Spiking neural network model of sound localisation using the interaural intensity difference

Article

Wall, J., McDaid, Liam J., Maguire, Liam P. and McGinnity, Thomas M. 2012. Spiking neural network model of sound localisation using the interaural intensity difference. IEEE Transactions on Neural Networks. 23 (4), pp. 574-586.
AuthorsWall, J., McDaid, Liam J., Maguire, Liam P. and McGinnity, Thomas M.
Abstract

In this paper, a spiking neural network (SNN) architecture to simulate the sound localization ability of the mammalian auditory pathways using the interaural intensity difference cue is presented. The lateral superior olive was the inspiration for the architecture, which required the integration of an auditory periphery (cochlea) model and a model of the medial nucleus of the trapezoid body. The SNN uses leaky integrateand-fire excitatory and inhibitory spiking neurons, facilitating synapses and receptive fields. Experimentally derived headrelated transfer function (HRTF) acoustical data from adult domestic cats were employed to train and validate the localization ability of the architecture, training used the supervised learning algorithm called the remote supervision method to determine the azimuthal angles. The experimental results demonstrate that the architecture performs best when it is localizing high-frequency sound data in agreement with the biology, and also shows a high degree of robustness when the HRTF acoustical data is corrupted by noise.

KeywordsInteraural intensity difference; lateral superior olive; sound localization; spiking neural networks
JournalIEEE Transactions on Neural Networks
Journal citation23 (4), pp. 574-586
ISSN2162-237X
Year2012
PublisherIEEE
Accepted author manuscript
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CC BY-NC-ND
Web address (URL)http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6145692
https://www.researchgate.net/publication/234128559_Spiking_Neural_Network_Model_of_Sound_Localization_Using_the_Interaural_Intensity_Difference
Publication dates
Print03 Feb 2012
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Deposited16 Nov 2015
Copyright information© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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