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A self-organizing state-space-model approach for parameter estimation in Hodgkin-Huxley-type models of single neurons
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Vavoulis, Dimitrios V., Straub, Volko A., Aston, John A. D. and Feng, Jianfeng (2012) A self-organizing state-space-model approach for parameter estimation in Hodgkin-Huxley-type models of single neurons. PLoS Computational Biology, Vol.8 (No.3). e1002401. doi:10.1371/journal.pcbi.1002401 ISSN 1553-7358.
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Official URL: http://dx.doi.org/10.1371/journal.pcbi.1002401
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.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics Q Science > QP Physiology |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Science > Statistics |
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Library of Congress Subject Headings (LCSH): | Parameter estimation, Neurons -- Mathematical models | ||||
Journal or Publication Title: | PLoS Computational Biology | ||||
Publisher: | PLOS | ||||
ISSN: | 1553-7358 | ||||
Official Date: | 1 March 2012 | ||||
Dates: |
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Volume: | Vol.8 | ||||
Number: | No.3 | ||||
Page Range: | e1002401 | ||||
DOI: | 10.1371/journal.pcbi.1002401 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 20 December 2015 | ||||
Date of first compliant Open Access: | 20 December 2015 | ||||
Funder: | European Commission (EC) | ||||
Grant number: | 213219 (EC) |
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