Advertisement

Principles of Associative Computation

  • Andrzej Wichert
  • Birgit Lonsinger-Miller
Conference paper

Abstract

Currently neural networks are used in many different domains. But are neural networks also suitable for modeling problem solving, a domain which is traditionally reserved for the symbolic approach? This central question of cognitive science is answered in this paper. It is affirmed by a corresponding neural network model. The model has the same behavior as a symbolic model. However, also additional properties resulting from the distributed representation emerge. It is shown by comparison of those additional abilities with the basic behavior of the model, that the additional properties lead to a significant algorithmic improvement. This is verified by statistical hypothesis testing.

Keywords

Short Term Memory Associative Memory Heuristic Function Frame Problem Statistical Hypothesis Testing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Andersoll, J. R. (1995). Cognitive Psyhology and its Implications. W. H. Freeman and Company, fourth edition.Google Scholar
  2. [2]
    Newell, A. (1990). Unified Theories of Cognition. Harvard University Press.Google Scholar
  3. [3]
    Newell, A. Simon, H. (1972). Human Problem Solving. Prentice-Hall.Google Scholar
  4. [4]
    Steinbuch, K. (1961). Die Lernmatrix. Kybernetik, 1:36–45.CrossRefMATHGoogle Scholar
  5. [5]
    Steinbuch, K. (1971). Automat und Mensch. Springer-Verlag, fourth edition.Google Scholar

Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Andrzej Wichert
  • Birgit Lonsinger-Miller
    • 1
  1. 1.Department of Neural Information ProcessingUniversity of UlmUlmGermany

Personalised recommendations