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Lateral Interactions in Self-Organizing Maps

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Artificial Neural Networks — ICANN 2002 (ICANN 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2415))

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Abstract

In the literature on topographic models of cortical organization, Kohonen’s self-organizing map is often treated as a computational short-cut version of a more detailed biological architecture, in which competition in the map is regulated by excitatory and inhibitory lateral interactions. A novel lateral interaction model will be presented here, whose investigation will show: first, that the behavior of the two models is not identical; and second, that the lateral interaction architecture behaves similarly to non-topographic algorithms, constructing representations of the input at intermediate levels of detail in the initial phases of training. This observation supports a novel interpretation of the topographic organization of the cerebral cortex.

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References

  1. Obermayer, K., Ritter, H., Schulten, K.: A principle for the formation of the spatial structure of cortical feature maps. Proceedings of the National Academy for Science USA 87 (1990) 8345–8349

    Article  Google Scholar 

  2. Ben-Yishai, R., Bar-Or, R. L., Sompolinsky, H.: Theory of orientation tuning in visual cortex. Proceedings of the National Academy for Science USA 92 (1995) 3844–3848

    Article  Google Scholar 

  3. Somers, D. C., Nelson, S. B., Sur, M.: An emergent model of orientation selectivity in cat visual cortical simple cells. The Journal of Neuroscience 15 (1995) 5448–5465

    Google Scholar 

  4. Ferster, D., Miller, K. D.: Neural mechanisms of orientation selectivity in the visual cortex. Annual Review of Neuroscience 23 (2000) 441–471

    Article  Google Scholar 

  5. Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 43 (1982) 59–69

    Article  MATH  MathSciNet  Google Scholar 

  6. Ritter, H., Martinetz, T. M., Schulten, K.: Neural Computation and Self-Organizing Maps. Wiley, Reading (Mass., 1992)

    MATH  Google Scholar 

  7. Linde, Y., Buzo, A., Gray, R. M.: An algorithm for vector quantiser design. IEEE Transactions on Communications 28 (1980) 84–95

    Article  Google Scholar 

  8. Rose, K., Gurewitz, E., Fox, G. C.: Vector quantization by deterministic annealing. IEEE Transactions on Information Theory 38 (1992) 1249–1257

    Article  MATH  Google Scholar 

  9. Martinetz, T. M., Berkovich, S. G., Schulten, K. L.:’ Neural-gas’ network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks 4 (1993) 558–569

    Article  Google Scholar 

  10. Yair, E., Zeger, K., Gersho, A.: Competitive learning and soft competition for vector quantizer design. IEEE Transactions on Signal Processing 40 (1992) 294–308

    Article  Google Scholar 

  11. Luttrell, S.P.: Derivation of a class of training algorithms. IEEE Transactions on Neural Networks 1 (1990) 229–232

    Article  Google Scholar 

  12. Cherkassky, V., Mulier, F.: Learning from Data. Concepts, Theory, and Methods. John Wiley & Sons, New York (1998)

    MATH  Google Scholar 

  13. Keil, F.C.: On the emergence of semantic and conceptual distinctions. Journal of Experimental Psychology: General 112 (1983) 357–385

    Article  Google Scholar 

  14. Mandler, J. M., Bauer, P. J., McDonough, L.: Separating the sheep from the goats: Differentiating global categories. Cognitive Psychology 23 (1991) 263–298

    Article  Google Scholar 

  15. Hopfield, J. J.: Neurons with graded response have collective computational properties like those of two state neurons. Proceedings of the National Academy for Science USA 81 (1984) 3088–3092

    Article  Google Scholar 

  16. Bezdek, J. C., Pal, N. R.: An index of topological preservation for feature extraction. Pattern Recognition 28 (1995) 381–391

    Article  Google Scholar 

  17. Luttrell, S.P.: A Bayesian analysis of self-organizing maps. Neural Computation 6 (1994) 767–794

    Article  MATH  Google Scholar 

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© 2002 Springer-Verlag Berlin Heidelberg

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Viviani, R. (2002). Lateral Interactions in Self-Organizing Maps. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_149

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  • DOI: https://doi.org/10.1007/3-540-46084-5_149

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44074-1

  • Online ISBN: 978-3-540-46084-8

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