Skip to main content

Dynamic feature linking in stochastic networks with short range interactions

  • Oral Presentations: Theory Theory V: Colective Dynamics
  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

Included in the following conference series:

  • 224 Accesses

Abstract

It is well established that cortical neurons display synchronous firing for some stimuli and not for others. The resulting synchronous subpopulation of neurons is thought to form the basis of object perception. In this paper this ’dynamic linking’ phenomenon is demonstrated in networks of binary neurons with stochastic dynamics. Feed-forward connections implement feature detectors and lateral connections implement memory traces or cell assemblies.

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

Access this chapter

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. C.M. Gray, P. König, A.K. Engel, and W. Singer. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature, 338:334, 1989.

    Google Scholar 

  2. R. Eckhorn, R. Bauer, W. Jordan, M. Brosch, W. Kruse, M. Munk, and H.J. Reitboeck. Coherent oscillations: A mechanism of feature linking in the visual cortex? Biological Cybernetics, 60:121–130, 1988.

    Google Scholar 

  3. A.K. Engel, P. König, and W. Singer. Direct physiological evidence for scene segmentation by temporal coding. Proceedings of the National Academy of Science of the USA, 88:9136–9140, 1991.

    Google Scholar 

  4. B Julesz. Foundations of cyclopean perception. University of Chicago Press, Illinois, 1971.

    Google Scholar 

  5. D Marr. Vision: A computational investigation into the human representation and procesing of visual information. Freeman, San Francisco, 1982.

    Google Scholar 

  6. P. König and T.B. Schillen. Stimulus-dependent assembly formation of oscillatory responses: I synchronization. Neural Computation, 3:155–166, 1991.

    Google Scholar 

  7. H. Sompolinsky, D. Golomb, and D. Kleinfeld. Cooperative dynamics in visual processing. Physical Review A, 43:6990–7011, 1991.

    Google Scholar 

  8. Ch. Malsburg and W. Schneider. A neural cocktail-party processor. Biological Cybernetics, 54:29–40, 1986.

    Google Scholar 

  9. T. Chawanya, T. Aoyagi, I. Nishikawa, K. Okuda, and Y. Kuramoto. A model for feature linking via collective oscillations in the primary visual cortex. Biological Cybernetics, 68:483–490, 1993.

    Google Scholar 

  10. M. Arndt, P. Dicke, M. Erb, R. Eckhorn, and H.J. Reitboeck. Two-layered physiology-orineted neuronal network models the combine dynamic feature linking via synchronization with a classical associative memory. In J.G. Taylor, editor, Neural Network Dynamics, pages 140–155. Springer Verlag, 1992.

    Google Scholar 

  11. D. Ackley, G. Hinton, and T. Sejnowski. A learning algorithm for Boltzmann machines. Cognitive Science, 9:147–169, 1985.

    Google Scholar 

  12. H.J. Kappen and M.J. Nijman. Dynamic linking in stochastic networks. In R. Moreno-Diaz, editor, Proceedings W.S. McCullock: 25 years in memoriam, pages 294–299, Las Palmas de Gran Canaria, Spain, 1995. MIT Press. F-95-043.

    Google Scholar 

  13. I Ginzburg and H Sompolinsky. Theory of correlations in stochastic neural networks. Physical Review E, 50:3171–3191, 1994.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kappen, B., Varona, P. (1996). Dynamic feature linking in stochastic networks with short range interactions. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_21

Download citation

  • DOI: https://doi.org/10.1007/3-540-61510-5_21

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics