Parameter estimation of the FitzHugh-Nagumo model using noisy measurements for membrane potential.
Chaos
; 22(2): 023139, 2012 Jun.
Article
em En
| MEDLINE
| ID: mdl-22757546
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Potenciais da Membrana
/
Modelos Neurológicos
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2012
Tipo de documento:
Article