Fractional-order Izhikevich neuron Model: PI-rules numerical simulations and parameter identification

Amr M. AbdelAty and Mohammed E. Fouda

Chaos, Solitons & Fractals, 2025, vol. 194, issue C

Abstract: This work introduces a novel approach to identifying parameters of the fractional-order (FO) Izhikevich spiking neuron model using real neuronal data. The primary contributions include the development of a limited memory numerical simulation scheme based on the modified Product-Integration Rectangular rule and the application of the Marine Predator Algorithm (MPA) to solve the nonlinear optimization problem of parameter identification. Experimental results demonstrate that the fractional-order neuron models significantly outperform the traditional integer-order models, as evidenced by higher median coincidence factors across multiple datasets. Specifically, the fractional-order models with smaller window sizes achieved superior performance, suggesting their potential for more accurate modeling of complex neuronal dynamics. This work paves the way for further exploration of fractional-order models in computational neuroscience, offering enhanced flexibility and precision in simulating neuronal behavior.

Keywords: Fractional order models; Identification; Optimization; Izhikevich neuron model; Spiking neuron; Marine predator algorithm (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:194:y:2025:i:c:s0960077925002164

DOI: 10.1016/j.chaos.2025.116203

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