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Title: The role of canonical neural computations in sound localization
Author: Lestang, Jean-Hugues
ISNI:       0000 0004 8504 8408
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2019
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Localizing sounds is an important ability for many species. However, reverberative sounds present a significant challenge to the auditory system as later arriving reverberations may carry confounding localization cues. The 'precedence effect' refers to a set of perceptual behaviours related to this situation. Studies investigating the precedence effect observed that the auditory system tends to focus the core of the localization process on the computation of localization cues carried by the first arriving sound. Doing so relieves the auditory system from dealing with contradictory localization cues in later arriving sounds. A recent study by Dietz et al. (2013) confirmed that human listeners use this approach to deal with dynamic localization cues. In order to provide an explanation for this finding, we first tested several auditory models on the specific task described in Dietz et al. (2013) in order to shortlist possible mechanisms capable of accounting for the early extraction of temporal binaural cues. We found that the best candidates to account for this data are single cell mechanisms, such as adaptation and onset firing, as well as inhibitory population mechanisms. To further understand how each mechanism contributes to the suppression of lagging sounds, we designed more general models capable of demonstrating the principal features of each mechanism. We tested these models thoroughly and found that all mechanisms were able to reproduce the results over a wide range of parameters. This finding suggests that mechanisms responsible for the precedence effect may not be specialized to perform this specific task but instead may be the results of more commonly found neural circuits in the brain. Finally, to facilitate comparing the performance of auditory models on psychoacoustical data, we also designed and implemented an auditory modelling framework capable of addressing many challenges existing in the field of auditory modelling.
Supervisor: Goodman, Daniel Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available