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Complex partial synchronization patterns in networks of delay-coupled neurons.

D Nikitin1, I Omelchenko2, A Zakharova2

  • 1Saint Petersburg State University, Saint Petersburg, Russia.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|July 23, 2019
PubMed
Summary
This summary is machine-generated.

Researchers discovered a new synchronization pattern in complex networks of FitzHugh-Nagumo oscillators. This finding reveals coexisting slow and fast oscillations, offering insights into nonlinear dynamics and neural network behavior.

Keywords:
chimera statesdelay couplingmultiplex networksneural networks

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Area of Science:

  • * Complex systems science
  • * Computational neuroscience
  • * Nonlinear dynamics

Background:

  • * FitzHugh-Nagumo oscillators are a fundamental model for neuronal dynamics.
  • * Multiplex networks with delay-coupled elements exhibit complex spatio-temporal behaviors.
  • * Chimera states represent a known form of partial synchronization in such systems.

Purpose of the Study:

  • * To investigate the spatio-temporal dynamics of delay-coupled FitzHugh-Nagumo oscillators in multiplex networks.
  • * To identify novel synchronization patterns beyond chimera states.
  • * To develop an analytical framework and control strategy for observed dynamics.

Main Methods:

  • * Analysis of spatio-temporal dynamics in multiplex networks.
  • * Modeling of delay-coupled FitzHugh-Nagumo oscillators with non-local and fractal connectivities.
  • * Derivation of analytical explanations for emergent phenomena.
  • * Development of a control scheme for neuronal populations.

Main Results:

  • * Identification of a new dynamical regime characterized by the coexistence of slow and fast oscillations.
  • * Observation of partial synchronization patterns associated with this new regime.
  • * Development of an analytical explanation for the emergence of these coexisting oscillations.
  • * Proposal of a method to control the number of fast and slow neurons in network layers.

Conclusions:

  • * The study reveals a previously undiscovered synchronization pattern in complex neural network models.
  • * Analytical insights provide a deeper understanding of nonlinear dynamics in delay-coupled systems.
  • * The proposed control scheme offers potential applications in manipulating network behavior.