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 to synchronize with respect to such heterogeneity is studied. Synchronization on a contour is found to be possible for a sufficiently large width. © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Hugues, E., Guilleux, F., & Rochel, O. (2002). Contour detection by synchronization of integrate-and-fire neurons. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2525, 60–69. https://doi.org/10.1007/3-540-36181-2_6
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