Abstract

This paper adresses the problem of modeling object segmentation in the visual cortex using oscillations. The proposed architecture is based on a network of locally connected FitzHugh-Nagumo oscillators which receive graded external input. We show the suitability of such a network to encode the stimulus since the amplitude of oscillations increases monotonically as a function of the input in the neighborhood of a bifurcation, while the frequency remains nearly constant. However, due to the diffusive effects of the Laplacian connectivity, the oscillators tend to be in phase even when they represent different objects. Therefore a desynchronization mechanism, which represents spatial information about the objects, is added. The overall dynamics are described and simulation results on real images are shown. I. Introduction Recent trends in modeling information processing in the brain suggest temporal coding as a possible mechanism for concurrently representing features of external stimuli...

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