Modeling auditory cortical processing as an adaptive chirplet transform

Recent evidence suggests that (a) auditory cortical neurons are tuned to co mplex time-varying acoustic features, (b) auditory cortex consists of sever al fields that decompose sounds in parallel, (c) the metric for such decomp osition varies across species, and (d) auditory cortical representations ca n be rapidly modulated. Past computational models of auditory cortical proc essing 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-dimension al auditory parameter space. This transformation can be modeled as a chirpl et transform implemented via a self-organizing neural network. (C) 2000 Els evier Science B.V. All rights reserved.