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RBFNN-based modelling and analysis for the signal reconstruction of peripheral nerve tissue

Zhang, Q and Sepulveda, F (2017) RBFNN-based modelling and analysis for the signal reconstruction of peripheral nerve tissue. In: UNSPECIFIED, ? - ?.

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Abstract

© 2017 ACM. This paper presents a novel modelling approach for complex nonlinear dynamic of the neural signal conduction along the myelinated or unmyelinated axons. Normally, this problem is described by the partial differential equation (PDE) combing cable equation, however the solution of the PDE approach is difficult to obtain and the interaction phenomena in nerve tissue is ignored. Based on radial basis function neural network (RBFNN), the membrane potential conduction can be restated by the dynamic of the weight vector while the shortcomings of the PDE approach can be fixed. Moreover, the neural signal prediction, the stimulation signal design and interaction characterization are further investigated using the presented model.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: ACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 17 Nov 2017 10:58
Last Modified: 30 Jan 2019 16:21
URI: http://repository.essex.ac.uk/id/eprint/20675

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