Optical Computing Based on Neuronal Models.
Final rept. 16 Feb 86-15 Feb 87,
MOORE SCHOOL OF ELECTRICAL ENGINEERING PHILADELPHIA PA ELECTRO-OPTICS AND MIC ROWAVE-OPTICS LAB
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Ever since the fit between what neural net models can offer collective, iterative, nonlinear, robust, and fault-tolerant approach to information processing and the inherent capabilities of optics parallelism and massive interconnectivity was first pointed out and the first optical associative memory demonstrated in 1985, work and interest in neuromorphic optical signal processing has been growing steadily. For example, work in optical associative memories is currently being conducted at several academic institutions e.g., California Institute of Technology, University of Colorado, University of California-San Diego, Stanford University, University of Rochester, and the authors own institution the University of Pennsylvania and at several industrial and governmental laboratories e.g., Hughes Research Laboratories - Malibu, the Naval Research Laboratory, and the Jet Propulsion Laboratory. In these efforts, in addition to the vector matrix multiplication with thresholding and feedback scheme utilized in early implementations, an arsenal of sophisticated optical tools such as holographic storage, phase conjugate optics, and wavefront modulation and mixing are being drawn upon to realize associative memory functions.