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The efficiency of synchronization dynamics and the role of network syncreactivity

Author

Listed:
  • Amirhossein Nazerian

    (University of New Mexico)

  • Joseph D. Hart

    (US Naval Research Laboratory, Code 5675)

  • Matteo Lodi

    (University of Genoa)

  • Francesco Sorrentino

    (University of New Mexico)

Abstract

Synchronization of coupled oscillators is a fundamental process in both natural and artificial networks. While much work has investigated the asymptotic stability of the synchronous solution, the fundamental question of the transient behavior toward synchronization has received far less attention. In this work, we present the transverse reactivity as a metric to quantify the instantaneous rate of growth or decay of desynchronizing perturbations. We first use the transverse reactivity to design a coupling-efficient and energy-efficient synchronization strategy that involves varying the coupling strength dynamically according to the current state of the system. We find that our synchronization strategy is able to synchronize networks in both simulation and experiment over a significantly larger (often by orders of magnitude) range of coupling strengths than is possible when the coupling strength is constant. Then, we characterize the effects of network topology on the transient dynamics towards synchronization by introducing the concept of network syncreactivity: A network with a larger syncreactivity has a larger transverse reactivity at every point on the synchronization manifold, independent of the oscillator dynamics. We classify real-world examples of complex networks in terms of their syncreactivity.

Suggested Citation

  • Amirhossein Nazerian & Joseph D. Hart & Matteo Lodi & Francesco Sorrentino, 2024. "The efficiency of synchronization dynamics and the role of network syncreactivity," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52486-0
    DOI: 10.1038/s41467-024-52486-0
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    References listed on IDEAS

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    1. Parastesh, Fatemeh & Azarnoush, Hamed & Jafari, Sajad & Hatef, Boshra & Perc, Matjaž & Repnik, Robert, 2019. "Synchronizability of two neurons with switching in the coupling," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 217-223.
    2. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    3. V. E. Demidov & H. Ulrichs & S. V. Gurevich & S. O. Demokritov & V. S. Tiberkevich & A. N. Slavin & A. Zholud & S. Urazhdin, 2014. "Synchronization of spin Hall nano-oscillators to external microwave signals," Nature Communications, Nature, vol. 5(1), pages 1-6, May.
    4. Apostolos Argyris & Dimitris Syvridis & Laurent Larger & Valerio Annovazzi-Lodi & Pere Colet & Ingo Fischer & Jordi García-Ojalvo & Claudio R. Mirasso & Luis Pesquera & K. Alan Shore, 2005. "Chaos-based communications at high bit rates using commercial fibre-optic links," Nature, Nature, vol. 438(7066), pages 343-346, November.
    5. Tengfei Hao & Qizhuang Cen & Yitang Dai & Jian Tang & Wei Li & Jianping Yao & Ninghua Zhu & Ming Li, 2018. "Breaking the limitation of mode building time in an optoelectronic oscillator," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
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