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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
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% |