A spiking neural network model of the medial superior olive using spike timing dependent plasticity for sound localization

Brendan Glackin, Julie Wall, TM McGinnity, LP Maguire, Liam McDaid

Research output: Contribution to journalArticle

17 Citations (Scopus)

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 InterauralTime 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 SpikeTiming Dependent Plasticity learning rule using experimentally observed Head RelatedTransfer 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.
LanguageEnglish
Pages1-16
JournalFrontiers in computational Neuroscience
Volume4
Issue number18
DOIs
Publication statusPublished - 3 Aug 2010

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Plasticity
Acoustic waves
Neural networks
Field programmable gate arrays (FPGA)
Brain
Hardware
Processing

Cite this

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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 InterauralTime 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 SpikeTiming Dependent Plasticity learning rule using experimentally observed Head RelatedTransfer 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.",
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