论文标题
带有Spintronic设备的短期突触可塑性的预期跟踪
Anticipative Tracking with the Short-Term Synaptic Plasticity of Spintronic Devices
论文作者
论文摘要
在当前的人工智能技术中，在认知任务中对高速对象的实时跟踪是具有挑战性的，因为数据处理和计算耗时会导致时间延迟。脑启发的连续吸引神经网络（CANN）可用于跟踪快速移动的目标，如果网络中的动态突触具有短期可塑性，则时间延迟将在本质上得到补偿。在这里，我们表明，具有短期抑郁症的突触可以通过磁性隧道连接来实现，这在广泛应用的数学模型中完美地重现了突触重量的动力学。然后，将这些动力学突触纳入一维和二维罐中，证明可以通过微磁模拟预测移动对象。这种用于神经形态计算的基于便携式旋转的硬件无需培训，因此对于移动目标的跟踪技术非常有前途。
Real-time tracking of high-speed objects in cognitive tasks is challenging in the present artificial intelligence techniques because the data processing and computation are time-consuming resulting in impeditive time delays. A brain-inspired continuous attractor neural network (CANN) can be used to track quickly moving targets, where the time delays are intrinsically compensated if the dynamical synapses in the network have the short-term plasticity. Here, we show that synapses with short-term depression can be realized by a magnetic tunnel junction, which perfectly reproduces the dynamics of the synaptic weight in a widely applied mathematical model. Then, these dynamical synapses are incorporated into one-dimensional and two-dimensional CANNs, which are demonstrated to have the ability to predict a moving object via micromagnetic simulations. This portable spintronics-based hardware for neuromorphic computing needs no training and is therefore very promising for the tracking technology for moving targets.
