Coherence Resonance in Complex Networks

In this Letter, we study the effect of time-periodic coupling strength (TPCS) on the coherence resonance (CR) of spiking behavior induced by a particular kind of non-Gaussian noise in Newman–Watts networks of Hodgkin–Huxley neurons. It is found that the CR by the non-Gaussian noise can be enhanced by TPCS when TPCS frequency is equal to or multiple of the inverse of the refractory period, and can occur in networks with more random shortcuts for TPCS than for constant coupling strength. Furthermore, the CR by the non-Gaussian noise can occur at smaller TPCS frequency when network randomness increases. These results show that the CR by the non-Gaussian noise can be enhanced by TPCS and can occur in more complex networks in case of TPCS. These findings may help to better understand the joint roles of the non-Gaussian noise and TPCS in the spiking activity of the neuronal networks.

Download Full-text

Coherence resonance in influencer networks

Nature Communications ◽

10.1038/s41467-020-20441-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ralf Tönjes ◽

Carlos E. Fiore ◽

Tiago Pereira

Keyword(s):

Complex Networks ◽

Structural Property ◽

Network Structure ◽

Coupled Oscillators ◽

Dynamical Response ◽

Coherence Resonance ◽

Stochastic Forcing ◽

Oscillatory Dynamics ◽

Highly Nonlinear ◽

Optimal Intensity

AbstractComplex networks are abundant in nature and many share an important structural property: they contain a few nodes that are abnormally highly connected (hubs). Some of these hubs are called influencers because they couple strongly to the network and play fundamental dynamical and structural roles. Strikingly, despite the abundance of networks with influencers, little is known about their response to stochastic forcing. Here, for oscillatory dynamics on influencer networks, we show that subjecting influencers to an optimal intensity of noise can result in enhanced network synchronization. This new network dynamical effect, which we call coherence resonance in influencer networks, emerges from a synergy between network structure and stochasticity and is highly nonlinear, vanishing when the noise is too weak or too strong. Our results reveal that the influencer backbone can sharply increase the dynamical response in complex systems of coupled oscillators.

Download Full-text