Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/26987
Title: A self-organizing state-space-model approach for parameter estimation in Hodgkin-Huxley-type models of single neurons
Authors: Vavoulis, D. V.
Feng, J.
Straub, Volko A.
Aston, J. A. D.
First Published: Mar-2012
Citation: PLoS Computational Biology, 2012, 8 (3)
Abstract: Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm), often in combination with a local search method (such as gradient descent) in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a potentially useful tool in the construction of biophysical neuron models.
DOI Link: 10.1371/journal.pcbi.1002401
ISSN: 1553-734X
eISSN: 1553-7358
Links: http://hdl.handle.net/2381/26987
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002401
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Appears in Collections:Published Articles, Dept. of Cell Physiology and Pharmacology

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