Abstract

In symbolic processing, associative network approaches show promise for solving difficult artificial intelligence problems. [1,2] Optical associative networks, including holographic[3,4] and matrix-vector multiplication [5] architectures, are one of the most attractive approaches toward large-scale associative processing. Optics provides both 2-D parallel interconnection ability between modules and parallel-computing mechanisms for parallel association algorithm. A hybrid optical inference architecture has been proposed. [6] Recently optical architectures for learning and self-organizing neural network are discussed.[7,8]

© 1989 Optical Society of America