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    Neural membrane mutual coupling characterisation using entropy-based iterative learning identification

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    Publication date
    2020-11
    Author
    Tang, X.
    Zhang, Qichun
    Dai, X.
    Zou, Y.
    Keyword
    Neural coupling analysis
    Extended Hodgkin-Huxley model
    Equivalent electric circuit
    Information entropy
    Iterative learning
    Convergence analysis
    Statistical description
    Kernel density estimation
    Research Development Fund Publication Prize Award
    Rights
    © 2020 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
    Peer-Reviewed
    Yes
    
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    Abstract
    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.
    URI
    http://hdl.handle.net/10454/18180
    Version
    Published version
    Citation
    Tang X, Zhang X, Dai X et al (2020) Neural membrane mutual coupling characterisation using entropy-based iterative learning identification. IEEE Access. 8: 205231-205243.
    Link to publisher’s version
    https://doi.org/10.1109/ACCESS.2020.3037816
    Type
    Article
    Notes
    Research Development Fund Publication Prize Award winner, Nov 2020.
    Collections
    Engineering and Digital Technology Publications

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