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

A Novel Frequency Based Current-to-Digital Converter with Programmable Dynamic Range

This work describes a novel frequency based Current to Digital converter, which would be fully realizable on a single chip. Biological systems make use of delay line techniques to compute many things critical to the life of an animal. Seeking to build up such a system, we are adapting the auditory localization circuit found in barn owls to detect and compute the magnitude of an input current. The increasing drive to produce ultra low-power circuits necessitates the use of very small currents. Frequently these currents need to accurately measured, but current solutions typically involve off-chip measurements. These are usually slow, and moving a current off chip increases noise to the system. Moving a system such as this completely on chip will allow for precise measurement and control of bias currents, and it will allow for better compensation of some common transistor mismatch issues. This project affords an extremely low power (100s nW) converter technology that is also very space efficient. The converter is completely asynchronous which yields ultra-low power standby operation [1]