Modeling auditory cortical processing as an adaptive chirplet transform

Eduardo Mercado, Catherine Myers, Mark Gluck

Research output: Contribution to journalConference articlepeer-review

16 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)913-919
Number of pages7
JournalNeurocomputing
Volume32-33
DOIs
StatePublished - Jan 1 2000
EventThe 8th Annual Computational Neuroscience Meeting (CNS'99) - Pittsburgh, PA, USA
Duration: Jul 18 1999Jul 22 1999

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Keywords

  • Neural
  • Plasticity
  • Receptive field
  • Unsupervised learning
  • Wavelet

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