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Modeling Populations of Spiking Neurons for Fine Timing Sound Localization

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


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.