Neural Network: Input Anticipation May Lead to Advanced Adaptation Properties

  • Conference paper
  • First Online:
Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003 (ICANN 2003, ICONIP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2714))

  • 1255 Accesses

Abstract

Network architecture is proposed, which is built according to principle of input anticipation. The network constantly anticipates the incoming input, compares the anticipation with the real input data and modifies its internal structure to ensure better anticipation in the future. It is argued that such network may exhibit advanced adaptation properties.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Maturana, H.R., Varela, F.J.: Autopoiesis and Cognition: The Realization of the Living. Reidel, Dordrecht (1980)

    Google Scholar 

  2. Yemelianov-Yaroslavsky, L.B., Intellectual quasi-biological system. Inductive automaton. Nauka, Moscow (1990) (in Russian). English version at http://www.aha.ru/~pvad/concept.htm.

  3. Anokhin, P.K.: The Functional System Theory as a Basis for Development of Physiological Cybernetics. In: Kuzin, A.M. et al. (eds.): Biological Aspects of Cybernetics. USSR Academy of Sciences Publishing House, Moscow (1962) 74–91 (in Russian)

    Google Scholar 

  4. Anokhin, P.K.: Functional System. In: Annual of the Great Medical Encyclopedia, Vol. 1. (1968) 1300–1322 (in Russian)

    Google Scholar 

  5. Neisser, U.: Cognition and Reality. Principles and Implications of Cognitive Psychology. W.H. Freeman and Company, San Francisco (1976)

    Google Scholar 

  6. Vitiayev, E.E.: Goal-making as Brain Working Principle. In: Models of Cognitive Processes, Proc. of Institute for Mathematics, Siberian Department, Russia Academy of Sciences, Novosibirsk (1997) 9–52 (in Russian).

    Google Scholar 

  7. Wickelgren, W.A.: Webs, Cell Assemblies, and Chunking in Neural Nets. In: Canadian Journal of Experimental psychology. Vol. 53.1(1999) 118–131

    Google Scholar 

  8. Amosov, N.M. et. al.: Automata and Intelligent Behavior. Naukova Dumka, Kiev (1973) (in Russian)

    Google Scholar 

  9. Amosov, N.M., Goltzev, A.D., Kussul E.M.: Functional Organization of Brain Processes and Their Concern with Neural Network Structures, Kibernetika 5 (1988) 113–119 (in Russian)

    Google Scholar 

  10. Palm, G.: Neural Assemblies. Studies of Brain Function. Vol VII. Springer, Berlin Heidelberg New York (1982).

    Google Scholar 

  11. Braitenberg, V.: Cell assemblies in the cerebral cortex. In: Heim, R. and Palm, G. (eds.): Theoretical Approaches to Complex Systems–Lecture Notes in Biomathematics, Vol. 21. Springer Verlag, Berlin (1978) 171–188.

    Google Scholar 

  12. Lakoff, G., Johnson, M.: Metaphors We Live By. University of Chicago Press, Chigaco (1980).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kursin, A. (2003). Neural Network: Input Anticipation May Lead to Advanced Adaptation Properties. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_93

Download citation

  • DOI: https://doi.org/10.1007/3-540-44989-2_93

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40408-8

  • Online ISBN: 978-3-540-44989-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Navigation