Contour Integration and Synchronization in Neuronal Networks of the Visual Cortex

  • Ekkehard Ullner
  • Raúl Vicente
  • Gordon Pipa
  • Jordi García-Ojalvo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5164)


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.


System Size Noise Intensity Stochastic Resonance Open Chain Closed Contour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Singer, W.: Neuronal synchrony: A versatile code for the definition of relations? Neuron 24(1), 49–65 (1999)CrossRefGoogle Scholar
  2. 2.
    Gray, C.M., König, P., Engel, A.K., Singer, W.: Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338, 334–337 (1989)CrossRefGoogle Scholar
  3. 3.
    Castelo-Branco, M., Goebel, R., Neuenschwander, S., Singer, W.: Neural synchrony correlates with surface segregation rules. Nature 405, 685–689 (2000)CrossRefGoogle Scholar
  4. 4.
    Sarpeshkar, R.: Analog versus digital: Extrapolating from electronics to neurobiology. Neural Comput. 10(7), 1601–1638 (1998)CrossRefGoogle Scholar
  5. 5.
    Mori, T., Kai, S.: Noise-induced entrainment and stochastic resonance in human brain waves. Phys. Rev. Lett. 88, 218101 (2002)CrossRefGoogle Scholar
  6. 6.
    Lee, S., Neiman, A., Kim, S.: Coherence resonance in a hodgkin-huxley neuron. Phys. Rev. E 57, 3292 (1998)CrossRefGoogle Scholar
  7. 7.
    Pikovsky, A., Kurths, J.: Coherence resonance in a noise-driven excitable system. Phys. Rev. Lett. 78, 775 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Lindner, B., Schimansky-Geier, L., Longtin, A.: Maximizing spike train coherence or incoherence in the leaky integrate-and-fire model. Phys. Rev. E 66, 31916 (2002)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Longtin, A.: Autonomous stochastic resonance in bursting neurons. Phys. Rev. E 55, 868 (1997)CrossRefGoogle Scholar
  10. 10.
    Palenzuela, C., Toral, R., Mirasso, C., Calvo, O., Gunton, J.: Coherence resonance in chaotic systems. Europhys. Lett. 56(3), 347–353 (2001)CrossRefGoogle Scholar
  11. 11.
    Ganopolski, A., Rahmstorf, S.: Abrupt glacial climate changes due to stochastic resonance. Phys. Rev. Lett. 88, 038501 (2002)CrossRefGoogle Scholar
  12. 12.
    Dubbeldam, J.L.A., Krauskopf, B., Lenstra, D.: Excitability and coherence resonance in lasers with saturable absorber. Phys. Rev. E 60, 6580–6588 (1999)CrossRefGoogle Scholar
  13. 13.
    Buldú, J.M., García-Ojalvo, J., Mirasso, C.R., Torrent, M.C., Sancho, J.M.: Effect of external noise correlation in optical coherence resonance. Phys. Rev. E 64, 051109 (2001)CrossRefGoogle Scholar
  14. 14.
    Hu, B., Zhou, C.: Phase synchronization in coupled nonidentical excitable systems and array-enhanced coherence resonance. Phys. Rev. E 61(2), R1001–R1004 (2000)CrossRefGoogle Scholar
  15. 15.
    Keener, J.P., Sneyd, J.: Mathematical Physiology. Springer, New York (1998)zbMATHGoogle Scholar
  16. 16.
    FitzHugh, R.A.: Impulses and physiological states in models of nerve membrane. Biophys. J. 1, 445–466 (1961)CrossRefGoogle Scholar
  17. 17.
    Nagumo, J., Arimoto, S., Yoshitzawa, S.: An active pulse transmission line simulating nerve axon. Proc. IRE 50, 2061 (1962)CrossRefGoogle Scholar
  18. 18.
    Mikhailov, A.S.: Foundations of Synergetics, 2nd edn. Springer, Berlin (1994)zbMATHGoogle Scholar
  19. 19.
    García-Ojalvo, J., Elowitz, M.B., Strogatz, S.H.: Modeling a synthetic multicellular clock: Repressilators coupled by quorum sensing. Proc. Natl. Acad. Sci. U.S.A 101(30), 10955–10960 (2004)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ekkehard Ullner
    • 1
  • Raúl Vicente
    • 2
    • 3
  • Gordon Pipa
    • 2
    • 3
    • 4
  • Jordi García-Ojalvo
    • 1
  1. 1.Departament de Física i Enginyeria NuclearUniversitat Politècnica de CatalunyaTerrassaSpain
  2. 2.Max-Planck Institute for Brain Research Frankfurt/MainGermany
  3. 3.Frankfurt Institute for Advanced Studies Frankfurt/MainGermany
  4. 4.Dep. of Brain and Cognitive Sciences, Massachusetts Inst. of Technology, and Dep. of Anesthesia and Critical CareMassachusetts General HospitalCambridgeUSA

Personalised recommendations