Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Adv Mater. 2020 Nov;32(46):e2004398. doi: 10.1002/adma.202004398. Epub 2020 Oct 15.
The sensory nervous system (SNS) builds up the association between external stimuli and the response of organisms. In this system, habituation is a fundamental characteristic that filters out irrelevantly repetitive information and makes the SNS adapt to the external environment. To emulate this critical process in electronic devices, a Li SiO -based memristor (TiN/Li SiO /Pt) is developed where the temporal response under repetitive stimulation is similar to that of habituation. By connecting this synaptic device to a leaky integrate-and-fire neuron based on a Ag/SiO :Ag/Au memristor, a fully memristive SNS with habituation is experimentally demonstrated. Finally, a habituation spiking neural network based on the SNS is built and its application in obstacle avoidance for robot navigation is successfully presented. The results provide that a direct emulation of the biologically inspired learning process by memristors could be a sound choice for neuromorphic hardware implementation.
感觉神经系统 (SNS) 建立了外部刺激与生物体反应之间的联系。在这个系统中，习惯化是一种基本特征，它可以过滤掉无关的重复信息，使 SNS 适应外部环境。为了在电子设备中模拟这一关键过程，开发了一种基于 LiSiO 的忆阻器 (TiN/LiSiO /Pt)，其在重复刺激下的时间响应类似于习惯化。通过将这个突触器件连接到一个基于 Ag/SiO :Ag/Au 忆阻器的漏积分放电神经元上，实验证明了具有习惯化功能的全忆阻 SNS。最后，构建了一个基于 SNS 的习惯化尖峰神经网络，并成功地将其应用于机器人导航的避障。结果表明，忆阻器对生物启发学习过程的直接模拟可能是神经形态硬件实现的一个不错选择。
