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Journal of Computational Electronics

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13-12-2023

A practical synthesis and analysis of the fractional-order FitzHugh-Nagumo neuronal model

Author: İbrahim Ethem Saçu

Published in: Journal of Computational Electronics | Issue 1/2024

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Abstract

This work focuses on the practical and reasonable synthesis of the fractional-order FitzHugh-Nagumo (FHN) neuronal model. First of all, the descriptive equations of the fractional FHN neuronal system have been given, and then the system stability has been analyzed according to these equations. Secondly, the Laplace-Adomian-decomposition-method is introduced for the numerical solution of the fractional-order FHN neuron model. By means of this method, rapid convergence can be achieved as well as advantages in terms of low hardware cost and uncomplicated computation. In numerical analysis, different situations have been evaluated in detail, depending on the values of fractional-order parameter and external stimulation. Third, the coupling status of fractional-order FHN neuron models is discussed. Finally, experimental validation of the numerical results obtained for the fractional-order single and coupled FHN neurons has been performed by means of the digital signal processor control card F28335 Delfino. Thus, the efficiency of the introduced method for synthesizing the fractional FHN neuronal model in a fast, low cost and simple way has been demonstrated.

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Title: A practical synthesis and analysis of the fractional-order FitzHugh-Nagumo neuronal model
Author: İbrahim Ethem Saçu
Publication date: 13-12-2023
Publisher: Springer US
Published in: Journal of Computational Electronics / Issue 1/2024
Print ISSN: 1569-8025
Electronic ISSN: 1572-8137
DOI: https://doi.org/10.1007/s10825-023-02120-x

Computational study of the structural, optoelectronic and thermoelectric properties of scandium-based ternary chalcogenides XScSe2 (X = Li, Rb) for applications in photovoltaic cell

Correction: Resistance saturation in semi-conducting polyacetylene molecular wires

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A practical synthesis and analysis of the fractional-order FitzHugh-Nagumo neuronal model

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