In spiking neural networks, neuron dynamics are described by the biologically realistic integrate-and-fire model that captures membrane potential accumulation and above-threshold firing behaviors. Among the hardware implementations of integrate-and-fire neuron devices, one important feature, reset, has been largely ignored. Here, we present the design and fabrication of a magnetic domain wall and magnetic tunnel junction based artificial integrate-and-fire neuron device that achieves reliable reset at the end of the integrate-fire cycle. We demonstrate the domain propagation in the domain wall racetrack (integration), reading using a magnetic tunnel junction (fire), and reset as the domain is ejected from the racetrack, showing the artificial neuron can be operated continuously over 100 integrate-fire-reset cycles. Both pulse amplitude and pulse number encoding is demonstrated. The device data is applied on an image classification task using a spiking neural network and shown to have comparable performance to an ideal leaky, integrate-and-fire neural network. These results achieve the first demonstration of reliable integrate-fire-reset in domain wall-magnetic tunnel junction-based neuron devices and shows the promise of spintronics for neuromorphic computing.

翻译：在脉冲神经网络中，神经元动力学由生物逼真的积分发放模型描述，该模型捕捉了膜电位累积与超阈值发放行为。在积分发放神经元器件的硬件实现中，一个关键特性——复位——长期被忽视。本文提出并制备了一种基于磁畴壁与磁隧道结的人工积分发放神经元器件，该器件在积分-发放周期结束时实现了可靠复位。我们展示了畴壁跑道中的畴传播（积分过程）、利用磁隧道结的读取（发放过程）以及畴从跑道中弹出时的复位过程，证明该人工神经元可连续运行超过100个积分-发放-复位周期。实验同时验证了脉冲幅度编码与脉冲数量编码。将器件数据应用于脉冲神经网络的图像分类任务，结果显示其性能可与理想的泄漏积分发放神经网络相媲美。这些成果首次在基于畴壁-磁隧道结的神经元器件中实现了可靠的积分-发放-复位循环，展现了自旋电子学在神经形态计算领域的应用潜力。