Paper Info
Title
A spiking neural network model of the medial superior olive using spike timing dependent plasticity for sound localization
Abstract
Sound localization can be defined as the ability to identify the position of an input sound source and is considered a powerful aspect of mammalian perception. For low frequency sounds, i.e., in the range 270 Hz-1.5 KHz, the mammalian auditory pathway achieves this by extracting the Interaural Time Difference between sound signals being received by the left and right ear. This processing is performed in a region of the brain known as the Medial Superior Olive (MSO). This paper presents a Spiking Neural Network (SNN) based model of the MSO. The network model is trained using the Spike Timing Dependent Plasticity learning rule using experimentally observed Head Related Transfer Function data in an adult domestic cat. The results presented demonstrate how the proposed SNN model is able to perform sound localization with an accuracy of 91.82% when an error tolerance of +/-10 degrees is used. For angular resolutions down to 2.5 degrees, it will be demonstrated how software based simulations of the model incur significant computation times. The paper thus also addresses preliminary implementation on a Field Programmable Gate Array based hardware platform to accelerate system performance.
Year
DOI
Venue
2010
10.3389/fncom.2010.00018
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Keywords
Field
DocType
sound localisation,MSO,SNN,STDP
Neuroscience,Head-related transfer function,Computer science,Artificial intelligence,Spiking neural network,Interaural time difference,Field-programmable gate array,Speech recognition,Learning rule,Sound localization,Spike-timing-dependent plasticity,Network model,Machine learning
Journal
Volume
Citations 
PageRank 
4
10
0.63
References 
Authors
28
5
Name
Order
Citations
PageRank
Brendan P. Glackin121016.82
Julie A. Wall2163.63
Thomas Martin McGinnity344124.86
Liam P. Maguire451151.18
Liam Mcdaid527030.48