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

Anders Christian Jensen; Susanne Ditlevsen; Mathieu Kessler; Omiros Papaspiliopoulos

doi:10.1103/physreve.86.041114

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

Anders Christian Jensen, Susanne Ditlevsen, Mathieu Kessler , Omiros Papaspiliopoulos

Institut for Matematiske Fag

11 Citationer (Scopus)

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.

Originalsprog	Engelsk
Tidsskrift	Physical Review E. Statistical, Nonlinear, and Soft Matter Physics
Vol/bind	86
Udgave nummer	4
Sider (fra-til)	041114
Antal sider	9
ISSN	1063-651X
DOI	https://doi.org/10.1103/physreve.86.041114
Status	Udgivet - 10 okt. 2012

Adgang til dokumentet

10.1103/physreve.86.041114

Citationsformater

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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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