Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model

Anders Chr. Jensen, Susanne Ditlevsen, Mathieu Kessler, and Omiros Papaspiliopoulos
Phys. Rev. E 86, 041114 – Published 10 October 2012

Abstract

Excitability is observed in a variety of natural systems, such as neuronal dynamics, cardiovascular tissues, or climate dynamics. The stochastic FitzHugh-Nagumo model is a prominent example representing an excitable system. To validate the practical use of a model, the first step is to estimate model parameters from experimental data. This is not an easy task because of the inherent nonlinearity necessary to produce the excitable dynamics, and because the two coordinates of the model are moving on different time scales. Here we propose a Bayesian framework for parameter estimation, which can handle multidimensional nonlinear diffusions with large time scale separation. The estimation method is illustrated on simulated data.

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  • Received 23 May 2012

DOI:https://doi.org/10.1103/PhysRevE.86.041114

©2012 American Physical Society

Authors & Affiliations

Anders Chr. Jensen* and Susanne Ditlevsen

  • Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark

Mathieu Kessler

  • Departamento de Matemática Aplicada y Estadística, Universidad Politécnica de Cartagena, Cartagena, Spain

Omiros Papaspiliopoulos

  • ICREA and Department of Economics, Universitat Pompeu Fabra, Barcelona, Spain

  • *anders@math.ku.dk

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Vol. 86, Iss. 4 — October 2012

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