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

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  • AbdelAty, Amr M.
  • Fouda, Mohammed E.

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.

Suggested Citation

  • AbdelAty, Amr M. & Fouda, Mohammed E., 2025. "Fractional-order Izhikevich neuron Model: PI-rules numerical simulations and parameter identification," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:chsofr:v:194:y:2025:i:c:s0960077925002164
    DOI: 10.1016/j.chaos.2025.116203
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    References listed on IDEAS

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