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dc.contributor.authorTang, X.
dc.contributor.authorZhang, Qichun
dc.contributor.authorDai, X.
dc.contributor.authorZou, Y.
dc.date.accessioned2020-11-17T00:24:00Z
dc.date.accessioned2020-11-26T15:29:40Z
dc.date.available2020-11-17T00:24:00Z
dc.date.available2020-11-26T15:29:40Z
dc.date.issued2020-11
dc.identifier.citationTang X, Zhang X, Dai X et al (2020) Neural membrane mutual coupling characterisation using entropy-based iterative learning identification. IEEE Access. 8: 205231-205243.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18180
dc.descriptionYesen_US
dc.description.abstractThis 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.en_US
dc.description.sponsorshipThis 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.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1109/ACCESS.2020.3037816en_US
dc.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/en_US
dc.subjectNeural coupling analysisen_US
dc.subjectExtended Hodgkin-Huxley modelen_US
dc.subjectEquivalent electric circuiten_US
dc.subjectInformation entropyen_US
dc.subjectIterative learningen_US
dc.subjectConvergence analysisen_US
dc.subjectStatistical descriptionen_US
dc.subjectKernel density estimationen_US
dc.subjectResearch Development Fund Publication Prize Award
dc.titleNeural membrane mutual coupling characterisation using entropy-based iterative learning identificationen_US
dc.status.refereedYesen_US
dc.date.Accepted2020-11-09
dc.date.application2020-11-16
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.description.publicnotesResearch Development Fund Publication Prize Award winner, Nov 2020.
dc.date.updated2020-11-17T00:24:08Z
refterms.dateFOA2020-11-26T15:32:42Z


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