# Show Reference: "A biologically inspired spiking neural network model of the auditory midbrain for sound source localisation"

A biologically inspired spiking neural network model of the auditory midbrain for sound source localisation Neurocomputing, Vol. 74, No. 1-3. (22 December 2010), pp. 129-139, doi:10.1016/j.neucom.2009.10.030 by Jindong Liu, David Perez-Gonzalez, Adrian Rees, Harry Erwin, Stefan Wermter
@article{liu-et-al-2010,
abstract = {This paper proposes a spiking neural network ({SNN}) of the mammalian subcortical auditory pathway to achieve binaural sound source localisation. The network is inspired by neurophysiological studies on the organisation of binaural processing in the medial superior olive ({MSO}), lateral superior olive ({LSO}) and the inferior colliculus ({IC}) to achieve a sharp azimuthal localisation of a sound source over a wide frequency range. Three groups of artificial neurons are constructed to represent the neurons in the {MSO}, {LSO} and {IC} that are sensitive to interaural time difference ({ITD}), interaural level difference ({ILD}) and azimuth angle (θ), respectively. The neurons in each group are tonotopically arranged to take into account the frequency organisation of the auditory pathway. To reflect the biological organisation, only {ITD} information extracted by the {MSO} is used for localisation of low frequency sounds; for sound frequencies between 1 and 4 {kHz} the model also uses {ILD} information extracted by the {LSO}. This information is combined in the {IC} model where we assume that the strengths of the inputs from the {MSO} and {LSO} are proportional to the conditional probability of {P(θ|ITD}) or {P(θ|ILD}) calculated based on the Bayes theorem. The experimental results show that the addition of {ILD} information significantly increases sound localisation performance at frequencies above 1 {kHz}. Our model can be used to test different paradigms for sound localisation in the mammalian brain, and demonstrates a potential practical application of sound localisation for robots.},
author = {Liu, Jindong and Perez-Gonzalez, David and Rees, Adrian and Erwin, Harry and Wermter, Stefan},
day = {22},
doi = {10.1016/j.neucom.2009.10.030},
issn = {09252312},
journal = {Neurocomputing},
keywords = {ann, auditory, ic, localization, probability, sc-input, spiking},
month = dec,
number = {1-3},
pages = {129--139},
posted-at = {2012-02-20 14:28:13},
priority = {2},
publisher = {Elsevier},
title = {A biologically inspired spiking neural network model of the auditory midbrain for sound source localisation},
url = {http://dx.doi.org/10.1016/j.neucom.2009.10.030},
volume = {74},
year = {2010}
}


Multiple cues are used in biological sound-source localization.

The difference in intensity between one ear and the other, the interaural level difference (ILD), is one cue used in biological sound-source localization.

The difference phase between one ear and the other, the interaural time difference (ITD), is one cue used in biological sound-source localization.

In mammals, different neurons in the lateral superior olive (LSO) are tuned to different ILDs.

In mammals, different neurons in the medial superior olive (MSO) are tuned to different ITDs.

Subregions of the superior olivary complex (SOC) extract auditory localization cues.

The model of biological computation of ITDs proposed by Jeffress extracts ITDs by means of delay lines and coincidence detecting neurons:

The peaks of the sound pressure at each ear lead, via a semi-mechanical process, to peaks in the activity of certain auditory nerve fibers. Those fibers connect to coincidence-detecting neurons. Different delays in connections from the two ears lead to coincidence for different ITDs, thus making these coincidence-detecting neurons selective for different angles to the sound source.

ITD and ILD are most useful for auditory localization in different frequency ranges:

• In the low frequency ranges, ITD is most informative for auditory localization.
• In the high frequency ranges, ILD is most informative for auditory localization.

The granularity of representations of ITDs and ILDs in MSO and LSO reflects the fact that ITD and ILD are most useful for auditory localization in different frequency ranges: ITDs for high frequencies are less densely represented in MSO and ITDS are less densely represented in LSO.

Liu et al. model the LSO and MSO as well as the integrating inferior colliculus.

Their system can localize sounds with a spatial resolution of 30 degrees.

Liu et al.'s model of the IC includes a Jeffress-type model of the MSO.