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Estimation of biophysical parameters in a neuron model under random fluctuations

Author

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  • Upadhyay, Ranjit Kumar
  • Paul, Chinmoy
  • Mondal, Argha
  • Vishwakarma, Gajendra K.

Abstract

In this paper, an attempt has been made to estimate the biophysical parameters in an improved version of Morris–Lecar (M–L) neuron model in a noisy environment. To observe the influence of noisy stimulation in estimation procedure, a Gaussian white noise has been added to the membrane voltage of the model system. Estimation of the parameters has been investigated by a proposed algorithm. The denoising technique (local projection method) has been applied to reduce the influence of noisy stimuli and the effectiveness of the method is reported. The proposed scheme performs well for an excitable neuron model and provides good estimates between the estimated parameters and the actual values in a reasonable way. This approach can be used for parameter estimation for other nonlinear dynamical systems.

Suggested Citation

  • Upadhyay, Ranjit Kumar & Paul, Chinmoy & Mondal, Argha & Vishwakarma, Gajendra K., 2018. "Estimation of biophysical parameters in a neuron model under random fluctuations," Applied Mathematics and Computation, Elsevier, vol. 329(C), pages 364-373.
    • Handle: RePEc:eee:apmaco:v:329:y:2018:i:c:p:364-373
    DOI: 10.1016/j.amc.2018.02.011
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    References listed on IDEAS

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    1. Upadhyay, Ranjit Kumar & Mondal, Argha, 2017. "Synchronization of bursting neurons with a slowly varying d. c. current," Chaos, Solitons & Fractals, Elsevier, vol. 99(C), pages 195-208.
    2. Perc, Matjaž, 2007. "Effects of small-world connectivity on noise-induced temporal and spatial order in neural media," Chaos, Solitons & Fractals, Elsevier, vol. 31(2), pages 280-291.
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    Cited by:

    1. Ping Ma & Lei Wang, 2022. "Partially Coupled Stochastic Gradient Estimation for Multivariate Equation-Error Systems," Mathematics, MDPI, vol. 10(16), pages 1-15, August.
    2. Guo, Yongfeng & Wang, Linjie & Dong, Qiang & Lou, Xiaojuan, 2021. "Dynamical complexity of FitzHugh–Nagumo neuron model driven by Lévy noise and Gaussian white noise," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 430-443.

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