Title |
Sparse Codes for Speech Predict Spectrotemporal Receptive Fields in the Inferior Colliculus
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Published in |
PLoS Computational Biology, July 2012
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DOI | 10.1371/journal.pcbi.1002594 |
Pubmed ID | |
Authors |
Nicole L. Carlson, Vivienne L. Ming, Michael Robert DeWeese |
Abstract |
We have developed a sparse mathematical representation of speech that minimizes the number of active model neurons needed to represent typical speech sounds. The model learns several well-known acoustic features of speech such as harmonic stacks, formants, onsets and terminations, but we also find more exotic structures in the spectrogram representation of sound such as localized checkerboard patterns and frequency-modulated excitatory subregions flanked by suppressive sidebands. Moreover, several of these novel features resemble neuronal receptive fields reported in the Inferior Colliculus (IC), as well as auditory thalamus and cortex, and our model neurons exhibit the same tradeoff in spectrotemporal resolution as has been observed in IC. To our knowledge, this is the first demonstration that receptive fields of neurons in the ascending mammalian auditory pathway beyond the auditory nerve can be predicted based on coding principles and the statistical properties of recorded sounds. |
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Geographical breakdown
Country | Count | As % |
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India | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 7 | 5% |
Germany | 2 | 1% |
United Kingdom | 2 | 1% |
Netherlands | 1 | <1% |
Canada | 1 | <1% |
Finland | 1 | <1% |
China | 1 | <1% |
Denmark | 1 | <1% |
Unknown | 121 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 42 | 31% |
Researcher | 31 | 23% |
Student > Master | 13 | 9% |
Student > Bachelor | 10 | 7% |
Professor > Associate Professor | 9 | 7% |
Other | 22 | 16% |
Unknown | 10 | 7% |
Readers by discipline | Count | As % |
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Neuroscience | 35 | 26% |
Agricultural and Biological Sciences | 27 | 20% |
Engineering | 26 | 19% |
Computer Science | 11 | 8% |
Physics and Astronomy | 10 | 7% |
Other | 12 | 9% |
Unknown | 16 | 12% |