Spiking neural network (SNN) consisting of memristor-based artificial neurons and synapses has emerged as a compact and energy-efficient hardware solution for spatiotemporal information processing. However, it is challenging to develop memristive neurons and synapses based on the same material system because the required resistive switching (RS) characteristics are different. Here, it is shown that SrFeO_x (SFO), an intriguing material system exhibiting topotactic phase transformation between insulating brownmillerite (BM) SrFeO_2.5 phase and conductive perovskite (PV) SrFeO₃ phase, can be engineered into both neuronal and synaptic devices. Using a BM-SFO single layer as the RS medium, the Au/BM-SFO/SrRuO₃ (SRO) memristor exhibits nonvolatile RS behavior originating from the formation/rupture of PV-SFO filaments in the BM-SFO matrix. By contrast, using a PV-SFO (matrix)/BM-SFO (interfacial layer) bilayer as the RS medium, the Au/PV-SFO/BM-SFO/SRO memristor exhibits volatile RS behavior originating from the interfacial BM-PV phase transformation. Synaptic and neuronal characteristics are further demonstrated in the Au/BM-SFO/SRO and Au/PV-SFO/BM-SFO/SRO memristors, respectively. Using the SFO-based synapses and neurons, fully memristive SNNs are constructed by simulation, which show good performance on unsupervised image recognition. Our study suggests that SFO is a versatile material platform on which both neuronal and synaptic devices can be developed for constructing fully memristive SNNs.

由基于忆阻器的人工神经元和突触组成的脉冲神经网络 (SNN) 已成为时空信息处理的紧凑且节能的硬件解决方案。然而，开发基于相同材料系统的忆阻神经元和突触具有挑战性，因为所需的电阻开关 (RS) 特性不同。在这里，研究表明 SrFeO _x (SFO) 是一种有趣的材料系统，在绝缘褐煤 (BM) SrFeO _2.5相和导电钙钛矿 (PV) SrFeO ₃相之间表现出拓扑相变，可以设计成神经元和突触装置。使用 BM-SFO 单层作为 RS 介质，Au/BM-SFO/SrRuO ₃(SRO) 忆阻器表现出非易失性 RS 行为，源于 BM-SFO 基质中 PV-SFO 细丝的形成/破裂。相比之下，使用 PV-SFO（矩阵）/BM-SFO（界面层）双层作为 RS 介质，Au/PV-SFO/BM-SFO/SRO 忆阻器表现出源自界面 BM-PV 相的挥发性 RS 行为转型。突触和神经元特征分别在 Au/BM-SFO/SRO 和 Au/PV-SFO/BM-SFO/SRO 忆阻器中得到进一步证明。使用基于 SFO 的突触和神经元，通过模拟构建全忆阻 SNN，在无监督图像识别方面表现出良好的性能。我们的研究表明，SFO 是一个多功能材料平台，可以在其上开发神经元和突触设备，以构建完全忆阻的 SNN。