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

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

Biologically inspired neural network implementations on reconfigurable hardware

For a considerable period of time, the goal of the computational intelligence research community has been the creation of an artificial system with the ability to leam for itself in a manner that replicates to some degree, the natural intelligence of the human brain. The development of the integrated circuit (IC), and the accompanying genesis of the modem day computer in the 1960s, was perhaps seen as a significant advancement towards this objective. However, whilst technology has progressed at an extraordinary rate, the fundamental issue is that it is very difficult to develop truly intelligent systems, irrespective of the vast number of computations that can be performed per second.EThOS - Electronic Theses Online ServiceGBUnited Kingdo