<title>Neural Analog Information Processing</title>

Abstract

Neural Analog Information Processing (NAIP) is an effort to develop general purpose pattern classification architectures based upon biological information processing principles. This paper gives an overview of NAIP and its relationship to the previous work in neural modeling from which its fundamental principles are derived. It also presents a theorem concerning the stability of response of a slab (a two dimensional array of identical simple processing units) to time-invariant (spatial) patterns. An experiment (via computer emulation) demonstrating classification of a spatial pattern by a simple, but complete NAIP architecture is described. A concept for hardware implementation of NAIP architectures is briefly discussed.