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Auditory Frequency and Intensity Discrimination Explained Using a Cortical Population Rate Code

Overview of attention for article published in PLoS Computational Biology, November 2013
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Title
Auditory Frequency and Intensity Discrimination Explained Using a Cortical Population Rate Code
Published in
PLoS Computational Biology, November 2013
DOI 10.1371/journal.pcbi.1003336
Pubmed ID
Authors

Christophe Micheyl, Paul R. Schrater, Andrew J. Oxenham

Abstract

The nature of the neural codes for pitch and loudness, two basic auditory attributes, has been a key question in neuroscience for over century. A currently widespread view is that sound intensity (subjectively, loudness) is encoded in spike rates, whereas sound frequency (subjectively, pitch) is encoded in precise spike timing. Here, using information-theoretic analyses, we show that the spike rates of a population of virtual neural units with frequency-tuning and spike-count correlation characteristics similar to those measured in the primary auditory cortex of primates, contain sufficient statistical information to account for the smallest frequency-discrimination thresholds measured in human listeners. The same population, and the same spike-rate code, can also account for the intensity-discrimination thresholds of humans. These results demonstrate the viability of a unified rate-based cortical population code for both sound frequency (pitch) and sound intensity (loudness), and thus suggest a resolution to a long-standing puzzle in auditory neuroscience.

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The data shown below were compiled from readership statistics for 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
Switzerland 2 2%
United Kingdom 2 2%
India 1 <1%
Canada 1 <1%
Netherlands 1 <1%
Unknown 92 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 25%
Researcher 18 17%
Student > Master 10 10%
Professor > Associate Professor 9 9%
Professor 7 7%
Other 22 21%
Unknown 11 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 20%
Neuroscience 20 19%
Psychology 11 11%
Engineering 10 10%
Computer Science 6 6%
Other 14 14%
Unknown 21 20%