17 research outputs found
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 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
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
Irish cardiac society - Proceedings of annual general meeting held 20th & 21st November 1992 in Dublin Castle
Modelling Architectures for VLSI Implementations of Fuzzy Logic Systems
Introduction A simple model for predicting the silicon area occupied by a fuzzy logic system when implemented as VLSI integrated circuits is presented. The approach considers only a zeroorder Sugeno fuzzy model [Sugeno88]. The model enables the early identification of corelimited or interconnect-limited architectures for a given technology, utilizing design rule considerations, and gives the designer the opportunity of evaluating competing architectures for realistic, practical implementations. 2. Fuzzy reasoning Fuzzy inference systems have been successfully applied in a diverse range of areas. As a result of this multi-disciplinary approach there has evolved a range of different approaches and nomenclatures [Jang95, Lee90]. The interpretation of fuzzy reasoning used in this work is commonly referred to as a zero-order Sugeno fuzzy model [Sugeno88, Takagi85]. In this model the consequent of each fuzzy IF THEN rule is represented by a fuzzy s
A compact spike-timing-dependent-plasticity circuit for floating gate weight implementation
The power of genetic diversity in genome-wide association studies of lipids (vol 600, pg 675, 2021)
Metabolic health: pathophysiological trajectories and therap