标题： 具有内在梯度下降适应的自学习磁霍普菲尔德神经网络 显示英文标题

标题： A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption

摘要： 利用物理材料和器件来模拟突触和神经元的物理神经网络，为实现人工神经网络提供了一种节能的方法。 然而，训练物理神经网络非常困难，并且严重依赖外部计算资源。 解决这一问题的一个新兴概念称为物理自学习，它使用内在物理参数作为可训练权重。 在外部输入（即训练数据）下，通过物理参数的自然演化实现训练，这些参数通过自主物理过程内在地适应现代学习规则，从而消除了对外部计算资源的需求。在这里，我们展示了一个真实的自旋电子系统，该系统模拟了霍普菲尔德神经网络（HNN），并通过物理过程的演化固有地执行无监督学习。 使用由磁性纹理定义的导电矩阵作为可训练权重，我们说明了在外部电压输入下，导电矩阵自然演化并以梯度下降的方式适应奥亚的学习算法。 自学习的HNN具有可扩展性，可以在高相似性的模式上实现联想记忆。 磁性纹理的快速自旋动力学和可重构性为在材料中直接实现高效自主训练提供了有利的平台。 显示英文摘要

摘要： Physical neural networks using physical materials and devices to mimic synapses and neurons offer an energy-efficient way to implement artificial neural networks. Yet, training physical neural networks are difficult and heavily relies on external computing resources. An emerging concept to solve this issue is called physical self-learning that uses intrinsic physical parameters as trainable weights. Under external inputs (i.e. training data), training is achieved by the natural evolution of physical parameters that intrinsically adapt modern learning rules via autonomous physical process, eliminating the requirements on external computation resources.Here, we demonstrate a real spintronic system that mimics Hopfield neural networks (HNN) and unsupervised learning is intrinsically performed via the evolution of physical process. Using magnetic texture defined conductance matrix as trainable weights, we illustrate that under external voltage inputs, the conductance matrix naturally evolves and adapts Oja's learning algorithm in a gradient descent manner. The self-learning HNN is scalable and can achieve associative memories on patterns with high similarities. The fast spin dynamics and reconfigurability of magnetic textures offer an advantageous platform towards efficient autonomous training directly in materials.