A New Adaptive Method for Solving Neuron Cells Problem of Hodgkin-Huxley and Estimating Parameters
DOI:
https://doi.org/10.29304/jqcsm.2024.16.41798Keywords:
Hodgkin–Huxley model, Adaptation algorithm design, Speed gradient method, Lyepunov functions, Parameter estimationAbstract
In this paper, a new adaptive parametrization model of fourth order neuron cells model of Hodgkin-Huxley (HH) is introduced. The adaptation algorithm design or as can be called adaptive identification algorithm of parameters of the neuron cells HH model is produced. This algorithm is theoretically and computationally proved. This introduced algorithm is basically based on Lyepunov functions and adaptive observer methods which may prove the stability of the model to obtain unique solutions. The results of the computer simulation of the identification problem are shown in the figures and the parameters are clearly coincide the real values. Some terms of the problem are defined for the measured data. It provides an observation model to determine the most informative data for a specific parameter, and find the best fit model. In the HH model of neural cells, the consistency of the differential equations of the adaptation model with the observers of cell’s activation is shown by fitting the observed data to the real data within the high accuracy of estimating the parameters.
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