A Hopfield neural network in magnetic films with natural learning

TL;DR

This paper proposes a physical realization of neural networks using magnetic textures in spintronics devices, demonstrating a Hopfield network that updates weights intrinsically through physical feedback for pattern recognition.

Contribution

It introduces a novel magnetic texture-based neural network that inherently updates weights without external computation, mimicking brain-like plasticity.

Findings

Simulated a 4-node Hopfield neural network using magnetic textures.

Showed intrinsic weight updating through physical feedback.

Demonstrated pattern recognition capabilities.

Abstract

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.