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  • 1 International Centre for Genetic Engineering and Biotechnology Padriciano 99 34129 Trieste Italy
  • | 2 Hungarian Academy of Sciences Biological Research Centre Temesvári krt. 62 H-6726 Szeged Hungary
  • | 3 University of Szeged Institute of Informatics Aradi vértanúk tere 1 H-6720 Szeged Hungary
  • | 4 University of Szeged HAS–SzTE Research Group on Artificial Intelligence Tisza Lajos krt. 103 H-6720 Szeged Hungary
  • | 5 Université de Paris-Sud, CNRS Laboratoire de l’Accélérateur Linéaire 91898 Orsay France
  • | 6 Pázmány Péter Catholic University Faculty of Information Technology Práter u. 50 H-1083 Budapest Hungary
  • | 7 Budapest University of Technology and Economics Department of Cognitive Science Stoczek u. 2 Budapest H-1111 Hungary
  • | 8 Budapest University of Technology and Economics HAS–BME Research Group in Cognitive Science Stoczek u. 2 Budapest H-1111 Hungary
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The primary visual cortex (V1) of the mammalian brain is equipped with a specifically connected network of neurons that can potentially solve difficult image processing tasks. These neurons are selectively tuned for locations in visual space and also for line orientation. The coupling of location and orientation tuning results in the neural representation of the visual world in terms of local features. These local features, e.g., oriented line segments, will have to be linked together in order to parse the visual world into regions corresponding to object and ground. Although standard models of V1 do not address the issue of interacting neuronal populations, we suggest that the long-range connectivity pattern of V1 provides an architecture where spreading neural activity may lead to pertinent figure-ground segmentation. The model relies on the fact that in addition to the processing units, their connections are also selectively tuned for space and orientation. From the computational point of view, the model uses a minimalist approach that applies the fundamental concepts of Gestalt psychology – proximity, similarity and continuity – to the spreading of neuronal activation signals. This model is successful in predicting psychophysical performance of human observers, and provides an account of the computational power of V1.

  • Altmann, C. F., Bulthoff, H. H., Kourtzi, Z. (2003): Perceptual organization of local elements into global shapes in the human visual cortex. Curr Biol, 13(4), 342–349.

  • Angelucci, A., Levitt, J. B., Walton, E. J., Hupe, J. M., Bullier, J., Lund, J. S. (2002): Circuits for local and global signal integration in primary visual cortex. J Neurosci, 22(19), 8633–8646.

  • August, J., Zucker, S. W. (2003): Sketches with curvature: The curve indicator random field and Markov processes. IEEE Trans. Pattern Anal. Mach. Intell., 25(4), 387–400.

  • Bosking, W. H., Crowley, J. C., Fitzpatrick, D. (2002): Spatial coding of position and orientation in primary visual cortex. Nat Neurosci, 5(9), 874–882.

  • Bosking, W. H., Zhang, Y., Schofield, B., Fitzpatrick, D. (1997): Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex. J Neurosci, 17(6), 2112–2127.

  • Douglas, R. J., Martin, K. A. (2004): Neuronal circuits of the neocortex. Annu Rev Neurosci, 27, 419–451.

  • Douglas, R. J., Martin, K. A. (2007): Mapping the matrix: the ways of neocortex. Neuron, 56(2), 226–238.

  • Field, D. J., Hayes, A., Hess, R. F. (1993): Contour integration by the human visual system: evidence for a local “association field”. Vision Res, 33(2), 173–193.

  • Giersch, A., Humphreys, G. W., Boucart, M., Kovacs, I. (2000): The computation of occluded contours in visual agnosia: Evidence for early computation prior to shape binding and figure-ground coding. Cogn Neuropsychol, 17(8), 731–759.

  • Gilbert, C. D. (1992): Horizontal integration and cortical dynamics. Neuron, 9(1), 1–13.

  • Gilbert, C. D., Wiesel, T. N. (1989): Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. J Neurosci, 9(7), 2432–2442.

  • Hubel, D. H., Wiesel, T. N. (1959): Receptive fields of single neurones in the cat’s striate cortex. J Physiol, 148, 574–591.

