title: Parameter estimation of the FitzHugh-Nagumo model using noisy measurements for membrane potential.
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.
authors: Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education, Tianjin 300222, People's Republic of China. yqche@tju.edu.cn
authors: Yanqiu
authors: Y
authors: Che
authors: Li-Hui
authors: LH
authors: Geng
authors: Chunxiao
authors: C
authors: Han
authors: Shigang
authors: S
authors: Cui
authors: Jiang
authors: J
authors: Wang
mesh_terms: D016477:Artifacts; D003198:Computer Simulation; D008564:Membrane Potentials / Q000502:physiology*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D009716:Numerical Analysis, Computer-Assisted