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Johns Hopkins researchers report that they have uncovered significant new details about how the cerebellum — the “learning machine” of the mammalian brain — makes predictions and learns from its mistakes. Their results demonstrate that the cerebellum is organized in a very different way than current designs of artificial neural networks, which are currently used in machine learning and artificial intelligence.