Reservoir Computing is a type of recursive neural network commonly used forrecognizing and predicting spatio-temporal events relying on a complexhierarchy of nested feedback loops to generate a memory functionality. TheReservoir Computing paradigm does not require any knowledge of the reservoirtopology or node weights for training purposes and can therefore utilizenaturally existing networks formed by a wide variety of physical processes.Most efforts prior to this have focused on utilizing memristor techniques toimplement recursive neural networks. This paper examines the potential ofskyrmion fabrics formed in magnets with broken inversion symmetry that mayprovide an attractive physical instantiation for Reservoir Computing.
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