A Spiking Neural Network Model of the Medial Superior Olive using Spike Timing Dependent Plasticity for Sound Localisation

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 degrees is used. For angular resolutions down to 2.5 degrees , 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.

Spiking neural network model of sound localisation using the interaural intensity difference

In this paper, a spiking neural network (SNN) architecture to simulate the sound localization ability of the mammalian auditory pathways using the interaural intensity difference cue is presented. The lateral superior olive 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. The SNN uses leaky integrateand-fire excitatory and inhibitory spiking neurons, facilitating synapses and receptive fields. Experimentally derived headrelated transfer function (HRTF) acoustical data from adult domestic cats were employed to train and validate the localization ability of the architecture, training used the supervised learning algorithm called the remote supervision method to determine the azimuthal angles. The experimental results demonstrate that the architecture performs best when it is localizing high-frequency sound data in agreement with the biology, and also shows a high degree of robustness when the HRTF acoustical data is corrupted by noise.

A spiking neural network implementation of sound localisation

The focus of this paper is the implementation of a spiking neural network to achieve sound localization; the model is based on the influential short paper by Jeffress in 1948. The SNN has a two-layer topology which can accommodate a limited number of angles in the azimuthal plane. The model accommodates multiple inter-neuron connections with associated delays, and a supervised STDP algorithm is applied to select the optimal pathway for sound localization. Also an analysis of previous relevant work in the area of auditory modelling supports this research.

Spiking neuron models of the medial and lateral superior olive for sound localisation

Sound localisation is defined as the ability to identify the position of a sound source. The brain employs two cues to achieve this functionality for the horizontal plane, interaural time difference (ITD) by means of neurons in the medial superior olive (MSO) and interaural intensity difference (IID) by neurons of the lateral superior olive (LSO), both located in the superior olivary complex of the auditory pathway. This paper presents spiking neuron architectures of the MSO and LSO. An implementation of the Jeffress model using spiking neurons is presented as a representation of the MSO, while a spiking neuron architecture showing how neurons of the medial nucleus of the trapezoid body interact with LSO neurons to determine the azimuthal angle is discussed. Experimental results to support this work are presented.

A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics

This paper outlines the development of a crosscorrelation algorithm and a spiking neural network (SNN) for sound localisation based on real sound recorded in a noisy and dynamic environment by a mobile robot. The SNN architecture aims to simulate the sound localisation ability of the mammalian auditory pathways by exploiting the binaural cue of interaural time difference (ITD). The medial superior olive was the inspiration for the SNN architecture which required the integration of an encoding layer which produced biologically realistic spike trains, a model of the bushy cells found in the cochlear nucleus and a supervised learning algorithm. The experimental results demonstrate that biologically inspired sound localisation achieved using a SNN can compare favourably to the more classical technique of cross-correlation.

Sustainability study of the application of geosynthetic clay liners in hostile and aggressive environments

This paper discusses the sustainable performance of geosynthetic clay liners (GCLs) which are popularly specified as “leachate retaining” or as “water proofing” membranes in the geo-environmental construction industry. Geosynthetic clay liners (GCLs) are composite matting comprising of bentonite clay with two covering geosynthetics. These are innovative labour saving construction material, developed over the last three decades. The paper outlines the variety of Geosynthetic Clay Liners (GCLs) can be classified essentially into two distinctly different forms viz; (a) air dry (< 8% m/c) with granular or powdered bentonite or (b) bentonite cake factory prehydrated to a moisture content (~40% m/c) beyond its shrinkage limit and vacuum extruded as a clay cake to enhance its sustainable performance. The dominant mineral in bentonite clay is the three-layered (2:1) clay mineral montmorillonite. High quality bentonites need to be used in the GCL manufacture. Sodium montmorillonite has the desired characteristic of high swelling capacity, high cation exchange capacity and the consequently very low hydraulic conductivity, providing the basis for the hydraulic sealing medium in GCLs. These encapsulate the active montmorillonite clay minerals which depend on the water and chemical balance between the sealing element and the surrounding geo environment. Quantitative mineralogical analyses and an assessment of the adsorbed cation regime, diffusion coefficients and clay leachate compatibility must necessarily be an integral part of the site appraisal to ensure acceptable long term sustainability and performance. Factors influencing the desired performance of bentonite in the GCLs placed in difficult construction and hostile chemical environments are discussed in this paper. Accordingly, the performance specifications for GCLs are identified and the appropriateness of enhancing the cation exchange capacity with polymer treatment and the need for factory prehydration of the untreated sodium bentonite is emphasised. The advantage of factory prehydrating the polymer treated bentonite to fluid content beyond its shrinkage limit and subsequently factory processing it to develop laminated clay is to develop a GCL that has enviable sealing characteristics with a greater resistance to geochemical attack and cracking. Since clay liners are buried in the ground as base liners, capping layer or as structural water proofing membrane, they can easily avoid strict quality and performance monitoring being “out of sight, out of mind!”. It is very necessary that barrier design for leachate containment must necessarily be in accordance with legislative requirement Assessment of long term hydraulic conductivities and clay-leachate compatibility assessment is deemed necessary. The derogatory factors affecting the sustainable performance of the bentonite in GCLs placed in difficult construction and hostile chemical environments are discussed. Sustainability concepts incorporated in waste management practice must aim to achieve 100% recycling and fully implement the handling of solid waste in developing countries with relatively lower labour costs.