing the statistics of action potential generation. Bruce (2007) subsequently showed that some of the inaccuracies described by Mino et al. (2002 were due to implementation choices, but several important inaccuracies remained. The objective of this study was to develop a framework for analyzing the remaining inaccuracies and determining their origin. Simulations of a patch of membrane with voltage-gated sodium and potassium channels were performed using an exact algorithm for the kinetics of channel gating and the approximate algorithm of Fox. The Fox algorithm assumes that channel gating particle dynamics have a stochastic term that is uncorrelated, zero-mean Gaussian noise, whereas the simulation results of this study demonstrate that in many cases the stochastic term in the Fox algorithm should be correlated and non-Gaussian noise with a non-zero mean. The results indicate that the source of these differences in noise statistics is that the Fox algorithm does not adequately describe the combined behavior of the multiple activation particles in each sodium and potassium channel (three and four, respectively)
Keywords :
bioelectric phenomena; biomembrane transport; stochastic processes; approximate stochastic Hodgkin-Huxley models; channel gating particle dynamics; ion channel gating; stochastic differential equations; stochastic term; Approximation algorithms; Biomembranes; Computational modeling; Differential equations; Gaussian noise; Kinetic theory; Markov processes; Statistics; Stochastic processes; Stochastic resonance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Engineering, 2007. CNE '07. 3rd International IEEE/EMBS Conference on
Conference_Location :
Kohala Coast, HI
Print_ISBN :
1-4244-0792-3
Electronic_ISBN :
1-4244-0792-3
Type :
conf
DOI :
10.1109/CNE.2007.369758
Filename :
4227363
Link To Document :
بازگشت