Feature Analysis of Natural Sounds in the Songbird Auditory Forebrain
1 Sloan Center for Theoretical Neuroscience, 2 Department of Psychiatry, and 3 Department of Physiology, University of California, San Francisco 94143-0444; and 4 Department of Psychology, University of California, Berkeley, California 94720-1650 Sen, Kamal, Frédéric E. Theunissen, and Allis...
Gespeichert in:
Veröffentlicht in: | Journal of neurophysiology 2001-09, Vol.86 (3), p.1445-1458 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 1 Sloan Center for Theoretical Neuroscience,
2 Department of Psychiatry, and
3 Department of Physiology, University of
California, San Francisco 94143-0444; and
4 Department of Psychology, University of
California, Berkeley, California 94720-1650
Sen, Kamal,
Frédéric E. Theunissen, and
Allison J. Doupe.
Feature Analysis of Natural Sounds in the Songbird Auditory
Forebrain. J. Neurophysiol. 86: 1445-1458, 2001. Although understanding the processing of natural sounds is an
important goal in auditory neuroscience, relatively little is known
about the neural coding of these sounds. Recently we demonstrated that
the spectral temporal receptive field (STRF), a description of the
stimulus-response function of auditory neurons, could be derived from
responses to arbitrary ensembles of complex sounds including
vocalizations. In this study, we use this method to investigate the
auditory processing of natural sounds in the birdsong system. We obtain
neural responses from several regions of the songbird auditory
forebrain to a large ensemble of bird songs and use these data to
calculate the STRFs, which are the best linear model of the
spectral-temporal features of sound to which auditory neurons respond.
We find that these neurons respond to a wide variety of features in
songs ranging from simple tonal components to more complex
spectral-temporal structures such as frequency sweeps and multi-peaked
frequency stacks. We quantify spectral and temporal characteristics of
these features by extracting several parameters from the STRFs.
Moreover, we assess the linearity versus nonlinearity of encoding by
quantifying the quality of the predictions of the neural responses to
songs obtained using the STRFs. Our results reveal successively complex
functional stages of song analysis by neurons in the auditory
forebrain. When we map the properties of auditory forebrain neurons, as
characterized by the STRF parameters, onto conventional anatomical
subdivisions of the auditory forebrain, we find that although some
properties are shared across different subregions, the distribution of
several parameters is suggestive of hierarchical processing. |
---|---|
ISSN: | 0022-3077 1522-1598 |
DOI: | 10.1152/jn.2001.86.3.1445 |