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Cognitive Neurodynamics
Erschienen in: Cognitive Neurodynamics 2/2023

07.06.2022 | Research Article

Nonlinear dynamical modeling of neural activity using volterra series with GA-enhanced particle swarm optimization algorithm

verfasst von: Siyuan Chang, Jiang Wang, Yulin Zhu, Xile Wei, Bin Deng, Huiyan Li, Chen Liu

Erschienen in: Cognitive Neurodynamics | Ausgabe 2/2023

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Abstract

In order to improve the modeling performance of Volterra sequence for nonlinear neural activity, in this paper, a new optimization algorithm is proposed to identify Volterra sequence parameters. Algorithm combines the advantages of particle swarm optimization (PSO) and genetic algorithm (GA) improve the performance of the identification of nonlinear model parameters from rapidity and accuracy. In the modeling experiments of neural signal data generated by the neural computing model and clinical neural data set in this paper, the proposed algorithm shows its excellent potential in nonlinear neural activity modeling. Compared with PSO and GA, the algorithm can achieve less identification error, and better balance the convergence speed and identification error. Further, we explore the influence of algorithm parameters on identification efficiency, which provides possible guiding significance for parameter setting in practical application of the algorithm.
Vorheriger Artikel Oscillatory biomarkers of autism: evidence from the innate visual fear evoking paradigm
Nächster Artikel A model description of beta oscillations in the external globus pallidus

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Metadaten
Titel
Nonlinear dynamical modeling of neural activity using volterra series with GA-enhanced particle swarm optimization algorithm
verfasst von
Siyuan Chang
Jiang Wang
Yulin Zhu
Xile Wei
Bin Deng
Huiyan Li
Chen Liu
Publikationsdatum
07.06.2022
Verlag
Springer Netherlands
Erschienen in
Cognitive Neurodynamics / Ausgabe 2/2023
Print ISSN: 1871-4080
Elektronische ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-022-09822-1

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