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Spike-Timing Dependent Plasticity in Unipolar Silicon Oxide RRAM Devices

机译:单极氧化硅RRAM器件中的尖峰时序相关可塑性

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摘要

Resistance switching, or Resistive RAM (RRAM) devices show considerable potential for application in hardware spiking neural networks (neuro-inspired computing) by mimicking some of the behavior of biological synapses, and hence enabling non-von Neumann computer architectures. Spike-timing dependent plasticity (STDP) is one such behavior, and one example of several classes of plasticity that are being examined with the aim of finding suitable algorithms for application in many computing tasks such as coincidence detection, classification and image recognition. In previous work we have demonstrated that the neuromorphic capabilities of silicon-rich silicon oxide (SiOx) resistance switching devices extend beyond plasticity to include thresholding, spiking, and integration. We previously demonstrated such behaviors in devices operated in the unipolar mode, opening up the question of whether we could add plasticity to the list of features exhibited by our devices. Here we demonstrate clear STDP in unipolar devices. Significantly, we show that the response of our devices is broadly similar to that of biological synapses. This work further reinforces the potential of simple two-terminal RRAM devices to mimic neuronal functionality in hardware spiking neural networks.
机译:电阻切换或电阻RAM(RRAM)设备通过模仿生物突触的某些行为,从而在非硬件神经网络(神经启发计算)中显示出巨大的应用潜力,从而支持非冯·诺依曼计算机体系结构。依赖于尖峰时序的可塑性(STDP)就是这样一种行为,并且正在研究几种可塑性的例子,目的是寻找适用于许多计算任务(例如巧合检测,分类和图像识别)的算法。在先前的工作中,我们已经证明了富硅氧化硅(SiOx)电阻开关器件的神经形态能力超出了可塑性范围,包括阈值,峰值和积分。我们以前在单极性模式下运行的设备中演示了这种行为,从而提出了一个问题,即是否可以在设备所显示的功能列表中增加可塑性。在这里,我们展示了单极器件中清晰的STDP。重要的是,我们证明了我们设备的反应与生物学突触的反应大致相似。这项工作进一步增强了简单的两端RRAM设备模仿硬件尖峰神经网络中神经元功能的潜力。

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