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Related Experiment Videos

Awaking and sleeping of a complex network.

R López-Ruiz1, Y Moreno, A F Pacheco

  • 1Departament of Computer Science and BIFI, Faculty of Sciences, University of Zaragoza, 50009, Zaragoza, Spain. rilopez@unizar.es

Neural Networks : the Official Journal of the International Neural Network Society
|June 27, 2006
PubMed
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This study models network activity transitions. A critical coupling value enables a synchronized "on" state, while lower values result in a "off" state, demonstrating bistability in network dynamics.

Area of Science:

  • Complex systems
  • Network science
  • Dynamical systems

Background:

  • Networks with local dynamics exhibit complex behaviors.
  • Coupling strength significantly influences network state.
  • Understanding transitions between active and inactive states is crucial for system modeling.

Purpose of the Study:

  • To investigate the emergence of synchronized activity in a network with logistic-like local dynamics.
  • To analyze the role of multiplicative coupling in network state transitions.
  • To model brain-like systems' waking and sleeping behaviors.

Main Methods:

  • Simulating a network with mean-field multiplicative coupling among nearest neighbors.
  • Analyzing the system's dynamics as a function of coupling parameter.

Related Experiment Videos

  • Characterizing state transitions, particularly on scale-free networks.

    • Main Results:

    • A critical coupling threshold induces a global bifurcation, transitioning the network from an inactive to an active, synchronized state.
    • This bifurcation is independent of network topology.
    • The transition from the active to inactive state is topology-dependent, especially for scale-free networks, revealing system bistability.

    Conclusions:

    • The model successfully demonstrates a switchable network behavior analogous to waking and sleeping.
    • Network topology plays a critical role in the deactivation process.
    • This work provides a simplified model for understanding bistable behaviors in multi-component systems.