  • Kinoshita, M., Gilbert, C. D., Das, A. (2009): Optical imaging of contextual interactions in V1 of the behaving monkey. J Neurophysiol, 102(3), 1930–1944.

  • Kisvarday, Z. F., Eysel, U. T. (1992): Cellular organization of reciprocal patchy networks in layer III of cat visual cortex (area 17). Neuroscience, 46(2), 275–286.

  • Kourtzi, Z., Tolias, A. S., Altmann, C. F., Augath, M., Logothetis, N. K. (2003): Integration of local features into global shapes: monkey and human FMRI studies. Neuron, 37(2), 333–346.

  • Kovacs, I. (1996): Gestalten of today: early processing of visual contours and surfaces. Behav Brain Res, 82(1), 1–11.

  • Kovacs, I., Feher, A., Julesz, B. (1998): Medial-point description of shape: a representation for action coding and its psychophysical correlates. Vision Res, 38(15–16), 2323–2333.

  • Kovacs, I., Julesz, B. (1993): A closed curve is much more than an incomplete one: effect of closure in figure-ground segmentation. Proc Natl Acad Sci U S A, 90(16), 7495–7497.

  • Kovacs, I., Julesz, B. (1994): Perceptual sensitivity maps within globally defined visual shapes. Nature, 370(6491), 644–646.

  • Kovacs, I., Polat, U., Pennefather, P. M., Chandna, A., Norcia, A. M. (2000): A new test of contour integration deficits in patients with a history of disrupted binocular experience during visual development. Vision Res, 40(13), 1775–1783.

  • Li, W., Gilbert, C. D. (2002): Global contour saliency and local colinear interactions. J Neurophysiol, 88(5), 2846–2856.

  • Li, W., Piech, V., Gilbert, C. D. (2008): Learning to link visual contours. Neuron, 57(3), 442–451.

  • Li, Z. (1998): A neural model of contour integration in the primary visual cortex. Neural Comput, 10(4), 903–940.

  • Li, Z. (2005): The Primary Visual Cortex Creates a Bottom-up Saliency Map. In I. Laurent, R. Geraint K. T. John (Eds.), Neurobiology of Attention (pp. 570–575): Academic Press, Burlington.

  • Mathes, B., Fahle, M. (2007): Closure facilitates contour integration. Vision Res, 47(6), 818–827.

  • Mumford, D. (1993): Elastica and Computer Vision. In C. Bajaj (Ed.), Algebraic Geometry and its Applications (pp. 507–518): Springer-Verlag.

  • Olshausen, B. A., Field, D. J. (2005): How close are we to understanding v1? Neural Comput, 17(8), 1665–1699.

  • Rockland, K. S., Lund, J. S. (1983): Intrinsic laminar lattice connections in primate visual cortex. J Comp Neurol, 216(3), 303–318.

  • Rockland, K. S., Lund, J. S., Humphrey, A. L. (1982): Anatomical binding of intrinsic connections in striate cortex of tree shrews (Tupaia glis): J Comp Neurol, 209(1), 41–58.

  • Stettler, D. D., Das, A., Bennett, J., Gilbert, C. D. (2002): Lateral connectivity and contextual interactions in macaque primary visual cortex. Neuron, 36(4), 739–750.

  • Tsodyks, M., Kenet, T., Grinvald, A., Arieli, A. (1999): Linking spontaneous activity of single cortical neurons and the underlying functional architecture. Science, 286(5446), 1943–1946.

  • Williams, L. R., Jacobs, D. W. (1997): Stochastic completion fields; a neural model of illusory contour shape and salience. Neural Comput, 9(4), 837–858.

  • Yen, S. C., Finkel, L. H. (1998): Extraction of perceptually salient contours by striate cortical networks. Vision Res, 38(5), 719–741.

Learning & Perception
Language English
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Publisher Akadémiai Kiadó
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Chief Executive Officer, Akadémiai Kiadó
ISSN 1789-3186 (Print)
ISSN 2060-9175 (Online)