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Title: Artificial neural pathway based on a memristor synapse for optically mediated motion learning
Authors: He, Ke
Liu, Yaqing
Yu, Jiancan
Guo, Xintong
Wang, Ming
Zhang, Liandong
Wan, Changjin
Wang, Ting
Zhou, Changjiu
Chen, Xiaodong
Keywords: Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics
Issue Date: 2022
Source: He, K., Liu, Y., Yu, J., Guo, X., Wang, M., Zhang, L., Wan, C., Wang, T., Zhou, C. & Chen, X. (2022). Artificial neural pathway based on a memristor synapse for optically mediated motion learning. ACS Nano, 16(6), 9691-9700.
Project: A18A1b0045 
Journal: ACS Nano 
Abstract: Animals execute intelligent and efficient interactions with their surroundings through neural pathways, exhibiting learning, memory, and cognition. Artificial autonomous devices that generate self-optimizing feedback mimicking biological systems are essential in pursuing future intelligent robots. Here, we report an artificial neural pathway (ANP) based on a memristor synapse to emulate neuromorphic learning behaviors. In our ANP, optical stimulations are detected and converted into electrical signals through a flexible perovskite photoreceptor. The acquired electrical signals are further processed in a zeolitic imidazolate frameworks-8 (ZIF-8)-based memristor device. By controlling the growth of the ZIF-8 nanoparticles, the conductance of the memristor can be finely modulated with electrical stimulations to mimic the modulation of synaptic plasticity. The device is employed in the ANP to implement synaptic functions of learning and memory. Subsequently, the synaptic feedbacks are used to direct a robotic arm to perform responding motions. Upon repeatedly "reviewing" the optical stimulation, the ANP is able to learn, memorize, and complete the specific motions. This work provides a promising strategy toward the design of intelligent autonomous devices and bioinspired robots through memristor-based systems.
ISSN: 1936-0851
DOI: 10.1021/acsnano.2c03100
Schools: School of Materials Science and Engineering 
Organisations: Institute of Materials Research and Engineering, A*STAR 
Research Centres: Innovative Centre for Flexible Devices 
Rights: This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Nano, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MSE Journal Articles

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