Henan Key Laboratory of Photovoltaic Materials, Center for Topological Functional Materials, Henan University, Kaifeng 475004, People's Republic of China.
Phys Chem Chem Phys. 2023 Feb 15;25(7):5453-5458. doi: 10.1039/d2cp05417e.
Memristors as electronic artificial synapses have attracted increasing attention in neuromorphic computing. Especially, organic small molecule artificial synapses show great promise for low-energy neuromorphic devices. In this study, the basic functions of biological synapses including paired-pulse facilitation/paired-pulse depression (PPF/PPD), spike rate-dependent plasticity (SRDP) and fast Bienenstock-Cooper-Munro learning rules (BCM) have been successfully simulated in the 1,4-diphenylbutadiyne (DPDA) memristor device. Furthermore, ultra-low energy consumption (∼25 fJ per spike), linear and large conductance changes have been obtained in the small molecule DPDA device. This work makes a great contribution to improve the accuracy, speed and to reduce the energy consumption for neuromorphic computing.
忆阻器作为电子人工突触在神经形态计算中受到了越来越多的关注。特别是，有机小分子人工突触为低能耗神经形态器件展示了巨大的应用前景。在这项研究中，生物突触的基本功能，包括成对脉冲易化/成对脉冲抑制（PPF/PPD）、尖峰率依赖性可塑性（SRDP）和快速 Bienenstock-Cooper-Munro 学习规则（BCM），已成功地在 1,4-二苯基丁二炔（DPDA）忆阻器器件中得到模拟。此外，在小分子 DPDA 器件中还获得了超低的能量消耗（每个尖峰约 25 fJ）、线性和大的电导变化。这项工作对于提高神经形态计算的准确性、速度和降低能耗做出了重要贡献。
