Modeling auditory cortical processing as an adaptive chirplet transform

Recent evidence suggests that (a) auditory cortical neurons are tuned to complex time-varying acoustic features, (b) auditory cortex consists of several fields that decompose sounds in parallel, (c) the metric for such decomposition varies across species, and (d) auditory cortical representations can be rapidly modulated. Past computational models of auditory cortical processing cannot capture such representational complexity. This paper proposes a novel framework in which auditory signal processing is characterized as an adaptive transformation from a one-dimensional space into an n-dimensional auditory parameter space. This transformation can be modeled as a chirplet transform implemented via a self-organizing neural network.