摘要在当前的人工智能技术中，对认知任务中的高速对象进行实时跟踪是具有挑战性的，因为数据处理和计算非常耗时，从而导致了强制性的时间延迟。受大脑的工作机制启发连续吸引神经网络（CANN）可用于跟踪快速移动的目 ...
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摘要 在当前的人工智能技术中，对认知任务中的高速对象进行实时跟踪是具有挑战性的，因为数据处理和计算非常耗时，从而导致了强制性的时间延迟。受大脑的工作机制启发连续吸引神经网络（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，则可以固有地补偿时间延迟。在这里，我们显示可以通过磁性隧道结实现具有短期突触可塑性的突触，该器件在完全应用的数学模型中完美地再现了突触权重的动态。然后，将这些动态突触合并到一维和二维CANN中，这表明它们具有通过微磁模拟预测运动对象的能力。这种用于神经形态计算的基于自旋电子学的便携式硬件无需培训，因此对于移动目标的跟踪技术非常有前途。 Real-time tracking ofhigh-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 fast moving targets, where the time delays are intrinsically compensated if the dynamical synapses in the network have 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 of moving targets.
图1 一维连续吸引子神经网络以及基于隧道结的硬件实现示意图
图2 二维连续吸引子神经网络示意图 |
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