Theoretical Investigation of Optical Computing Based on Neural Network Models

Abstract

The optical implementation of weighted interconnections is investigated and basic relationship are derived between the number of neurons, the number of connections and methods for selecting the positions of the neurons to achieve the maximum density of independent connections are presented. The connectivity of a neural network (number of synapses per neuron) is related to the complexity of the problems it can handle. For a network that learns a problem from examples using a local learning rule, it is proved that the entropy of the problem becomes a lower bound for the connectivity of the network.

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Document Details

Document Type
Technical Report
Publication Date
Nov 17, 1988
Accession Number
ADA203078

Entities

People

  • David Brady
  • Demetri Psaltis
  • Xiang-guang Gu
  • Yaser S. Abu-mostafa

Organizations

  • California Institute of Technology

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Computing System Architectures
  • Correlators
  • Electrical Engineering
  • Holograms
  • Information Theory
  • Learning
  • Neural Networks
  • Optical Correlators
  • Optical Interconnects
  • Plane Waves
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistics
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Fluid Dynamics.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks