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Author Joosten, Eva R. M. ♦ Neri, Peter
Source SpringerLink
Content type Text
Publisher Springer-Verlag
File Format PDF
Copyright Year ©2012
Language English
Subject Domain (in DDC) Technology ♦ Medicine & health
Subject Keyword Noise image classification ♦ Computational modeling ♦ Psychophysics ♦ MAX uncertainty model ♦ Statistical Physics, Dynamical Systems and Complexity ♦ Computer Application in Life Sciences ♦ Neurobiology ♦ Bioinformatics ♦ Neurosciences
Abstract Single neurons in auditory cortex display highly selective spectrotemporal properties: their receptive fields modulate over small fractions of an octave and integrate across temporal windows of 100–200 ms. We investigated how these characteristics impact auditory behavior. Human observers were asked to detect a specific sound frequency masked by broadband noise; we adopted an experimental design which required the engagement of frequency-selective mechanisms to perform above chance. We then applied psychophysical reverse correlation to derive spectrotemporal perceptual filters for the assigned task. We were able to expose signatures of neuronal-like spectrotemporal tuning on a scale of 1/10 octave and 50–100 ms, but detailed modeling of our results showed that observers were not able to rely on the explicit output of these channels. Instead, human observers pooled from a large bank of highly selective channels via a weighting envelope poorly tuned for frequency (on a scale of 1.5 octave) with sluggish temporal dynamics, followed by a highly nonlinear max-like operation. We conclude that human detection of specific frequencies embedded within complex sounds suffers from a high degree of intrinsic spectrotemporal uncertainty, resulting in low efficiency values (< 1 %) for this perceptual ability. Signatures of the underlying neural circuitry can be exposed, but there does not appear to be a direct line for accessing individual neural channels on a fine scale.
ISSN 03401200
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2012-08-02
Publisher Place Berlin/Heidelberg
e-ISSN 14320770
Journal Biological Cybernetics
Volume Number 106
Issue Number 8
Page Count 18
Starting Page 465
Ending Page 482


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Source: SpringerLink