Metastability of multi-population Kuramoto-Sakaguchi oscillators
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View abstract on PubMed
Summary
This summary is machine-generated.This study explores phase-lag effects in coupled oscillator networks, revealing metastable dynamics and diverse spatiotemporal patterns. The findings offer insights into brain network synchronization.
Area Of Science
- Dynamical systems
- Theoretical neuroscience
- Complex systems
Background
- The Kuramoto-Sakaguchi model describes coupled oscillator synchronization.
- Understanding collective dynamics in oscillator networks is crucial for various fields, including neuroscience.
- The Ott-Antonsen ansatz simplifies analysis of large populations of coupled oscillators.
Purpose Of The Study
- To investigate the influence of the phase-lag parameter (α) on the collective dynamics of an Ott-Antonsen reduced M-population of Kuramoto-Sakaguchi oscillators.
- To characterize the range of spatiotemporal patterns and transitions between them.
- To analyze the stability of different dynamic states and their dependence on the phase-lag parameter.
Main Methods
- Utilized the Ott-Antonsen reduced M-population model for Kuramoto-Sakaguchi oscillators.
- Performed linear stability analysis to determine stable regions of coherent states.
- Investigated spatiotemporal patterns and transitions for varying phase-lag parameters (α).
Main Results
- Observed diverse spatiotemporal patterns: coherent, traveling waves, partially synchronized, modulated, and incoherent states.
- Identified back-and-forth transitions between states, indicating metastability.
- Found stable traveling wave solutions in certain α ranges, even when coherent states were also stable.
- Determined specific α ranges associated with frequent metastable transitions between different states (e.g., coherent and partially synchronized states around α≈0.46π).
Conclusions
- The phase-lag parameter significantly influences the collective dynamics and emergent patterns in oscillator networks.
- Metastable dynamics, characterized by frequent transitions between states, are a key feature of this system.
- The model provides a framework for understanding complex phenomena like brain network synchronization and metastability.
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