Collective recall via the brain-state-in-a-box network

A number of approaches to pattern recognition employ variants of nearest neighbor recall. This procedure uses a number of prototypes of known class and identifies an unknown pattern vector according to the prototype it is nearest to. A recall criterion of this type that depends on the relation of the unknown to a single prototype is a non-smooth function and leads to a decision boundary that is a jagged, piecewise linear hypersurface. Collective recall, a pattern recognition method based on a smooth nearness measure of the unknown to all the prototypes, is developed. The prototypes are represented as cells in a brain-state-in-a-box (BSB) network. Cells that represent the same pattern class are linked by positive weights and cells representing different pattern classes are linked by negative weights. Computer simulations of collective recall used in conjunction with learning vector quantization (LVQ) show significant improvement in performance relative to nearest neighbor recall for pattern classes defined by nonspherically symmetric Gaussians