ABSTRACT Title of Dissertation: One-Shot Multi-Winner Self-Organizing Maps

There exist two different approaches to self-organizing maps (SOMs). One ap-proach, rooted in theoretical neuroscience, uses SOMs as computational models of biological cortex. The other approach, taken in computer science and engineering, views SOMs as tools suitable to perform, for example, data visualization and pattern classification tasks. While the first approach emphasizes fidelity to neurobiological data, the latter stresses computational efficiency and effectiveness. In the research reported here, I developed and studied a class of SOMs that incorporates the multiple, simultaneous winner nodes implicit in many biologically-oriented SOMs, but determines the winners using the same efficient one-shot algo-rithm employed by computationally-oriented, single-winner SOMs. This was achieved by generalizing single-winner SOMs, using localized competitions. The resulting one-shot multi-winner SOM was found to support the formation of multiple adjacent, mirror-symmetric topographic maps. It constitutes the first computational model of mirror-image map formation, and raises questions about the role of Hebbian-type synaptic changes in the formation of mirror-symmetric maps that are often observe