Cai Jia-Wei, Ye Jing-Ting, Zhong Ya-Nan, Zhang Zhong-Da, Zong Hao, Li Li-Xing, Han Xue-Er, Xu Jian-Long, Gao Xu, Lee Shuit-Tong, Wang Sui-Dong
Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China.
Macao Institute of Materials Science and Engineering (MIMSE), MUST-SUDA Joint Research Center for Advanced Functional Materials, Macau University of Science and Technology, Taipa 999078, Macao, P. R. China.
ACS Appl Mater Interfaces. 2024 Sep 11;16(36):47996-48004. doi: 10.1021/acsami.4c09593. Epub 2024 Sep 2.
In the vanguard of neuromorphic engineering, we develop a paradigm of biocompatible polymer memcapacitors using a seamless solution process, unleashing comprehensive synaptic capabilities depending on both the stimulation form and history. Like the human brain to learn and adapt, the memcapacitors exhibit analogue-type and evolvable capacitance shifts that mirror the complex flexibility of synaptic strengthening and weakening. With increasing frequency and intensity of the stimulation, the memcapacitors demonstrate an evolution from short-term plasticity (STP) to long-term plasticity (LTP), and even to metaplasticity (MP) at a higher level. A physical picture, featuring the stimulus-controlled spatiotemporal ion redistribution in the polymer, elaborates the origin of the memcapacitive prowess and resultant versatile synaptic plasticity. The distinctive MP behavior endows the memcapacitors with a dynamic learning rate (LR), which is utilized in an artificial neural network. The superiority of implementing a dynamic LR compared with conventional practices of using constant LR shines light on the potential of the memcapacitors to exploit organic neuromorphic computing hardware.
在神经形态工程的前沿领域,我们采用无缝溶液工艺开发了一种生物相容性聚合物忆阻器范式,它能根据刺激形式和历史展现出全面的突触功能。就像人类大脑学习和适应一样,忆阻器呈现出类似模拟类型且可演化的电容变化,反映了突触增强和减弱的复杂灵活性。随着刺激频率和强度的增加,忆阻器表现出从短期可塑性(STP)到长期可塑性(LTP)的演变,甚至在更高水平上发展为元可塑性(MP)。一幅描绘聚合物中受刺激控制的时空离子重新分布的物理图景,阐述了忆阻能力的起源以及由此产生的通用突触可塑性。独特的MP行为赋予忆阻器动态学习率(LR),该学习率被应用于人工神经网络。与使用恒定学习率的传统做法相比,实施动态学习率的优势凸显了忆阻器在开发有机神经形态计算硬件方面的潜力。