title: Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model.
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.
authors.affiliation: Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark. anders@math.ku.dk
authors.forename: Anders Chr
authors.initials: AC
authors.lastname: Jensen
authors.forename: Susanne
authors.initials: S
authors.lastname: Ditlevsen
authors.forename: Mathieu
authors.initials: M
authors.lastname: Kessler
authors.forename: Omiros
authors.initials: O
authors.lastname: Papaspiliopoulos
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001703:Biophysics / Q000379:methods*; D003198:Computer Simulation; D004058:Diffusion; D008390:Markov Chains; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D009991:Oscillometry / Q000379:methods; D013269:Stochastic Processes