Publications

We propose a model of coupled oscillators with noise that performs binding and segmentation of objects using a set of stored images each consisting of figures and a background. The amplitudes of the oscillators encode the spatial and featural distribution of the external stimulus. In the learning stage the couplings between the phases are modified in a Hebb-like manner. By meanfield analysis and simulations we show that an external stimulus whose local features resemble those of one or several of the stored figures causes a selective phase coherence that retrieves the stored pattern of segmentation.