Top-Down Teaching Enables Non-Trivial Clustering via Competitive Learning

Unsupervised competitive learning classifies patterns based on similarity of their input representations. As it is not given external guidance, it has no means of incorporating taskspecific information useful for classifying based on semantic similarity. This report describes a method of augmenting the basic competitive learning algorithm with a top-down teaching signal. This teaching signal removes the restriction inherent in unsupervised learning and allows high level structuring of the representation while maintaining the speed and biological plausibility of a local Hebbian style learning algorithm. Examples, using this algorithm in small problems, are presented and the function of the teaching input is illustrated geometrically. This work supports the hypothesis that cortical back-projections are important for the organization of sensory traces during learning. This research was supported by a. grant from the Huma.n Frontier Science Program and by a Canadien NSERC 1967 Science and Engineering Scholarship.