Localized numerical impulse solutions in diffuse neural networks modeled by the complex fractional Ginzburg–Landau equation

• Published in: Communications in nonlinear science & numerical simulation 2016-10, Vol.39, p.396-410
• Main Authors: Mvogo, Alain, Tambue, Antoine, Ben-Bolie, Germain H., Kofané, Timoléon C.
• Format: Article
• Language: English
• Subjects: Complex fractional Ginzburg–Landau equation; Computer simulation; Equivalence; Finite difference method; Hindmarsh–Rose neural model; Impulses; Localized solutions; Mathematical analysis; Mathematical models; Networks; Neural networks; Riesz fractional finite-difference schemes
• ISSN: 1007-5704; 1878-7274
• DOI: 10.1016/j.cnsns.2016.03.008

•Effect on long-range diffusive interaction is studied in a network of Hindmarsh–Rose neural model.•Localized solutions are studied both analytically and numerically.•Short impulse-like structures are obtained for intercellular communication. We investigate localized wave solutions in a network of Hindmarsh–Rose neural model taking into account the long-range diffusive couplings. We show by a specific analytical technique that the model equations in the infrared limit (wave number k → 0) can be governed by the complex fractional Ginzburg–Landau (CFGL) equation. According to the stiffness of the system, we propose both the semi and the linearly implicit Riesz fractional finite-difference schemes to solve efficiently the CFGL equation. The obtained fractional numerical solutions for the nerve impulse reveal localized short impulse properties. We also show the equivalence between the continuous CFGL and the discrete Hindmarsh–Rose models for relatively large network.