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

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

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Article in Cognitive neurodynamics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Siyuan ChangSchool of Electrical and Information Engineering, Tianjin University, Tianjin, 30072 China.

Jiang WangSchool of Electrical and Information Engineering, Tianjin University, Tianjin, 30072 China.

Yulin ZhuSchool of Electrical and Information Engineering, Tianjin University, Tianjin, 30072 China.

Xile WeiSchool of Electrical and Information Engineering, Tianjin University, Tianjin, 30072 China.

Bin DengSchool of Electrical and Information Engineering, Tianjin University, Tianjin, 30072 China.

Huiyan LiSchool of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China.

Chen LiuSchool of Electrical and Information Engineering, Tianjin University, Tianjin, 30072 China.ORCID 0000-0003-1635-3479

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6 · The paper itself

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.

Indexed as

Genetic algorithmNeural activityNonlinear dynamical modelingParticle swarm optimizationVolterra series

Identifiers

PMID37007203

DOI10.1007/s11571-022-09822-1

PMCPMC10050660

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