TY - JOUR T1 - Cortical transformation of spatial processing for solving the cocktail party problem: a computational model JF - eneuro JO - eneuro DO - 10.1523/ENEURO.0086-15.2015 SP - ENEURO.0086-15.2015 AU - Junzi Dong AU - H. Steven Colburn AU - Kamal Sen Y1 - 2016/01/13 UR - http://www.eneuro.org/content/early/2016/01/13/ENEURO.0086-15.2015.abstract N2 - In multi-source, “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 pre-cortical 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.Significance Statement: Spatial cues are known to be critical for human and animal brains when following specific sound sources in the presence of competing sounds, but the exact mechanism by which this happens is not clear. The role of spatial cues in localizing single sound sources in the midbrain is well documented, but how these extracted cues are used downstream in the cortex to separate competing sources is not clear. We present a computational neural network model based on recent recordings to bridge this gap. The model identifies specific candidate physiological mechanisms underlying this process and can be extended to construct engineering solutions that may be useful for hearing assistive devices for coping with the cocktail party problem. ER -