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

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

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
LanguageEnglish
Pages574-586
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume23
Issue number4
DOIs
Publication statusPublished - Apr 2012

Fingerprint

Acoustic waves
Neural networks
Supervised learning
Network architecture
Acoustic noise
Learning algorithms
Neurons
Transfer functions
Fires

Cite this

@article{c78ddfa54604414ebef504a47b56fa99,
title = "Spiking Neural Network Model of Sound Localisation using the interaural intensity Difference",
abstract = "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",
author = "Julie Wall and Liam McDaid and Liam Maguire and TM McGinnity",
year = "2012",
month = "4",
doi = "10.1109/TNNLS.2011.2178317",
language = "English",
volume = "23",
pages = "574--586",
journal = "IEEE Transactions on Neural Networks and Learning Systems",
issn = "2162-237X",
number = "4",

}

TY - JOUR

T1 - Spiking Neural Network Model of Sound Localisation using the interaural intensity Difference

AU - Wall, Julie

AU - McDaid, Liam

AU - Maguire, Liam

AU - McGinnity, TM

PY - 2012/4

Y1 - 2012/4

N2 - 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

AB - 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

U2 - 10.1109/TNNLS.2011.2178317

DO - 10.1109/TNNLS.2011.2178317

M3 - Article

VL - 23

SP - 574

EP - 586

JO - IEEE Transactions on Neural Networks and Learning Systems

T2 - IEEE Transactions on Neural Networks and Learning Systems

JF - IEEE Transactions on Neural Networks and Learning Systems

SN - 2162-237X

IS - 4

ER -