Effect of small-world topology on wave propagation on networks of excitable elements
arXiv:1408.5731 · doi:10.1088/1367-2630/17/2/023058
Abstract
We study excitation waves on a Newman-Watts small-world network model of coupled excitable elements. Depending on the global coupling strength, we find differing resilience to the added long-range links and different mechanisms of propagation failure. For high coupling strengths, we show agreement between the network and a reaction-diffusion model with additional mean-field term. Employing this approximation, we are able to estimate the critical density of long-range links for propagation failure.
arXiv comment19 pages, 8 figures and 5 pages supplementary material
References in corpus (13)
- Statistical mechanics of complex networks
- The structure and function of complex networks
- Self-sustained activity in a small-world network of excitable neurons
- Cluster and group synchronization in delay-coupled networks
- Experimental Observations of Group Synchrony in a System of Chaotic Optoelectronic Oscillators
- Emergence of self-sustained patterns in small-world excitable media
- Two-dimensional wave patterns of spreading depolarization: retracting, re-entrant, and stationary waves
- Traveling and pinned fronts in bistable reaction-diffusion systems on network
- Failure of feedback as a putative common mechanism of spreading depolarizations in migraine and stroke
- Propagation failure of excitation waves on trees and random networks
- Nucleation of reaction-diffusion waves on curved surfaces
- Nonlocal control of pulse propagation in excitable media
- Wave trains, self-oscillations and synchronization in discrete media
Cited by in corpus (5)
- Control of amplitude chimeras by time delay in dynamical networks
- Turing instability in Reaction-Diffusion models on complex networks
- Excitation Waves on a Minimal Small-World Model
- Stationary patterns in star networks of bistable units: Theory and application to chemical reactions
- Propagation of extreme events in multiplex neuronal networks