Entraining chimeras: The effect of driving with regular, irregular, and real-world phases

Area of Science:

• Nonlinear dynamics and complex systems
• Network science
• Computational neuroscience

Background:

• Chimera states represent partial synchronization in coupled oscillator networks, relevant to neuronal dynamics.
• Previous studies on chimera states often used periodic forcing in the thermodynamic limit, leaving gaps in understanding finite-size network responses and noisy drivers.

Purpose of the Study:

• Investigate how finite-size chimera states respond to external driving, including periodic and noisy signals.
• Explore the potential application of driven chimera states to analyze electroencephalography (EEG) signals from epilepsy patients.

Main Methods:

• Drove a finite-size oscillator network exhibiting chimera states with constant-angular-frequency phases and with superimposed noise.
• Analyzed the effects of angular-frequency mismatch, driving strength, and noise on chimera entrainment and collapse.
• Applied the driven chimera framework to EEG signals from seizure-free epilepsy patients (focal vs. nonfocal).

Main Results:

• Entrainment of chimera states was achieved within a specific range of angular-frequency mismatch and driving strength, without collapse.
• Noise addition reduced entrainment and promoted collapse into full synchronization.
• Focal EEG signals showed higher entrainment, coherence, and collapse power than nonfocal signals when the driver's dominant frequency matched the chimera's mean angular frequency.

Conclusions:

• External driving, with or without noise, significantly impacts chimera state dynamics, particularly their tendency to collapse into full synchronization.
• The study provides a framework for understanding driven chimera states and offers insights into brain dynamics in epilepsy patients by analyzing EEG signals.