Working memory networks for learning multiple groupings of temporally ordered events: applications to 3-D visual object recognition

Abstract

Working memory neural networks which encode the invariant temporal order of sequential events that may be presented at widely differing speeds, durations, and interstimulus intervals are characterized. Working memory, a kind of short-term memory, can be quickly erased by a distracting event, unlike long-term memory. The authors describe a working memory architecture for the storage of temporal order information across a series of item representations. This temporal order code is designed to enable all possible groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system. Such a competence is needed in neural architectures which self-organize learned codes. Using such a working memory, a self-organizing architecture for invariant 3D visual object recognition, based on the model of M. Siebert and A.M. Waxman (1990), is described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>