Neural Membrane Mutual Coupling Characterisation Using Entropy-Based Iterative Learning Identification

Tang, Xiafei, Zhang, Qichun, Dai, Xuewu and Zou, Yiqun (2020) Neural Membrane Mutual Coupling Characterisation Using Entropy-Based Iterative Learning Identification. IEEE Access, 8. pp. 205231-205243. ISSN 2169-3536

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This paper investigates the interaction phenomena of the coupled axons while the mutual coupling factor is presented as a pairwise description. Based on the Hodgkin-Huxley model and the coupling factor matrix, the membrane potentials of the coupled myelinated/unmyelinated axons are quantified which implies that the neural coupling can be characterised by the presented coupling factor. Meanwhile the equivalent electric circuit is supplied to illustrate the physical meaning of this extended model. In order to estimate the coupling factor, a data-based iterative learning identification algorithm is presented where the Rényi entropy of the estimation error has been minimised. The convergence of the presented algorithm is analysed and the learning rate is designed. To verified the presented model and the algorithm, the numerical simulation results indicate the correctness and the effectiveness. Furthermore, the statistical description of the neural coupling, the approximation using ordinary differential equation, the measurement and the conduction of the nerve signals are discussed respectively as advanced topics. The novelties can be summarised as follows: 1) the Hodgkin-Huxley model has been extended considering the mutual interaction between the neural axon membranes, 2) the iterative learning approach has been developed for factor identification using entropy criterion, and 3) the theoretical framework has been established for this class of system identification problems with convergence analysis.

Item Type: Article
Additional Information: Funding Information: This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 51807010, and in part by the Natural Science Foundation of Hunan under Grant 1541 and Grant 1734.
Uncontrolled Keywords: Convergence analysis, Equivalent electric circuit, Extended Hodgkin-Huxley model, Information entropy, Iterative learning, Kernel density estimation, Neural coupling analysis, Statistical description
Subjects: F200 Materials Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Rachel Branson
Date Deposited: 02 Nov 2022 12:20
Last Modified: 02 Nov 2022 12:30

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