Connectionism and an Inescapable Defect

Abstract

Linear connectionist models of neurocomputing show how input patterns may be recognized, stored, compared, and recalled for output in serial-parallel, quasi-Hebbian networks. This aids the design of hardware and software for better robotics, while offering useful insights to neuroscientists studying sensorimotor systems, but connectivity via quasi-Hebbian nodes and back-propagation layers alone cannot show us how vertebrate cerebellum, allocortex, and neocortex work.