A biologically inspired spiking neural network model of the auditory midbrain for sound source localisation

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 ðyÞ, 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 ðo1 kHzÞ 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 PðyjITDÞ or PðyjILDÞ 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.

@Article{LPREW10, 
 	 author =  {Liu, Jindong and Perez-Gonzales, David and Rees, Adrian and Erwin, Harry and Wermter, Stefan},  
 	 title = {A biologically inspired spiking neural network model of the auditory midbrain for sound source localisation}, 
 	 journal = {Neurocomputing},
 	 number = {1--3},
 	 volume = {74},
 	 pages = {129--139},
 	 year = {2010},
 	 month = {Dec},
 	 publisher = {Elsevier},
 	 doi = {10.1016/j.neucom.2009.10.030}, 
 }