Signal Processing in Periodically Forced Gradient Frequency Neural Networks

This paper develops a signal processing framework for gradient frequency neural networks (GFNNs) — networks of nonlinear oscillators with a range of natural frequencies — responding to periodic forcing. The analysis characterizes how such networks decompose complex acoustic signals, including music, into their constituent frequency components through resonance, providing a biologically plausible model of the auditory system's frequency analysis capabilities.