Activation of silent synapses is of great significance for the extension of neural plasticity related to learning and memory. Inspired by the activation of silent synapses via receptor insertion in neural synapses, we propose an efficient method for activating artificial synapses through the intercalation of Sn in layered α-MoO₃. Sn intercalation is capable of switching on the response of layered α-MoO₃ to the stimuli of visible and near infrared light by decreasing the bandgap. This mimics the receptor insertion process in silent neural synapses. The Sn-intercalated MoO₃ (Sn-MoO₃) exhibits persistent photoconductivity due to the donor impurity induced by Sn intercalation. This enables the two-terminal Sn-MoO₃ device promising optoelectronic synapse with an ultrahigh paired pulse facilitation (PPF) up to 199.5%. On-demand activation and tunable synaptic plasticity endow the device great potentials for extensible neuromorphic computing. Superior performance of the extensible artificial neural network (ANN) based on the Sn-MoO₃ synapses are demonstrated in pattern recognition. Impressively, the recognition accuracy increases from 89.7% to 94.8% by activating more nodes into the ANN. This is consistent with the recognition process of physical neural network during brain development. The intercalation engineering of MoO₃ may provide inspirations for the design of high-performance neuromorphic computing architectures.

沉默突触的激活对于扩展与学习和记忆相关的神经可塑性具有重要意义。受到通过在神经突触中插入受体来激活沉默突触的启发，我们提出了一种通过在层状α -MoO ₃中嵌入 Sn 来激活人工突触的有效方法。Sn嵌入能够通过减小带隙来开启层状α -MoO _{3对可见光和近红外光刺激的响应。}这模仿了沉默神经突触中的受体插入过程。Sn插层MoO ₃ (Sn-MoO ₃）由于 Sn 嵌入引起的施主杂质而表现出持久的光电导性。这使得两端 Sn-MoO ₃器件有望成为具有高达 199.5% 的超高配对脉冲促进 (PPF) 的光电突触。按需激活和可调突触可塑性赋予该设备可扩展神经形态计算的巨大潜力。基于Sn-MoO ₃突触的可扩展人工神经网络（ANN）的优越性能在模式识别中得到了证明。令人印象深刻的是，通过在 ANN 中激活更多节点，识别准确率从 89.7% 提高到 94.8%。这与大脑发育过程中物理神经网络的识别过程是一致的。_{MoO 3}插层工程可能为高性能神经形态计算架构的设计提供灵感。