SEGMENTATION BY A NETWORK OF OSCILLATORS WITH STORED MEMORIES

We propose a model of coupled oscillators with noise that performs segmentation of stimuli using a set of stored images, each consisting of objects and a background. The oscillators' amplitudes encode the spatial and featural distribution of the external stimulus. The coherence of their phases signifies their belonging to the same object. In the learning stage, the couplings between phases are modified in a Hebb-like manner. By mean-field analysis and simulations, we show that an external stimulus whose local features resemble those of one or several of the stored objects generates a selective phase coherence that represents the stored pattern of segmentation.