Adaptation and Self-Repair in Parallel Computing Structures

Abstract

This paper reports a study of the dynamics of highly concurrent computing structures capable of learning and storing several input patterns. The existence of attractors in their dynamical behavior leads to a novel self-repairing mechanism. Quantitative experiments on adaptive processor arrays demonstrate the existence of attractive fixed points in their adaptation and recognition properties, as well as the immunity of these collective computation machines to random errors.