Mode Locking in Periodically Forced Gradient Frequency Neural Networks

This computational study analyzes mode-locking phenomena in gradient frequency neural networks (GFNNs) driven by periodic forcing. The authors demonstrate how networks of nonlinear oscillators with graded natural frequencies lock into stable frequency relationships with periodic inputs, forming the mathematical basis for understanding how the brain entrains to rhythmic auditory stimuli. These dynamics underlie the neural resonance mechanisms exploited in music-based therapeutic interventions.