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

MÅ‚ynarski 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

Spectrotemporal properties of the representation.A) Centers of mass of monaural modulation spectra. B) Centers of mass of temporal modulation in monaural parts of Bi basis functions plotted C) Centers of mass of spectral modulation in monaural parts of Bi basis functions plotted. Letters correspond to panels in Fig 4. Black dashed lines depict linear regression fits. Parameters of each fit are written in figure insets.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4440638&req=5

pcbi.1004294.g005: Spectrotemporal properties of the representation.A) Centers of mass of monaural modulation spectra. B) Centers of mass of temporal modulation in monaural parts of Bi basis functions plotted C) Centers of mass of spectral modulation in monaural parts of Bi basis functions plotted. Letters correspond to panels in Fig 4. Black dashed lines depict linear regression fits. Parameters of each fit are written in figure insets.

Mentions: To get a more detailed understanding of the spectrotemporal features captured by the representation, analysis of modulation spectra was performed. A modulation spectrum is a 2D Fourier transform of the spectrotemporal representation of a signal. It is known that modulation spectra of natural sounds posess specific structure [40]. Here, modulation spectrum was computed separately for monaural parts of amplitude basis functions Bi (see Methods). In the next step a center of mass of each of the modulation spectra was computed. Centers of mass are represented by single points in Fig 5A.


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

MÅ‚ynarski W - PLoS Comput. Biol. (2015)

Spectrotemporal properties of the representation.A) Centers of mass of monaural modulation spectra. B) Centers of mass of temporal modulation in monaural parts of Bi basis functions plotted C) Centers of mass of spectral modulation in monaural parts of Bi basis functions plotted. Letters correspond to panels in Fig 4. Black dashed lines depict linear regression fits. Parameters of each fit are written in figure insets.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004294.g005: Spectrotemporal properties of the representation.A) Centers of mass of monaural modulation spectra. B) Centers of mass of temporal modulation in monaural parts of Bi basis functions plotted C) Centers of mass of spectral modulation in monaural parts of Bi basis functions plotted. Letters correspond to panels in Fig 4. Black dashed lines depict linear regression fits. Parameters of each fit are written in figure insets.
Mentions: To get a more detailed understanding of the spectrotemporal features captured by the representation, analysis of modulation spectra was performed. A modulation spectrum is a 2D Fourier transform of the spectrotemporal representation of a signal. It is known that modulation spectra of natural sounds posess specific structure [40]. Here, modulation spectrum was computed separately for monaural parts of amplitude basis functions Bi (see Methods). In the next step a center of mass of each of the modulation spectra was computed. Centers of mass are represented by single points in Fig 5A.

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