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Efficient current-based optimization techniques for parameter estimation in multi-compartment neuronal models

Lepora, N.F., Overton, P.G. and Gurney, K. (2009) Efficient current-based optimization techniques for parameter estimation in multi-compartment neuronal models. In: Eighteenth Annual Computational Neuroscience Meeting: CNS 2009, 18 - 23 July 2009, Berlin, Germany.

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Abstract

Estimation of the maximal ion channel conductances in Hodgkin-Huxley models from patch clamp data is a difficult optimization problem. Automating this process promises high-throughput computational modeling of use to both experimenters (for rapid feedback on their experimental preparations) and modelers (for investigating the details of neuronal function). Hitherto, attempts to do this have focused on stochastic searches such as genetic algorithms and simulated annealing [1-3]. Such methods give robust estimates of model parameters but converge slowly or need to sample a large population of test cases in parallel, and therefore require substantial computing resources. Meanwhile, deterministic searches (e.g. the simplex search and conjugate gradient descent) are far more computationally efficient but are hampered by the complex fitting landscape of the optimization problem. As such, there is no general neuronal parameter-fitting algorithm that is both computationally efficient and robust.

Item Type: Conference or Workshop Item (Poster)
Copyright, Publisher and Additional Information: © 2009 Lepora et al; licensee BioMed Central Ltd.
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield)
Depositing User: Sheffield Import
Date Deposited: 08 Oct 2009 15:28
Last Modified: 08 Oct 2009 15:28
Published Version: http://www.biomedcentral.com/1471-2202/10/S1/P347
Status: Published
Publisher: BioMed Central
URI: http://eprints.whiterose.ac.uk/id/eprint/9693

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