Parameter estimation of the FitzHugh-Nagumo model using noisy measurements for membrane potential

Yanqiu Che, Li Hui Geng, Chunxiao Han, Shigang Cui, Jiang Wang

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Abstract

This paper proposes an identification method to estimate the parameters of the FitzHugh-Nagumo (FHN) model for a neuron using noisy measurements available from a voltage-clamp experiment. By eliminating an unmeasurable recovery variable from the FHN model, a parametric second order ordinary differential equation for the only measurable membrane potential variable can be obtained. In the presence of the measurement noise, a simple least squares method is employed to estimate the associated parameters involved in the FHN model. Although the available measurements for the membrane potential are contaminated with noises, the proposed identification method aided by wavelet denoising can also give the FHN model parameters with satisfactory accuracy. Finally, two simulation examples demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Article number023139
JournalChaos
Volume22
Issue number2
DOIs
StatePublished - Apr 4 2012

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All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • Physics and Astronomy(all)
  • Applied Mathematics

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