Doctoral Dissertations
Date of Award
12-2024
Degree Type
Dissertation
Degree Name
Doctor of Philosophy
Major
Computer Science
Major Professor
James S. Plank
Committee Members
Catherine D. Schuman, Garrett S. Rose, Xiaopeng Zhao
Abstract
Spiking Neural Networks are a promising field with potential for performing machine learning tasks using hardware with very low SWaP costs. These systems have an inherently temporal nature using event-based information transfer with a dynamic state, making Spiking Neural Networks well suited for dynamic, temporal tasks. However, training a spiking neural network to take advantage of the capabilities of such a system is challenging. Existing Evolutionary Optimization algorithms work well to solve small problems very efficiently but struggle to learn larger problems. This work begins by introducing a network compilation process to assemble these small networks into a more complex whole to potentially operate for more complex problems. Further, this work introduces a means of reducing the size of assembled networks through a process called time multiplexing. Finally a modified version of the EONS algorithm is introduced to operate on problems with larger input spaces by creating more densely-connected networks.
Recommended Citation
Skuda, Nicholas Dane, "Topology Modification and Training for Spiking Neural Networks. " PhD diss., University of Tennessee, 2024.
https://trace.tennessee.edu/utk_graddiss/11327
