Modeling populations of spiking neurons for fine timing sound localization

Qian Liu, Cameron Patterson, Steve Furber, Zhangqin Huang, Yibin Hou, Huibing Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

When two or more sound detectors are available, interaural time differences may be used to determine the direction of a sound's origin. This process, known as sound localization, is performed in mammals via the auditory pathways of the head and by computation in the brain. The Jeffress Model successfully describes the mechanism by exploiting coincidence detector neurons in conjunction with delay lines. However, one of the difficulties of using this model on neural simulators is that it requires timing accuracies which are much finer than the typical 1 ms resolution provided by simulation platforms. One solution is clearly to reduce the simulation's time step, but in this paper we also explore the use of population coding to represent more precise timing information without changing the simulation's timing resolution. The implementation of both the Jeffress and population coded models are contrasted, together with their results, which show that population coding is indeed able to provide successful sound localization. © 2013 IEEE.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks|Proc Int Jt Conf Neural Networks
Place of PublicationUSA
PublisherIEEE
ISBN (Print)9781467361293
DOIs
Publication statusPublished - 2013
Event2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX
Duration: 1 Jul 2013 → …

Conference

Conference2013 International Joint Conference on Neural Networks, IJCNN 2013
CityDallas, TX
Period1/07/13 → …

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