Recurrent timing nets for auditory scene analysis

Abstract

We have recently proposed neural timing networks that operate on temporal fine structure of inputs to build up and separate periodic signals with different fundamental periods (Neural Networks, 14: 737-753, 2001). Simple recurrent nets consist of arrays of coincidence detectors fed by common input lines and conduction delay loops of different recurrence times. Short-term facilitation amplifies correlations between input and loop signals to amplify periodic patterns and segregate those with different periods, thereby allowing constituent waveforms to be recovered. Timing nets constitute a new, general strategy for scene analysis that builds up correlational invariances rather than feature-based labeling, segregation and binding of channels.