Cortical transformation of spatial processing for solving the cocktail party problem: a computational model(1,2,3).

Date Issued
2016-01Publisher Version
10.1523/ENEURO.0086-15.2015Author(s)
Dong, Junzi
Colburn, H. Steven
Sen, Kamal
Metadata
Show full item recordPermanent Link
https://hdl.handle.net/2144/40659Version
Published version
Citation (published version)
Junzi Dong, H. Steven Colburn, Kamal Sen. 2016. "Cortical Transformation of Spatial Processing for Solving the Cocktail Party Problem: A Computational Model(1,2,3).." eNeuro, Volume 3, Issue 1, https://doi.org/10.1523/ENEURO.0086-15.2015Abstract
In multisource, "cocktail party" sound environments, human and animal auditory systems can use spatial cues to effectively separate and follow one source of sound over competing sources. While mechanisms to extract spatial cues such as interaural time differences (ITDs) are well understood in precortical areas, how such information is reused and transformed in higher cortical regions to represent segregated sound sources is not clear. We present a computational model describing a hypothesized neural network that spans spatial cue detection areas and the cortex. This network is based on recent physiological findings that cortical neurons selectively encode target stimuli in the presence of competing maskers based on source locations (Maddox et al., 2012). We demonstrate that key features of cortical responses can be generated by the model network, which exploits spatial interactions between inputs via lateral inhibition, enabling the spatial separation of target and interfering sources while allowing monitoring of a broader acoustic space when there is no competition. We present the model network along with testable experimental paradigms as a starting point for understanding the transformation and organization of spatial information from midbrain to cortex. This network is then extended to suggest engineering solutions that may be useful for hearing-assistive devices in solving the cocktail party problem.
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© 2016 Dong et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.Collections
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