title: A self-organizing state-space-model approach for parameter estimation in hodgkin-huxley-type models of single neurons.
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.
authors.affiliation: Department of Computer Science, University of Warwick, Coventry, United Kingdom. Dimitris.Vavoulis@dcs.warwick.ac.uk
authors.forename: Dimitrios V
authors.initials: DV
authors.lastname: Vavoulis
authors.forename: Volko A
authors.initials: VA
authors.lastname: Straub
authors.forename: John A D
authors.initials: JA
authors.lastname: Aston
authors.forename: Jianfeng
authors.initials: J
authors.lastname: Feng
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology*