From Synchronization Physics to Trained Dynamics: A Survey of Oscillator Networks in Machine Learning
SSRN Working Paper, September 2026
Eric Kryski
A survey of coupled-oscillator networks as a machine-learning substrate, organizing eighteen published oscillatory neural network systems around whether gradients reach the oscillator dynamics and how a model is trained around them. Argues that a substrate whose native operations are resonance and entrainment resembles how neurons evolved to sense physical signals, and closes with open directions for shared data, model evaluation, and controls.