Oscillatory neural network for image segmentation with biased competition for attention.

We study the emergent properties of an artificial neural network which combines segmentation by oscillations and biased competition for perceptual processing. The aim is to progress in image segmentation by mimicking abstractly the way how the cerebral cortex works. In our model, the neurons associated with features belonging to an object start to oscillate synchronously, while competing objects oscillate with an opposing phase. The emergent properties of the network are confirmed by experiments with artificial image data.

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