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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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
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