The memristor has been regarded as a promising candidate for constructing a neuromorphic computing platform that is capable of confronting the bottleneck of the traditional von Neumann architecture. Here, inspired by the working mechanism of the G-protein-linked receptor of biological cells, a novel double-layer memristive device with reduced graphene oxide (rGO) nanosheets covered by chitosan (an ionic conductive polymer) as the channel material is constructed. The protons in chitosan and the functional groups in rGO nanosheets imitate the functions of the ligands and receptors of biological cells, respectively. Smooth changes in the response current depending on the historical applied voltages are observed, offering a promising pathway toward biorealistic synaptic emulation. The memristive behavior is mainly a result of the interaction between protons provided by chitosan and the defects and functional groups in the rGO nanosheets. The channel current is due to the hopping of protons through functional groups and is limited by the traps in the rGO nanosheets. The transition from short-term to long-term potentiation is achieved, and learning-forgetting behaviors of the memristor mimicking those of the human brain are demonstrated. Overall, the bioinspired memristor-type artificial synaptic device shows great potential in neuromorphic networks.

忆阻器被认为是构建能够解决传统冯诺依曼架构瓶颈的神经形态计算平台的有前途的候选者。在这里，受生物细胞 G 蛋白连接受体工作机制的启发，构建了一种新型双层忆阻器件，其具有被壳聚糖（一种离子导电聚合物）覆盖的还原氧化石墨烯（rGO）纳米片作为通道材料。壳聚糖中的质子和 rGO 纳米片中的官能团分别模拟生物细胞的配体和受体的功能。观察到响应电流的平滑变化取决于历史施加的电压，为生物现实突触仿真提供了有希望的途径。忆阻行为主要是壳聚糖提供的质子与 rGO 纳米片中的缺陷和官能团之间相互作用的结果。通道电流是由于质子通过官能团跳跃，并受到 rGO 纳米片中陷阱的限制。实现了从短期到长期增强的转变，并证明了忆阻器模仿人脑的学习遗忘行为。总体而言，仿生忆阻器型人工突触装置在神经形态网络中显示出巨大的潜力。实现了从短期到长期增强的转变，并证明了忆阻器模仿人脑的学习遗忘行为。总体而言，仿生忆阻器型人工突触装置在神经形态网络中显示出巨大的潜力。实现了从短期到长期增强的转变，并证明了忆阻器模仿人脑的学习遗忘行为。总体而言，仿生忆阻器型人工突触装置在神经形态网络中显示出巨大的潜力。