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The opponent channel population code of sound location is an efficient representation of natural binaural sounds.

Młynarski W - PLoS Comput. Biol. (2015)

Bottom Line: Obtained tuning curves match well tuning characteristics of neurons in the mammalian auditory cortex.This study connects neuronal coding of the auditory space with natural stimulus statistics and generates new experimental predictions.Moreover, results presented here suggest that cortical regions with seemingly different functions may implement the same computational strategy-efficient coding.

View Article: PubMed Central - PubMed

Affiliation: Max-Planck Institute for Mathematics in the Sciences, Leipzig, Germany. wiktor.mlynarski@gmail.com

ABSTRACT
In mammalian auditory cortex, sound source position is represented by a population of broadly tuned neurons whose firing is modulated by sounds located at all positions surrounding the animal. Peaks of their tuning curves are concentrated at lateral position, while their slopes are steepest at the interaural midline, allowing for the maximum localization accuracy in that area. These experimental observations contradict initial assumptions that the auditory space is represented as a topographic cortical map. It has been suggested that a "panoramic" code has evolved to match specific demands of the sound localization task. This work provides evidence suggesting that properties of spatial auditory neurons identified experimentally follow from a general design principle- learning a sparse, efficient representation of natural stimuli. Natural binaural sounds were recorded and served as input to a hierarchical sparse-coding model. In the first layer, left and right ear sounds were separately encoded by a population of complex-valued basis functions which separated phase and amplitude. Both parameters are known to carry information relevant for spatial hearing. Monaural input converged in the second layer, which learned a joint representation of amplitude and interaural phase difference. Spatial selectivity of each second-layer unit was measured by exposing the model to natural sound sources recorded at different positions. Obtained tuning curves match well tuning characteristics of neurons in the mammalian auditory cortex. This study connects neuronal coding of the auditory space with natural stimulus statistics and generates new experimental predictions. Moreover, results presented here suggest that cortical regions with seemingly different functions may implement the same computational strategy-efficient coding.

No MeSH data available.


Related in: MedlinePlus

Higher-order basis functions.A) Explanation of the visualization of second layer basis functions. Top two panels depict the binaural amplitude basis function Bi. Spectrotemporal information in each ear is represented using isoprobability contours of Wigner-Ville distributions of first-layer basis functions (see Fig 2). Colors correspond to the log-amplitude weight. The bottom panel represents the IPD basis function ξi. Each gray bar represents one of 20 selected low-layer basis functions. Here almost all values are positive (the bars point upwards), which corresponds to the right-ear precedence. B)-J) Basis functions ordered vertically by spectral modulation and horizontally by the dominating side.
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pcbi.1004294.g004: Higher-order basis functions.A) Explanation of the visualization of second layer basis functions. Top two panels depict the binaural amplitude basis function Bi. Spectrotemporal information in each ear is represented using isoprobability contours of Wigner-Ville distributions of first-layer basis functions (see Fig 2). Colors correspond to the log-amplitude weight. The bottom panel represents the IPD basis function ξi. Each gray bar represents one of 20 selected low-layer basis functions. Here almost all values are positive (the bars point upwards), which corresponds to the right-ear precedence. B)-J) Basis functions ordered vertically by spectral modulation and horizontally by the dominating side.

Mentions: The second layer learned cooccuring phase and amplitude patterns forming a sparse, combinatorial code of the first layer output. Fig 4 displays 10 representative examples of basis function pairs ξi and Bi, which encoded amplitudes and IPDs respectively.


The opponent channel population code of sound location is an efficient representation of natural binaural sounds.

Młynarski W - PLoS Comput. Biol. (2015)

Higher-order basis functions.A) Explanation of the visualization of second layer basis functions. Top two panels depict the binaural amplitude basis function Bi. Spectrotemporal information in each ear is represented using isoprobability contours of Wigner-Ville distributions of first-layer basis functions (see Fig 2). Colors correspond to the log-amplitude weight. The bottom panel represents the IPD basis function ξi. Each gray bar represents one of 20 selected low-layer basis functions. Here almost all values are positive (the bars point upwards), which corresponds to the right-ear precedence. B)-J) Basis functions ordered vertically by spectral modulation and horizontally by the dominating side.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4440638&req=5

pcbi.1004294.g004: Higher-order basis functions.A) Explanation of the visualization of second layer basis functions. Top two panels depict the binaural amplitude basis function Bi. Spectrotemporal information in each ear is represented using isoprobability contours of Wigner-Ville distributions of first-layer basis functions (see Fig 2). Colors correspond to the log-amplitude weight. The bottom panel represents the IPD basis function ξi. Each gray bar represents one of 20 selected low-layer basis functions. Here almost all values are positive (the bars point upwards), which corresponds to the right-ear precedence. B)-J) Basis functions ordered vertically by spectral modulation and horizontally by the dominating side.
Mentions: The second layer learned cooccuring phase and amplitude patterns forming a sparse, combinatorial code of the first layer output. Fig 4 displays 10 representative examples of basis function pairs ξi and Bi, which encoded amplitudes and IPDs respectively.

Bottom Line: Obtained tuning curves match well tuning characteristics of neurons in the mammalian auditory cortex.This study connects neuronal coding of the auditory space with natural stimulus statistics and generates new experimental predictions.Moreover, results presented here suggest that cortical regions with seemingly different functions may implement the same computational strategy-efficient coding.

View Article: PubMed Central - PubMed

Affiliation: Max-Planck Institute for Mathematics in the Sciences, Leipzig, Germany. wiktor.mlynarski@gmail.com

ABSTRACT
In mammalian auditory cortex, sound source position is represented by a population of broadly tuned neurons whose firing is modulated by sounds located at all positions surrounding the animal. Peaks of their tuning curves are concentrated at lateral position, while their slopes are steepest at the interaural midline, allowing for the maximum localization accuracy in that area. These experimental observations contradict initial assumptions that the auditory space is represented as a topographic cortical map. It has been suggested that a "panoramic" code has evolved to match specific demands of the sound localization task. This work provides evidence suggesting that properties of spatial auditory neurons identified experimentally follow from a general design principle- learning a sparse, efficient representation of natural stimuli. Natural binaural sounds were recorded and served as input to a hierarchical sparse-coding model. In the first layer, left and right ear sounds were separately encoded by a population of complex-valued basis functions which separated phase and amplitude. Both parameters are known to carry information relevant for spatial hearing. Monaural input converged in the second layer, which learned a joint representation of amplitude and interaural phase difference. Spatial selectivity of each second-layer unit was measured by exposing the model to natural sound sources recorded at different positions. Obtained tuning curves match well tuning characteristics of neurons in the mammalian auditory cortex. This study connects neuronal coding of the auditory space with natural stimulus statistics and generates new experimental predictions. Moreover, results presented here suggest that cortical regions with seemingly different functions may implement the same computational strategy-efficient coding.

No MeSH data available.


Related in: MedlinePlus