Iterative Orientation Tuning in V1: A Simple Cell Circuit with Cross-Orientation Suppression

  • Marina Kolesnik
  • Alexander Barlit
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)


An iterative model for contrast detection, which accounts for the contrast invariance of orientation preference, has been recently suggested [16]. The work here extends the iterative model by incorporating a cross-oriented suppression of simple cells in the primary visual cortex (V1). The modified model has a better performance in terms of robustness to noise, generates sharper edge responses while suppressing weak edges, and converges faster on equilibrium. The model exhibits a higher level of contrast invariance of orientation preference generating a clear pattern of edges in natural images.


Lateral Geniculate Nucleus Simple Cell Ocular Dominance Orientation Selectivity Luminance Change 
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  1. 1.
    Hubel, D., H., Wiesel, T., N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Psychology, 160:106–154, 1962.Google Scholar
  2. 2.
    Movshon, J., A., Thompson, I., D., Tolhurst, D., J. Spatial summation in the receptive fields of simple cells in the cat’s striate cortex. Journal of Physiology (London), 283:53–77, 1978.Google Scholar
  3. 3.
    Carandini, M., Ferster, D. A tonic hyperpolarization underlying contrast adaptation in the cat visual cortex. Science 276:949–952. 1997.CrossRefGoogle Scholar
  4. 4.
    Sclar, G., Freeman, R.: Orientation selectivity in the cat’s striate cortex is invariant with stimulus contrast. Experimental Brain Research, 46, (1982) 457–461.CrossRefGoogle Scholar
  5. 5.
    Albrecht, D., G., Geisler, W., S. Motion sensitivity and the contrast response function of simple cells in the visual cortex. Visual Neuroscience. 7:531–546.Google Scholar
  6. 6.
    Heeger, D., J. Nonlinear model of neural responses in cat visual cortex. In M. Landy, J. A. Movshon (eds.): Computational models of visual processing. Cambridge, MIT: 119–133, 1991.Google Scholar
  7. 7.
    DeAngelis G., C., Robson, J., G., Ohzawa, I., Freeman, R., D. The organization of suppression in receptive fields of neurons in the cat’s visual cortex. J. Neurophysiology. 68:144–163. 1992.Google Scholar
  8. 8.
    Carandini, M., Heeger, D., J. Summation and division by neurons in visual cortex. Science, 264:1333–1336, 1994.CrossRefGoogle Scholar
  9. 9.
    Ferster, D.: The synaptic inputs to simple cells in the cat visual cortex. In: D. Lam and G. Gilbert (eds.): Neural mechanisms of visual perception, Ch. 3, Portfolio Publ. Co, The Woodlands, Texas: 63–85, 1989.Google Scholar
  10. 10.
    Pessoa, L., Mingolla, E., Neumann, H.: A contrast-and luminance-driven multiscale network model of brightness perception. Vision Research, 35:2201–2223, 1995.CrossRefGoogle Scholar
  11. 11.
    Neumann, H., Pessoa, L., Hansen, Th.: Interaction of ON and OFF pathways for visual contrast measurement. Biological Cybernetics, 81:515–532, 1999.CrossRefGoogle Scholar
  12. 12.
    Hansen, Th., Neumann, H. A model of V1 visual contrast processing utilizing long-range connections and recurrent interactions. In Proc. of the International Conference on Artificial Neural Networks, Edinburgh, UK, Sept. 7–10:61–66, 1999.Google Scholar
  13. 13.
    Hansen, Th., Baratoff, G., Neumann, H.: A simple cell model with dominating opponent inhibition for robust contrast detection. Kognitionswissenschaft, 9:93–100, 2000.Google Scholar
  14. 14.
    Hubel, D., H., Wiesel, T., N.: Sequence regularity and geometry of orientation columns in the monkey striate cortex. Journal of Comparative Neurology, 158:267–294, 1974.CrossRefGoogle Scholar
  15. 15.
    Hubel, D., H., Wiesel, T., N.: Functional architecture of macaque monkey visual cortex. Proceedings of the Royal Cosiety of London, B, 198:1–59, 1977.Google Scholar
  16. 16.
    Kolesnik, M., Barlit, A., Zubkov, E. Iterative Tuning of Simple Cells for Contrast Invariant Edge Enhancement. Proc. of the 2nd International Workshop on Biologically Motivated Computer Vision (BMCV’2002), 27–37, 2002.Google Scholar
  17. 17.
    Morrone, M., C., Burr, D., C., Maffei, L. Functional implications of cross-orientation inhibition of cortical visual cell. 1. Neurophysiological evidence. Proc. Royal Society London [Biol.], 216:335–354, 1982.CrossRefGoogle Scholar
  18. 18.
    Bonds, A., B. Role of inhibition in the specification of orientation selectivity of cells in the cat striate cortex. Visual Neuroscience, 2:41–55.Google Scholar
  19. 19.
    Kuffler, S., W.: Discharge patterns and functional organization of mammalian retina. Journal of Neurophysiology, 16:37–68, 1953.Google Scholar
  20. 20.
    Grossberg, S., Raizada, R., D., S.: Contrast-sensitive perceptual grouping and object-based attention in the laminar circuits of primary visual cortex. CAS/CNS TR-99-008, Boston University: 1–35, 1999.Google Scholar
  21. 21.
    Obermayer, K., Blasdel, G., G. Geometry of orientation and ocular dominance columns in monkey striate cortex. Journal of Neuroscience. 13:4114–4129. 1993.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Marina Kolesnik
    • 1
  • Alexander Barlit
    • 1
  1. 1.Fraunhofer Institute for Media CommunicationSankt-AugustinGermany

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