Macroscopic spin ensembles possess brain-like features such as non-linearity, plasticity, stochasticity, selfoscillations, and memory effects, and therefore offer opportunities for neuromorphic computing by spintronics devices. Here we propose a physical realization of artificial neural networks based on magnetic textures, which can update their weights intrinsically via built-in physical feedback utilizing the plasticity and large number of degrees of freedom of the magnetic domain patterns and without resource-demanding external computations. We demonstrate the idea by simulating the operation of a 4-node Hopfield neural network for pattern recognition.
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