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Fractional-order Izhikevich neuron Model: PI-rules numerical simulations and parameter identification
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DOI：10.1016/j.chaos.2025.116203.png)
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Abstract
En 中文
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
Journal
C
IF:
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Papers:
1.3K
Citations:
3.8W