Content-Addressable Memory Storage by Neural Networks: A General Model and Global Liapunov Method,

Abstract

Many neural network models capable of content-addressable memory are shown to be special cases of the general model and global Liapunov function. These include examples of the additive, brain-state-in-a-box, McCulloch-Pitts, Boltzmann machine, shunting, masking field, bidirectional associative memory, Volterra-Lotka, Gilpin-Ayala, and Eigen-Schuster models. The Cohen-Grossberg model thus defines a general principle for the design of content addressable memory, that is shared by all model exemplars of such a general design constitutes a computational invariant. Such a general model and analytic method defines a computational framework within which specialized model exemplars may be compared to discover which models are best able to explain particular parametric data about brain and behavior, or to solve particular technological problems.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1988
Accession Number
ADA192716

Entities

People

  • Stephen Grossberg

Organizations

  • Boston University

Tags

DTIC Thesaurus Topics

  • Applied Mathematics
  • Artificial Intelligence
  • Biological Sciences
  • Brain
  • Cognitive Science
  • Computational Neuroscience
  • Computational Science
  • Computers
  • Content Addressable Memory
  • Mathematical Analysis
  • Mathematical Models
  • Nervous System
  • Neural Networks
  • Neurons
  • Neurosciences
  • Psychology
  • Self Organizing Systems

Readers

  • Calculus or Mathematical Analysis
  • Neural Network Machine Learning.
  • Parallel and Distributed Computing.

Technology Areas

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