Accession Number : ADP008668

Title :   Competitive Optical Learning with Winner-Take-All Modulators,

Corporate Author : COLORADO UNIV AT BOULDER

Personal Author(s) : Wagner, Kelvin ; Slagle, Tim

Report Date : 22 MAY 1992

Pagination or Media Count : 4

Abstract : Modern neural network learning models such as competitive learning networks, resonance correlation networks, and back propagation networks require 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 electro-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. We discuss in detail a competitive optical learning network which uses LC/VLSI winner-take-all neurons on fractal grids to program adaptive volume holographic interconnections. We will present results from tests of the LC/VLSI winner-take-all modulator arrays, and in addition will show preliminary results from an optical competitive learning system that uses the LC/VLSI modulators as neurons.

Descriptors :   *NEURAL NETS, *LEARNING, *OPTICAL PROCESSING, *LIGHT MODULATORS, VERY LARGE SCALE INTEGRATION, LIQUID CRYSTALS, ELECTROOPTICS.

Subject Categories : Electrooptical and Optoelectronic Devices
      Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE