Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.
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机译：忆阻突触是人工神经网络中用于突触互连的最有希望的无源设备，是最近对硬件神经网络进行研究的驱动力。尽管为利用忆阻突触付出了巨大的努力，但迄今为止的进展仅显示了构建可对简单图像模式进行分类的神经网络系统的可能性。在本文中，我们报告了具有改善的突触特性的高密度交叉点忆阻突触阵列。所提出的基于PCMO的忆阻突触表现出必要的渐变和对称电导变化，并且已经成功地适应了神经网络系统。该系统使用在受试者想象说元音时产生的脑电图信号来学习并随后识别与三个元音对应的人类思维模式，即/ a /，/ i /和/ u /。我们成功地演示了用于EEG模式识别的神经网络系统，这可能会吸引许多研究人员，并激发新的研究方向。
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