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Synchronization regimes in coupled noisy excitable systems.

B Hu1, C Zhou

  • 1Department of Physics and Center for Nonlinear Studies, Hong Kong Baptist University, Hong Kong, China.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|April 20, 2001
PubMed
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We investigated synchronization in coupled noisy excitable systems. Increasing coupling strength leads to distinct regimes: desynchronous, train, phase, and complete synchronization, differing from chaotic systems.

Area of Science:

  • Nonlinear Dynamics
  • Complex Systems
  • Computational Neuroscience

Background:

  • Excitable systems are fundamental in various scientific fields, including neuroscience and physics.
  • Understanding synchronization in coupled systems is crucial for modeling complex phenomena.
  • Systems near an Andronov bifurcation exhibit unique dynamic behaviors.

Purpose of the Study:

  • To investigate the synchronization regimes in a system of two coupled noisy excitable systems.
  • To analyze the impact of coupling strength on the system's dynamics.
  • To differentiate the synchronization mechanisms from those in coupled phase-coherent chaotic systems.

Main Methods:

  • Numerical simulations of two coupled noisy excitable systems.
  • Analysis of phase space to identify fixed points (node, saddle, unstable focus).

Related Experiment Videos

  • Systematic variation of coupling strength to observe transitions in synchronization states.

    • Main Results:

    • The system transitions from desynchronization to train, phase, and complete synchronization with increased coupling.
    • Train synchronization is linked to the presence of a saddle point in the phase space.
    • The observed synchronization transitions differ from those in coupled phase-coherent chaotic systems.

    Conclusions:

    • Coupling strength dictates the synchronization regime in noisy excitable systems.
    • The saddle point plays a key role in enabling train synchronization.
    • Noisy excitable systems exhibit distinct synchronization dynamics compared to chaotic systems.