Abstract
The optical implementation of modern neural network learning models such as competitive learning networks, resonance correlation networks, and back propagation networks requires a wider range of neuron behavior than a simple saturating threshold non-linearity. However, optical implementation of neurons that incorporate non-local, non-linear functions such as shunting inhibition, winner-take-all, and history-dependent behavior is beyond the capability of conventional optical devices. A new class of light modulator has been developed that combines the flexibility of analog and digital electronic VLSI circuits, optical detectors, and the switchable electo-optic capabilities of liquid crystal materials. In this paper we will show how these liquid crystal/VLSI modulators can be used in optical implementations of these learning networks.1 We will discuss in detail a competitive optical learning network that uses LC/VLSI winner-take-all neurons on fractal grids to program adaptive volume holographic interconnections.2
© 1992 Optical Society of America
PDF ArticleMore Like This
Kelvin Wagner and Tim Slagle
WA5 Optical Computing (IP) 1991
Timothy M. Slagle and Kelvin Wagner
SMD.3 Spatial Light Modulators and Applications (SLM) 1993
Timothy M. Slagle and Kelvin Wagner
ThT3 OSA Annual Meeting (FIO) 1991