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Contour Integration and Synchronization in Neuronal Networks of the Visual Cortex

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Book cover Artificial Neural Networks - ICANN 2008 (ICANN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5164))

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Abstract

The visual perception of contours by the brain is selective. When embedded within a noisy background, closed contours are detected faster, and with higher certainty, than open contours. We investigate this phenomenon theoretically with the paradigmatic excitable FitzHugh-Nagumo model, by considering a set of locally coupled oscillators subject to local uncorrelated noise. Noise is needed to overcome the excitation threshold and evoke spikes. We model one-dimensional structures and consider the synchronization throughout them as a mechanism for contour perception, for various system sizes and local noise intensities. The model with a closed ring structure shows a significantly higher synchronization than the one with the open structure. Interestingly, the effect is most pronounced for intermediate system sizes and noise intensities.

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Authors and Affiliations

  1. Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Colom 11, E–08222, Terrassa, Spain

    Ekkehard Ullner & Jordi García-Ojalvo

  2. Max-Planck Institute for Brain Research,  , Deutschordenstr. 46, D-60528, Frankfurt/Main, Germany

    Raúl Vicente & Gordon Pipa

  3. Frankfurt Institute for Advanced Studies,  , Ruth-Moufang-Str. 1, D-60438, Frankfurt/Main, Germany

    Raúl Vicente & Gordon Pipa

  4. Dep. of Brain and Cognitive Sciences, Massachusetts Inst. of Technology, and Dep. of Anesthesia and Critical Care, Massachusetts General Hospital, 77 Massachusetts Ave., Cambridge, MA 02139-4307, USA

    Gordon Pipa

Authors
  1. Ekkehard Ullner
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  2. Raúl Vicente
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  3. Gordon Pipa
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  4. Jordi García-Ojalvo
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Véra Kůrková Roman Neruda Jan Koutník

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© 2008 Springer-Verlag Berlin Heidelberg

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Ullner, E., Vicente, R., Pipa, G., García-Ojalvo, J. (2008). Contour Integration and Synchronization in Neuronal Networks of the Visual Cortex. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_73

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  • DOI: https://doi.org/10.1007/978-3-540-87559-8_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87558-1

  • Online ISBN: 978-3-540-87559-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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