Generalized synchronization of the extended Hindmarsh-Rose neuronal model with fractional order derivative

DOI10.1016/J.CHAOS.2018.11.028zbMath1442.34013OpenAlexW2905254681WikidataQ128786685 ScholiaQ128786685MaRDI QIDQ2201458

Tchoffo Martin, Tene Alain Giresse, Kofane Timoleon Crepin

Publication date: 29 September 2020

Published in: Chaos, Solitons and Fractals (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.chaos.2018.11.028

zbMATH Keywords

chaossynchronizationfractional-order derivativeAdam-Bashforth-Moulton methodHindmarsh-Rose neuronal model

Mathematics Subject Classification ID

Complex behavior and chaotic systems of ordinary differential equations (34C28) Fractional ordinary differential equations (34A08) Synchronization of solutions to ordinary differential equations (34D06)

Related Items (4)

INTERLAYER AND INTRALAYER SYNCHRONIZATION IN MULTIPLEX FRACTIONAL-ORDER NEURONAL NETWORKS ⋮ Generalized synchronization of regulate seizures dynamics in partial epilepsy with fractional-order derivatives ⋮ Numerical simulation of initial value problems with generalized Caputo-type fractional derivatives ⋮ Dynamical behavior and network analysis of an extended Hindmarsh-Rose neuron model

Uses Software

• Matlab

Cites Work

• Unnamed Item
• Predictability of chaotic dynamics. A finite-time Lyapunov exponents approach
• Chaos generalized synchronization of new Mathieu-Van der pol systems with new Duffing-Van der Pol systems as functional system by GYC partial region stability theory
• Chaotic fractional-order Coullet system: synchronization and control approach
• Bursting, chaos and birhythmicity originating from self-modulation of the inositol 1,4,5-trisphosphate signal in a model for intracellular Ca\(^{2+}\) oscillations
• Stability of the synchronized network of Hindmarsh-Rose neuronal models with nearest and global couplings
• Lag synchronization of multiple identical Hindmarsh-Rose neuron models coupled in a ring structure
• A comparison between the variational iteration method and Adomian decomposition method
• Multi-quasi-synchronization of coupled fractional-order neural networks with delays via pinning impulsive control
• Finite-time Lyapunov dimension and hidden attractor of the Rabinovich system
• Finite time Lyapunov exponent and advection-diffusion equation
• Analysis of two- and three-dimensional fractional-order Hindmarsh-Rose type neuronal models
• Chaos generalized synchronization of coupled Mathieu-van der Pol and coupled Duffing-van der Pol systems using fractional order-derivative
• Dynamics of Disordered Network of Coupled Hindmarsh–Rose Neuronal Models
• On fractional lyapunov exponent for solutions of linear fractional differential equations
• Deterministic and stochastic bifurcations in the Hindmarsh-Rose neuronal model
• Fractional Calculus
• Matlab Code for Lyapunov Exponents of Fractional-Order Systems
• Hidden Attractors on One Path: Glukhovsky–Dolzhansky, Lorenz, and Rabinovich Systems
• Fractional Calculus
• Characterization of finite-time Lyapunov exponents and vectors in two-dimensional turbulence
• Dispersion of passive tracers in closed basins: Beyond the diffusion coefficient

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