Neural membrane mutual coupling characterisation using entropy-based iterative learning identification
Tang, X., Zhang, Qichun, Dai, X. and Zou, Y. (2020) Neural membrane mutual coupling characterisation using entropy-based iterative learning identification.
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| Item Type: | Article |
|---|---|
| Additional Information: | This paper investigates the interaction phenomena of the coupled axons while the mutualcoupling factor is presented as a pairwise description. Based on the Hodgkin-Huxley model and the couplingfactor matrix, the membrane potentials of the coupled myelinated/unmyelinated axons are quantified whichimplies that the neural coupling can be characterised by the presented coupling factor. Meanwhile theequivalent electric circuit is supplied to illustrate the physical meaning of this extended model. In orderto estimate the coupling factor, a data-based iterative learning identification algorithm is presented wherethe Rényi entropy of the estimation error has been minimised. The convergence of the presented algorithm isanalysed and the learning rate is designed. To verified the presented model and the algorithm, the numericalsimulation results indicate the correctness and the effectiveness. Furthermore, the statistical description of theneural coupling, the approximation using ordinary differential equation, the measurement and the conductionof the nerve signals are discussed respectively as advanced topics. The novelties can be summarised asfollows: 1) the Hodgkin-Huxley model has been extended considering the mutual interaction between theneural axon membranes, 2) the iterative learning approach has been developed for factor identification usingentropy criterion, and 3) the theoretical framework has been established for this class of system identificationproblems with convergence analysis. |
| Depositing User: | RED Unit Admin |
| Date Deposited: | 18 Dec 2024 14:14 |
| Last Modified: | 18 Dec 2024 14:14 |
| URI: | https://bnu.repository.guildhe.ac.uk/id/eprint/19536 |
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