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Emergent explosive synchronization in adaptive complex networks.

Vanesa Avalos-Gaytán1, Juan A Almendral2, I Leyva2

  • 1Research Center in Applied Mathematics, Universidad Autónoma de Coahuila, Saltillo, Coahuila 25115, Mexico.

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Summary
This summary is machine-generated.

This study introduces an adaptive network model that couples synchronization with anti-Hebbian learning to explore network structure. The model reveals how inhibitory effects can lead to explosive synchronization in complex systems like brain networks.

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Area of Science:

  • Complex systems
  • Network science
  • Computational neuroscience

Background:

  • Adaptation is crucial for network structure and function.
  • While synchronization drives adaptation, excessive synchronization can be detrimental.
  • Coherence alone doesn't explain all network structural features.

Purpose of the Study:

  • To propose an adaptive network model incorporating inhibitory effects.
  • To investigate how network structure evolves under adaptive rules.
  • To understand the emergence of explosive synchronization.

Main Methods:

  • Developed an adaptive network model coupling node state synchronization with link weight evolution.
  • Employed an anti-Hebbian adaptive rule to model inhibitory effects.
  • Analyzed emergent network structures and dynamics.

Main Results:

  • The model spontaneously developed structures supporting explosive synchronization.
  • Coupling synchronization dynamics with anti-Hebbian learning shapes network architecture.
  • Inhibitory effects are key to achieving robust adaptive network structures.

Conclusions:

  • The proposed model provides insights into the structural and dynamical organization of biological networks.
  • Explosive synchronization can emerge from adaptive processes involving inhibition.
  • Findings are relevant for understanding brain network organization and function.