Effect of higher-order interactions on synchronization of neuron models with electromagnetic induction

Mohanasubha, R.; Devarajan, Subhasri; Kumarasamy, Suresh; Rajagopal, Karthikeyan

doi:10.1016/j.amc.2022.127447

Cited by 5 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Te literature on synchronization is huge, and one may refer to [1,3,4,6,8,10,[16][17][18][19][20][21][22] and references therein. Recently, several studies have dealt with the synchronization of complex networks with higher-order interactions [23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Synchronizability of Discrete Nonlinear Systems: A Master Stability Function Approach

Ramasamy,

Kumarasamy,

Sampathkumar

et al. 2023

Complexity

Self Cite

View full text Add to dashboard Cite

In recent times, studies on discrete nonlinear systems received much attention among researchers because of their potential applications in real-world problems. In this study, we conducted an in-depth exploration into the stability of synchronization within discrete nonlinear systems, specifically focusing on the Hindmarsh–Rose map, the Chialvo neuron model, and the Lorenz map. Our methodology revolved around the utilization of the master stability function approach. We systematically examined all conceivable coupling configurations for each model to ascertain the stability of synchronization manifolds. The outcomes underscored that only distinct coupling schemes manifest stable synchronization manifolds, while others do not exhibit this trait. Furthermore, a comprehensive analysis of the master stability function’s behavior was performed across a diverse range of coupling strengths σ and system parameters. These findings greatly enhance our understanding of network dynamics, as discrete-time dynamical systems adeptly replicate the dynamics of continuous-time models, offering significant reductions in computational complexity.

show abstract

Section: Introductionmentioning

confidence: 99%

Synchronizability of Discrete Nonlinear Systems: A Master Stability Function Approach

Ramasamy,

Kumarasamy,

Sampathkumar

et al. 2023

Complexity

Self Cite

View full text Add to dashboard Cite

show abstract

Formation of defects in network under local energy balance

Wang

Zhou

et al. 2023

Nonlinear Dyn

View full text Add to dashboard Cite

Effects of high-order interactions on synchronization of a fractional-order neural system

Saçu

2024

Cogn Neurodyn

View full text Add to dashboard Cite

In this study, effects of high-order interactions on synchronization of the fractional-order Hindmarsh–Rose neuron models have been examined deeply. Three different network situations in which first-order coupling, high-order couplings and first-plus second-order couplings included in the neuron models, have been considered, respectively. In order to find the optimal values of the first- and high-order coupling parameters by minimizing the cost function resulted from pairwise and triple interactions, the particle swarm optimization algorithm is employed. It has been deduced from the numerical simulation results that the first-plus second-order couplings induce the synchronization with both reduced first-order coupling strength and total cost compared to the first-order coupled case solely. When the only first-order coupled case is compared with the only second-order coupled case, it is determined that the neural network with only second-order couplings involved could achieve synchronization with lower coupling strength and, as a natural result, lower cost. On the other hand, solely second- and first-plus second-order coupled networks give very similar results each other. Therefore, high-order interactions have a positive effect on the synchronization. Additionally, increasing the network size decreases the values of the both first- and high-order coupling strengths to reach synchronization. However, in this case, total cost should be kept in the mind. Decreasing the fractional order parameter causes slower synchronization due to the decreased frequency of the neural response. On the other hand, more synchronous network is possible with increasing the fractional order parameter. Thus, the neural network with higher fractional order as well as high-order coupled is a good candidate in terms of the neural synchronization.

show abstract

Effect of higher-order interactions on synchronization of neuron models with electromagnetic induction

Cited by 5 publications

References 32 publications

Synchronizability of Discrete Nonlinear Systems: A Master Stability Function Approach

Synchronizability of Discrete Nonlinear Systems: A Master Stability Function Approach

Formation of defects in network under local energy balance

Effects of high-order interactions on synchronization of a fractional-order neural system

Contact Info

Product

Resources

About