  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.
