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Related Experiment Video

Updated: Jun 19, 2025

Rewiring Neuronal Circuits: A New Method for Fast Neurite Extension and Functional Neuronal Connection
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Emergent order in adaptively rewired networks.

Sudeshna Sinha1

  • 1Indian Institute of Science Education and Research Mohali, Knowledge City, SAS Nagar, Sector 81, Manauli PO 140 306, Punjab, India.

Chaos (Woodbury, N.Y.)
|July 24, 2024
PubMed
Summary

We developed adaptive strategies to control network dynamics by adjusting connections based on synchronization error. These methods effectively suppress chaos and find ordered network configurations.

Area of Science:

  • Complex Systems
  • Network Science
  • Chaos Theory

Background:

  • Complex networks often exhibit intricate dynamical states.
  • Controlling these states, particularly chaos, is crucial for system stability and function.
  • Adaptive mechanisms are needed to navigate vast configuration spaces for desired network behavior.

Purpose of the Study:

  • To propose and evaluate adaptive link change strategies for achieving ordered dynamical states in complex networks.
  • To investigate feedback mechanisms based on global synchronization error for network adaptation.
  • To demonstrate the effectiveness of these strategies in suppressing chaos within coupled chaotic map systems.

Main Methods:

  • Two adaptive strategies were proposed: threshold-based and error-proportional link changes.

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  • Global synchronization error was used as feedback to guide network connectivity adjustments.
  • The strategies were tested on two prototypical systems of coupled chaotic maps.

    • Main Results:

    • Both adaptive strategies successfully guided networks to chaos suppression within a defined tolerance.
    • A sharply defined transition to low mean synchronization error was observed after a critical coupling strength.
    • The time and fraction of link adaptation decreased significantly, indicating convergence to stable, ordered configurations.

    Conclusions:

    • Adaptive link change strategies are effective for efficient search and discovery of network configurations yielding targeted dynamics.
    • These methods offer potential for controlling extended interactive systems and understanding natural regularization mechanisms in complex networks.
    • The findings highlight the role of feedback-based adaptation in achieving desired network states and suppressing undesirable dynamics like chaos.