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Spiking Neural Network Model of Sound Localisation using the interaural intensity Difference

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Wall, Julie, McDaid, Liam, Maguire, Liam and McGinnity, TM (2012) Spiking Neural Network Model of Sound Localisation using the interaural intensity Difference. IEEE Transactions on Neural Networks and Learning Systems, 23 (4). pp. 574-586. [Journal article]

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DOI: 10.1109/TNNLS.2011.2178317


Abstract—In this paper, a spiking neural network (SNN) architecture to simulate the sound localisation ability of the mammalian auditory pathways using the interaural intensitydifference (IID) cue is presented. The lateral superior olive (LSO) 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 (MNTB). The SNN uses leaky integrate and fire excitatory and inhibitory spiking neurons, facilitating synapses and receptive fields. Experimentally derived Head Related Transfer Function (HRTF) acoustical datafrom adult domestic cats were employed to train and validate the localisation ability of the architecture; training used the supervised learning algorithm called the Remote Supervision Method (ReSuMe) to determine the azimuthal angles. The experimental results demonstrate that the architecture performs best when it is localising high frequency sound data in agreement with the biology, and also shows a high degree of robustness when theHRTF acoustical data is corrupted by noise.Index Terms—Spiking neural networks, sound localisation, lateral superior olive, interaural intensity difference

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:16734
Deposited By:Professor Martin McGinnity
Deposited On:11 May 2012 15:36
Last Modified:11 May 2012 15:36

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