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Synchronization in random balanced networks.

Luis Carlos García del Molino1, Khashayar Pakdaman, Jonathan Touboul

  • 1Institut Jacques Monod, CNRS UMR 7592, Université Paris Diderot, Paris Cité Sorbonne, F-750205, Paris, France.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 16, 2013
PubMed
Summary
This summary is machine-generated.

Disordered neuronal networks exhibit a universal transition to synchronized states, driven by the interplay of structure and connectivity disorder. This emergent behavior requires more than spectral density analysis to understand.

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Area of Science:

  • Neuroscience
  • Complex Systems
  • Network Science

Background:

  • Understanding emergent behavior in interacting systems is crucial across scientific disciplines.
  • Neuronal networks, with their complex connectivity, are key models for studying these phenomena.
  • Disordered networks with balanced excitation and inhibition present unique challenges.

Purpose of the Study:

  • To investigate how network structure and disorder influence emergent dynamics in neuronal networks.
  • To identify the key factors driving transitions between different network states.
  • To develop a simplified model for analyzing network dynamics.

Main Methods:

  • Analysis of disordered neuronal networks with excitatory-inhibitory balance.
  • Examination of the interplay between network structure and connectivity disorder.
  • Development of a low-dimensional approximation for network dynamics.

Main Results:

  • A universal transition from trivial to synchronized states (stationary or periodic) was observed.
  • This transition is influenced by the combined effects of structure and disorder.
  • The spectral density of the connectivity matrix alone does not explain this transition.

Conclusions:

  • Network structure and disorder are critical determinants of emergent dynamics in neuronal systems.
  • A low-dimensional approximation effectively captures the role of structure and disorder.
  • The findings offer insights into the fundamental principles governing complex network behavior.