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Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
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Published on: May 29, 2017

Multiple effects of gradient coupling on network synchronization.

Xingang Wang1, Cangtao Zhou, Choy Heng Lai

  • 1Temasek Laboratories, National University of Singapore, 117508 Singapore.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|July 23, 2008
PubMed
Summary
This summary is machine-generated.

While gradient couplings can enhance network synchronizability, an optimal gradient exists. Excessive gradients may degrade synchronization, especially in sparse, homogeneous networks.

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Area of Science:

  • Complex networks
  • Network science
  • Nonlinear dynamics

Background:

  • Gradient and asymmetric couplings can improve network synchronizability.
  • Increasing gradient strength often monotonically enhances synchronizability.

Purpose of the Study:

  • To investigate the existence of an optimal gradient strength for maximizing network synchronizability.
  • To explore the mechanisms by which gradient coupling affects synchronization.
  • To compare the effects of gradient coupling on different network structures.

Main Methods:

  • Eigenvalue analysis for indirect simulation of synchronizability.
  • Direct simulation of coupled nonidentical oscillators.
  • Analysis of network breaking phenomenon under gradient coupling.

Main Results:

  • A critical optimal gradient strength exists, beyond which synchronizability decreases.
  • Excessive gradient coupling can lead to network breaking, suppressing synchronization.
  • Dense heterogeneous networks are less susceptible to network breaking and benefit more from stronger gradients than sparse homogeneous networks.

Conclusions:

  • The relationship between gradient strength and network synchronizability is not always monotonic.
  • Network structure significantly influences the impact of gradient coupling on synchronization.
  • Understanding optimal gradient strengths is crucial for designing robust and synchronizable complex networks.