- Poster presentation
- Open Access
Pattern recognition of Hodgkin-Huxley equations by auto-regressive Laguerre Volterra network
© Geng and Marmarelis 2015
- Published: 18 December 2015
- Simulated Annealing
- Refractory Period
- Algorithm Simulated Annealing
- Convergence Problem
- Cross Term
We adopt stochastic global optimization algorithm Simulated Annealing  to train the ASLVN instead of Back-propagation method  to avoid local minima and convergence problems. We also use lasso regularization  to enhance the spasity of the network and prune redundant branches for parsimony. The prediction results are shown in Fig.1B, it can be seen that the exogenous output z(1) represents the subthreshold dynamics in phase III, and the autoregressive output z(2) dominates in the spike shape in phase I, and the cross term output z(x) helps to maintain the refractory period by cancelling the effect of z(1) in phase II and we also observe that refractory inhibition effect decays after initiation of AP, which explains the absolute refractory period and relative refractory period in physiology.
- Eikenberry SE, Marmarelis VZ: A nonlinear autoregressive Volterra model of the Hodgkin-Huxley equations. Journal of computational neuroscience. 2013, 34 (1): 163-183.PubMedView ArticleGoogle Scholar
- Marmarelis VZ: Nonlinear dynamic modeling of physiological systems. John Wiley & Sons. 2004, 10:Google Scholar
- Kirkpatrick S: Optimization by simmulated annealing. science. 1983, 220 (4598): 671-680.PubMedView ArticleGoogle Scholar
- Tibshirani R: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B (Methodological). 1996, 267-288.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.