Abstract
We present a biologically inspired spiking neural network which is able to detect contours in grey level images by synchronization of neurons. This network is made of integrate-and-fire neurons, spaced on a triangular network, whose oriented receptive field is constructed by a wavelet which specifically detects edges. The neurons are excitatorily and locally connected between receptive fields that tend to detect the same contour. A contour, if its width is not too large, activates a chain of neurons, with some heterogeneity in the inputs. The capacity of a chain tosync hronize with respect tosuc h heterogeneity is studied. Synchronization on a contour is found to be possible for a sufficiently large width.
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Hugues, E., Guilleux, F., Rochel, O. (2002). Contour Detection by Synchronization of Integrate-and-Fire Neurons. In: Bülthoff, H.H., Wallraven, C., Lee, SW., Poggio, T.A. (eds) Biologically Motivated Computer Vision. BMCV 2002. Lecture Notes in Computer Science, vol 2525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36181-2_6
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DOI: https://doi.org/10.1007/3-540-36181-2_6
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