logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Modeling auditory cortical processing as an adaptive chirplet transform

delete2000-06-01
delete11
PRE
AI
E
Eduardo Mercado
C
Catherine E. Myers
M
Mark A. Gluck
DOI：10.1016/S0925-2312(00)00260-5delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
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. (C) 2000 Elsevier Science B.V. All rights reserved.
Keywords:
neural
wavelet
unsupervised learning
plasticity
receptive field

Journal

Neurocomputing cover
Neurocomputing
IF:
6.5
Papers:
2.5W
Citations:
6.5W

Organization

No organization information available