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Application Of Hierarchical Networks In Analog Pattern Categorization

A neural network architecture is presented in which the strengths of interconnection pathways in one part of the network are modulated by activities in another part of the network. The network is arranged in a series of stages forming a hierarchy. The lower level stages produce output codes that are abstractions of the desired categorization task. Multiple levels of abstraction combine to provide categorization and generalization capabilities that appear to exceed those of standard backpropagation. Empirical results on two-dimensional continuous-valued (analog) simulated feature data are presented.