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Synchronization in Multiplex Leaky Integrate-and-Fire Networks With Nonlocal Interactions.

K Anesiadis1,2, A Provata1

  • 1Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", Athens, Greece.

Frontiers in Network Physiology
|March 17, 2023
PubMed
Summary
This summary is machine-generated.

This study explores synchronization in brain-inspired networks of Leaky Integrate-and-Fire (LIF) oscillators. Researchers found diverse states like chimera states and full coherence, especially with weak multiplexing between network layers.

Keywords:
chimera statescorrelation functionexcitatory couplinginhibitory couplingkuramoto order parametermultilayer networkssubthreshold oscillationsweak multiplexing

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Area of Science:

  • Computational Neuroscience
  • Complex Systems
  • Network Science

Background:

  • The brain exhibits complex connectivity patterns, with local interactions within hemispheres and homologous interactions between them.
  • Understanding synchronization in such networks is crucial for deciphering brain function.
  • Leaky Integrate-and-Fire (LIF) oscillators are a standard model for neuronal dynamics.

Purpose of the Study:

  • To investigate synchronization phenomena in a multiplex network of two coupled rings of LIF oscillators.
  • To model brain hemisphere connectivity using a network with nonlocal intra-ring and one-to-one inter-ring interactions.
  • To identify parameter regimes leading to various synchronization patterns, including chimera states.

Main Methods:

  • Numerical simulations of a multiplex network composed of two rings of identical LIF oscillators.
  • Analysis of network dynamics under varying intra-ring and inter-ring coupling strengths (positive and negative).
  • Identification of distinct dynamical regimes: coexistence of active and subthreshold domains, chimera states, solitary states, full coherence, and incoherence.

Main Results:

  • The multiplex network exhibits a rich variety of synchronization patterns, including chimera states, full coherence, and incoherence.
  • Weak inter-ring coupling (weak multiplexing) allows for stable, distinct synchronization patterns on each ring.
  • Specific synchronization patterns emerge near critical coupling points, transitioning between regimes like traveling fronts and chimera states.

Conclusions:

  • The brain-inspired multiplex network model can reproduce diverse complex dynamical states observed in neural systems.
  • The interplay between intra-ring and inter-ring coupling significantly influences network synchronization.
  • Weak multiplexing provides a mechanism for maintaining distinct functional states in coupled oscillatory systems.