Artificial neural pathway based on a memristor synapse for optically mediated motion learning

• Main Authors: He, Ke, Liu, Yaqing, Yu, Jiancan, Guo, Xintong, Wang, Ming, Zhang, Liandong, Wan, Changjin, Wang, Ting, Zhou, Changjiu, Chen, Xiaodong
• Other Authors: School of Materials Science and Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics; Synaptic Device; Zeolitic Imidazolate Framework
• Online Access: https://hdl.handle.net/10356/161555
• Institution: Nanyang Technological University

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.