Learning and Generalization in Layered Neural Networks

Layered neural networks are of interest as a tool to implement input-output mappings. This work explores the ability of a highly connected, layered network of simple processing units to perform such task. The functional capabilities of a fixed network architecture are investigated by exploring the configuration space of all possible couplings among the processing units, and determining the associated probability distribution over the space of realizable mappings.