<title>Neural Analog Processing</title>

Abstract

This paper presents a bionic approach to pattern classification entitled Neural Analog Processing (NAP). NAP systems are based upon information processing principles discovered by neural modelers, but are not themselves neural models. To set the stage for a discussion of how NAP systems work, the theory of a particular type of local-in-time template-matching classifier -- the Generalized Nearest Neighbor (GNN) classifier -- for general time-varying patterns (imagery, spectra, tactile signals, etc.) is reviewed. The definition and function of the fundamental NAP structure -- the slab -- is then presented and it is shown that a GNN classifier can, in principle, be implemented using slabs. The embellishments necessary to allow NAP systems to be realized in hardware are then described. Finally, a summary of NAP system characteristics is presented.