A spiking neural network model of the medial superior olive using spike timing dependent plasticity for sound localization
- Intelligent Systems Research Centre, Magee Campus, University of Ulster, Derry, Northern Ireland, UK
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° is used. For angular resolutions down to 2.5°, 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.
sound localisation, MSO, SNN, STDP
Glackin B, Wall JA, McGinnity TM, Maguire LP and McDaid LJ (2010) A spiking neural network model of the medial superior olive using spike timing dependent plasticity for sound localization. Front. Comput. Neurosci. 4:18. doi: 10.3389/fncom.2010.00018
Received: 22 February 2010;
Paper pending published: 20 March 2010;
Accepted: 04 June 2010;
Published online: 03 August 2010
Henry Markram, Ecole Polytechnique Federale de Lausanne, Switzerland
Silvia Scarpetta, University of Salerno, Italy
Claudia Clopath, Ecole Polytechnique Federale de Lausanne, Switzerland
© 2010 Glackin, Wall, McGinnity, Maguire and McDaid. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
Thomas M. McGinnity, Intelligent Systems Research Centre, Magee Campus, University of Ulster, Derry, Northern Ireland BT48 7JL, UK. e-mail: email@example.com