JACIII Vol.15 No.8 pp. 1123-1130
doi: 10.20965/jaciii.2011.p1123


Solving the Binding Problem with Separated Extraction of Information by Oscillatory Self-Organizing Maps

Ryota Miyata* and Koji Kurata**

*Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259-G5-17, Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8502, Japan

**Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara, Nakagami, Okinawa 903-0213, Japan

March 5, 2011
July 15, 2011
October 20, 2011
separated extraction of information, binding problem, synchronous firing hypothesis, oscillatory selforganizing map, selective Hebbian learning

One solution to the binding problem is the hypothesis that property binding should be represented by phaselocking among neuronal oscillatory firing. We introduce this synchronous firing hypothesis into the selforganizing maps, or SOMs. Here we propose oscillatory self-organizing maps. Using the computer simulation, we show that our model composed of the oscillatory SOMs can separate and extract information of the shapes and the colors from two simultaneous inputs, solving the binding problem.

Cite this article as:
Ryota Miyata and Koji Kurata, “Solving the Binding Problem with Separated Extraction of Information by Oscillatory Self-Organizing Maps,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.8, pp. 1123-1130, 2011.
Data files:
  1. [1] C. von der Malsburg, “The correlation theory of brain function,” Internal Report, pp. 1-26, 1981.
  2. [2] C. von der Malsburg, “Binding in models of perception and brain function,” Neurobiology, pp. 520-526, 1995.
  3. [3] R. Eckhorn, R. Bauer, W. Jordan, M. Brosch, W. Kruse, M. Munk, and H. J. Reiboeck, “Coherent oscillations: a mechanism of feature linking in the visual cortex?,” Biological Cybernetics, Vol.60, pp. 121-130, 1988.
  4. [4] C. M. Gray, P. Konig, A. K. Engel, and W. Singer, “Oscillatory responses in cat visual cortex exhibit intercolumnar synchronization which reflects global stimulus properties,” Nature, 388, pp. 334-337, 1989.
  5. [5] A. K. Engel, P. Konig, A. K. Kreiter, and W. Singer, “Interhemispheric synchronization of oscillatory neuronal responses in cat visual cortex,” Science, Vol.252, pp. 1177-1179, 1991.
  6. [6] A. K. Engel, P. Konig, A. K. Kreiter, and W. Singer, “Temporal coding in the visual cortex: new vistas on integration in the nervous system,” Trends Neuroscience, Vol.15, pp. 218-226, 1992.
  7. [7] W. Singer, “Time as coding space in neocortical processing,” in “Temporal coding in the brain,” Springer-Verlag, pp. 51-79, 1994.
  8. [8] T. Kohonen, “Self-Organized Formation of Topology Correct Feature Maps,” Biological Cybernetics, Vol.43, pp. 59-69, 1982.
  9. [9] T. Kohonen, “Self-Organizing Maps,” Springer-Verlag Berlin Heidelberg, NewYork, 1995.
  10. [10] D. O. Hebb, “The organization of behavior,” New York: Wiley & Sons, 1949.
  11. [11] J. Shirakura and K. Kurata, “Nonlinear Principal Component Analysis by Learning Nerve Fields United by Inhibitory Connections,” The Transactions of the IEICE, Vol.J84-D-II, No.3, pp. 549-558, 2001.
  12. [12] M. Mitsutake, K. Kida, K. Wada, and K. Kurata, “Separate Extraction of Two Kind of Information by Self-Organizing-overlapping-Map (in Japanese),” The Brain & Neural Networks Vol.6, No.4, pp. 196-202, 1999.
  13. [13] M. Watanabe, K. Nakanishi, and K. Aihara, “Solving the Binding Problem of the Brain with Bi-Directional Functional Connectivity,” Neural Networks, Vol.14, pp. 395-406, 2001.
  14. [14] A. R. Damasio, “The brain binds entities and events by multiregional activation from convergence zones,” Neural Computation, Vol.1, pp. 123-132, 1989.
  15. [15] F. C. Hoppensteadt and J. P. Keener, “Phase-Locking of Biological Clocks,” Journal of Mathematical Biology, Vol.15, No.3, pp. 339-349, 1982.
  16. [16] J. T. Stuart, “On the nonlinear mechanics of wave disturbances in stable and unstable parallel flows,” Journal of Fluid Mechanics, Vol.9, pp. 353-370, 1960.
  17. [17] R.Miyata and K. Kurata, “Properties of localized oscillatory excitation on the non-linear oscillatory field,” to appear in Journal of Artificial Life and Robotics, Vol.16, 2011.
  18. [18] P. Foldiak, “Learning Invariance from transformation sequences,” Neural Computation, Vol.3, No.2, pp. 194-200, 1991.
  19. [19] T. Nagano and K. Kurata, “A self-organizing neural network model for the development of complex cells,” Biological Cybernetics, Vol.40, pp.195-200, 1981.
  20. [20] P. Foldiak, “Forming sparse representations by local anti-Hebbian learning,” Biological Cybernetics, Vol.64, pp. 165-170, 1990.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Feb. 25, 2021