Energy and the Behavior of Connectionist Models.

Abstract

Massively parallel (connectionist) computational models are playing an increasingly important role in cognitive science. Establishing the behavioral correctness of a connectionist model is exceedingly difficult, as it is with any complex system. For a restricted class of models, one can define an analog to the energy function of physics and this can be used to help prove properties of a network. This paper explores energy and other techniques for establishing that a network meets its specifications. The treatment is elementary, computational, and focuses on specific examples. No free lunch is offered.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1985
Accession Number
ADA170324

Entities

People

  • Jerome A. Feldman

Organizations

  • University of Rochester

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Bayesian Networks
  • Cognitive Science
  • Computational Science
  • Computer Languages
  • Computer Science
  • Computer Vision
  • Computers
  • Information Processing
  • Information Science
  • Information Systems
  • Neural Networks
  • Parallel Computing
  • Reasoning
  • Standards

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design