Neuromorphic computing systems are promising alternatives in areas such as pattern recognition and image processing. This work focuses on the fabrication of tin oxide memristors (Ag/SnO2/Pt) to emulate artificial synapses and neurons. These tin oxide memristors demonstrate stable switching between threshold switch (TS) and resistive switch (RS) modes, achieved by adjusting the compliance current. Notably, this memristor achieves extremely low switching voltage and excellent cycle endurance. Moreover, the conductance value of the memristor can continuously transform under different illumination conditions, such as white light and purple light. A single tin oxide memristor device is used to model typical neuromorphic responses, such as synaptic plasticity and artificial neuron impulse responses. This approach offers a promising solution for high‐density, low‐power, brain‐inspired computing chips. Additionally, memristive Leaky Integrate‐and‐Fire (LIF) neuron and synapse models are designed and integrated for the first time into a Self‐Organizing Map Spiking Neural Network (SOM‐SNN) architecture. Applying this architecture to an unsupervised learning self‐organizing map memristor SNN achieved an impressive 94% recognition rate on the MNIST dataset. This study elucidates the potential for seamlessly integrating memristors into neuromorphic systems.

中文翻译：

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基于氧化锡忆阻突触和神经元的自组织映射脉冲神经网络


神经形态计算系统是模式识别和图像处理等领域有前途的替代方案。这项工作的重点是制造氧化锡忆阻器 （Ag/SnO2/Pt） 以模拟人工突触和神经元。这些氧化锡忆阻器通过调节顺从电流实现阈值开关 （TS） 和电阻开关 （RS） 模式之间的稳定切换。值得注意的是，该忆阻器实现了极低的开关电压和出色的循环耐久性。此外，忆阻器的电导值可以在不同的照明条件下连续变换，例如白光和紫光。单个氧化锡忆阻器器件用于模拟典型的神经形态反应，例如突触可塑性和人工神经元冲动反应。这种方法为高密度、低功耗、类脑计算芯片提供了一种有前途的解决方案。此外，忆阻性 Leaky Integrate-and-Fire （LIF） 神经元和突触模型被设计并首次集成到自组织映射脉冲神经网络 （SOM-SNN） 架构中。将这种架构应用于无监督学习自组织映射忆阻器，SNN 在 MNIST 数据集上实现了令人印象深刻的 94% 识别率。本研究阐明了将忆阻器无缝集成到神经形态系统中的潜力。