A new architecture for optical implementation of large-scale neural networks is proposed. This architecture is based on a time-division-multiplexing technique, in which both the neuron state vector and the interconnection matrix are divided in the time domain. Computer simulation and experimental results for associative memories show the effectiveness in implementing large-scale networks.
© 1990 Optical Society of America
Original Manuscript: September 22, 1989
Manuscript Accepted: December 18, 1989
Published: February 15, 1990
Masaya Oita, Jun Ohta, Shuichi Tai, and Kazuo Kyuma, "Optical implementation of large-scale neural networks using a time-division-multiplexing technique," Opt. Lett. 15, 227-229 (1990)