The Opponent Channel Population Code of Sound Location Is an Efficient Representation of Natural Binaural Sounds
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 i...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
2015
|
| Online Access: | http://hdl.handle.net/1721.1/98119 https://orcid.org/0000-0002-3791-5656 |
| _version_ | 1811098190682783744 |
|---|---|
| author | Mlynarski, Wiktor |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Mlynarski, Wiktor |
| author_sort | Mlynarski, Wiktor |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T17:11:19Z |
| format | Article |
| id | mit-1721.1/98119 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T17:11:19Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | mit-1721.1/981192022-10-03T11:00:55Z The Opponent Channel Population Code of Sound Location Is an Efficient Representation of Natural Binaural Sounds Mlynarski, Wiktor Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Mlynarski, Wiktor 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. German Science Foundation (Graduate College "InterNeuro") 2015-08-20T15:46:56Z 2015-08-20T15:46:56Z 2015-05 2014-09 Article http://purl.org/eprint/type/JournalArticle 1553-7358 1553-734X http://hdl.handle.net/1721.1/98119 Mlynarski, Wiktor. “The Opponent Channel Population Code of Sound Location Is an Efficient Representation of Natural Binaural Sounds.” Edited by Matthias Bethge. PLoS Comput Biol 11, no. 5 (May 21, 2015): e1004294. https://orcid.org/0000-0002-3791-5656 en_US http://dx.doi.org/10.1371/journal.pcbi.1004294 PLOS Computational Biology Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science |
| spellingShingle | Mlynarski, Wiktor The Opponent Channel Population Code of Sound Location Is an Efficient Representation of Natural Binaural Sounds |
| title | The Opponent Channel Population Code of Sound Location Is an Efficient Representation of Natural Binaural Sounds |
| title_full | The Opponent Channel Population Code of Sound Location Is an Efficient Representation of Natural Binaural Sounds |
| title_fullStr | The Opponent Channel Population Code of Sound Location Is an Efficient Representation of Natural Binaural Sounds |
| title_full_unstemmed | The Opponent Channel Population Code of Sound Location Is an Efficient Representation of Natural Binaural Sounds |
| title_short | The Opponent Channel Population Code of Sound Location Is an Efficient Representation of Natural Binaural Sounds |
| title_sort | opponent channel population code of sound location is an efficient representation of natural binaural sounds |
| url | http://hdl.handle.net/1721.1/98119 https://orcid.org/0000-0002-3791-5656 |
| work_keys_str_mv | AT mlynarskiwiktor theopponentchannelpopulationcodeofsoundlocationisanefficientrepresentationofnaturalbinauralsounds AT mlynarskiwiktor opponentchannelpopulationcodeofsoundlocationisanefficientrepresentationofnaturalbinauralsounds |