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The human brain is able to learn, generalize, and predict crossmodal stimuli.Learning by expectation fine-tunes crossmodal processing at different levels,thus enhancing our power of generalization and adaptation in highly dynamicenvironments. In this paper, we propose a deep neural architecture trained byusing expectation learning accounting for unsupervised learning tasks. Ourlearning model exhibits a self-adaptable behavior, setting the first stepstowards the development of deep learning architectures for crossmodal stimuliassociation.

Expectation Learning for Adaptive Crossmodal Stimuli Association