Yes / 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. / 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. / Research Development Fund Publication Prize Award winner, Nov 2020.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/18180 |
| Date | 17 November 2020 |
| Creators | Tang, X., Zhang, Qichun, Dai, X., Zou, Y. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Published version |
| 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/ |
Page generated in 0.0018 seconds