Researchers uncover new method to control brain network synchronization
Identical pulses can either sync or desync neural networks depending on timing and strength.
Researchers Ehsan Ahmadi, Mojtaba Madadi Asl, and Alireza Valizadeh have published a study in *arXiv* demonstrating a novel method to control synchronization in neural networks using phase-targeted pulsed stimulation. The team analyzed a balanced excitatory-inhibitory network of exponential integrate-and-fire (EIF) neurons, showing that identical stimulation pulses can either enhance, suppress, or leave synchronization unchanged depending on their timing within the oscillation cycle.
The study introduces a framework combining network phase response curves (nPRC), network amplitude response curves (nARC), and synchrony changes to characterize collective network responses. Key findings include distinct synchronizing and desynchronizing windows based on pulse phase, with cumulative perturbations progressively desynchronizing network activity. These effects remain robust across varying stimulation intensities, synaptic time constants, and network realizations, offering general principles for state-dependent control of neural dynamics without altering optimal stimulation phases.
- Phase-targeted current pulses can either enhance or suppress neural network synchronization based on timing within the oscillation cycle.
- The framework uses nPRC and nARC to identify synchronizing/desynchronizing windows and optimal stimulation phases.
- Cumulative perturbations progressively desynchronize networks while maintaining robustness across varying conditions.
Why It Matters
This research provides a foundation for precise neural modulation, with potential applications in treating synchronization disorders like epilepsy or Parkinson’s disease.