Neuromorphic systems consisting of artificial neurons and synapses can process complex information with high efficiency to overcome the bottleneck of von Neumann architecture. Artificial neurons are essentially required to possess functions such as leaky integrate‐and‐fire and output spike. However, previous reported artificial neurons typically have high operation voltage and large leakage current, leading to significant power consumption, which is contrary to the energy‐efficient biological model. Here, an oscillatory neuron based on Ag filamentary threshold switching memristor (TS) that has a low operation voltage (<0.6 V) with ultralow power consumption (<1.8 µW) is presented. It can trigger neuronal functions, including leaky integrate‐and‐fire and threshold‐driven spiking output, with high endurance (>10⁸ cycles). Being connected to an external resistor or a resistive switching memristor (RS) as synaptic weight, the TS clearly demonstrates self‐oscillation behavior once the input pulse voltage exceeds the threshold voltage. Meanwhile, the oscillation frequency is proportional to the input pulse voltage and the conductance of RS synapse, which can be used to integrate the weighted sum current. As an energy‐efficient memristor‐based spiking neural network, this combination of TS oscillatory neuron with RS synapse is further evaluated for image recognition achieving an accuracy of 79.2 ± 2.4% for CIFAR‐10 subset.

中文翻译：

---

用于基于忆阻器的神经形态系统的低压振荡神经元。


由人工神经元和突触组成的神经形态系统可以高效地处理复杂信息，克服冯·诺依曼架构的瓶颈。人工神经元本质上需要具备泄漏积分激发和输出尖峰等功能。然而，先前报道的人工神经元通常具有高工作电压和大漏电流，导致显着的功耗，这与节能的生物模型相反。这里提出了一种基于银丝阈值开关忆阻器 (TS) 的振荡神经元，该神经元具有低工作电压 (<0.6 V) 和超低功耗 (<1.8 µW)。它可以触发神经元功能，包括泄漏积分触发和阈值驱动的尖峰输出，具有高耐久性（>10 ^{8 个}周期）。连接到外部电阻器或阻变忆阻器 (RS) 作为突触权重后，一旦输入脉冲电压超过阈值电压，TS 就会清晰地表现出自振荡行为。同时，振荡频率与输入脉冲电压和RS突触的电导成正比，可用于对加权和电流进行积分。作为一种基于忆阻器的节能尖峰神经网络，TS 振荡神经元与 RS 突触的这种组合被进一步评估用于图像识别，CIFAR-10 子集的准确率达到 79.2 ± 2.4%。