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.

Effects of Prochlorperazine on Normal Vestibular Ocular and Perceptual Responses: A Randomised, Double-Blind, Crossover, Placebo-Controlled Study

Background: The present study investigated whether prochlorperazine affects vestibular-ocular reflex (VOR) and vestibulo-perceptual function. Methods: We studied 12 healthy naïve subjects 3 hours after a single dose of oral prochlorperazine 5mg in a randomised, placebo-controlled, double-blind, cross-over study in healthy young subjects. Two rotational tests in yaw were used: 1) a Threshold task investigating perceptual motion detection and nystagmic thresholds (acceleration steps of 0.5deg/s/s) and 2) Suprathreshold responses to velocity steps of 90deg/s in which vestibulo-ocular (VO) and vestibulo-perceptual (VP) time constants of decay, as well as VOR gain, were measured. Results: Prochlorperazine had no effect upon any measure of nystagmic or perceptual vestibular function compared to placebo. This lack of effects on vestibular-mediated motion perception suggests that the drug is likely to act more as an antiemetic than as an anti-vertiginous agent.