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Updated: Oct 26, 2025

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
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Phase-dynamic causalities within dynamical effects framework.

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Summary
This summary is machine-generated.

This study reveals how transfer entropy (TE) quantifies causal coupling in oscillatory systems. Reduced TE offers insights into phase diffusion and synchronization dynamics under varying noise and coupling.

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Area of Science:

  • Nonlinear dynamics
  • Complex systems analysis
  • Statistical physics

Background:

  • Directional coupling in oscillatory systems is crucial for understanding emergent behaviors.
  • Phase-dynamic quantifiers like transfer entropy (TE) are used to measure causal effects.
  • The relationship between these quantifiers and phase diffusion requires further investigation.

Purpose of the Study:

  • To numerically investigate phase-dynamic quantifiers of directional coupling.
  • To analyze transfer entropy (TE), differential quantifier, and squared-coefficients quantifier.
  • To explore their behavior in a system of two stochastic Kuramoto oscillators.

Main Methods:

  • Numerical simulations of two coupled stochastic Kuramoto oscillators.
  • Analysis of phase-dynamic quantifiers including transfer entropy (TE).
  • Investigation within the framework of dynamical causal effects.

Main Results:

  • Reduced TE equals twice the asymptotic effect on phase diffusion in non-synchronous regimes with high noise.
  • TE rate increases unboundedly with coupling strength, even during effective synchronization.
  • Reduced TE simplifies to a function of noise and coupling ratios in synchronization regimes.

Conclusions:

  • Phase-dynamic quantifiers provide distinct insights into causal coupling.
  • TE rate is a sensitive indicator of coupling strength, even in synchronized states.
  • Reduced TE offers a normalized measure of coupling effects on phase diffusion.