Multilayer optical learning networks

A new approach to learning in a multilayer optical neural network based on holographically interconnected nonlinear devices is presented. The proposed network can learn the interconnections that form a distributed representation of a desired pattern transformation operation. The interconnections are formed in an adaptive and self-aligning fashioias volume holographic gratings in photorefractive crystals. Parallel arrays of globally space-integrated inner products diffracted by the interconnecting hologram illuminate arrays of nonlinear Fabry-Perot etalons for fast thresholding of the transformed patterns. A phase conjugated reference wave interferes with a backward propagating error signal to form holographic interference patterns which are time integrated in the volume of a photorefractive crystal to modify slowly and learn the appropriate self-aligning interconnections. This multilayer system performs an approximate implementation of the backpropagation learning procedure in a massively parallel high-speed nonlinear optical network

Optical computing: introduction by the guest editors to the feature in the 1 May 1988 issue

The feature in the 1 May 1988 issue of Applied Optics includes a collection of papers originally presented at the 1987 Lake Tahoe Topical Meeting on Optical Computing. These papers emphasize digital optical computing systems, optical interconnects, and devices for optical computing, but analog optical processing is considered as well

An exploration of synchronization solutions for parallel short-range optical interconnect in mesochronous systems

As a result of the increasing complexity of electronic chips, the bandwidths required for inter- and intra-chip communication are rapidly increasing. As optoelectronics provides high=bandwidth and high-density interconnection it is considered as a candidate for short-range interconnection. For such interconnections, situated at a low level in the systems hierarchy, the interconnect latency is extremely critical for the systems performance. This paper describes some methods for mesochronous synchronization, needed for such interconnections. It will be shown that it can be beneficial to use an additional optical link to transfer a synchronization signal. Such a reference signal can be used efficiently for phase detection, provided that the data skew is sufficiently small, and result in a decrease of the cost-per-link

Optical neural networks: an introduction to a special issue by the feature editors

This feature of Applied Optics is devoted to papers on the optical implementation of neural-network models of computation. Papers are included on optoelectronic neuron array devices, optical interconnection techniques using holograms and spatial light modulators, optical associative memories, demonstrations of optoelectronic systems for learning, classification, and target recognition, and on the demonstration, analysis, and simulation of adaptive interconnections for optical neural networks using photorefractive volume holograms

Adaptive optical networks using photorefractive crystals

The capabilities of photorefractive crystals as media for holographic interconnections in neural networks are examined. Limitations on the density of interconnections and the number of holographic associations which can be stored in photorefractive crystals are derived. Optical architectures for implementing various neural schemes are described. Experimental results are presented for one of these architectures