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A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system

Author

Listed:
  • Rui Yuan

    (Peking University)

  • Qingxi Duan

    (Peking University)

  • Pek Jun Tiw

    (Peking University)

  • Ge Li

    (Chinese Academy of Sciences)

  • Zhuojian Xiao

    (Peking University)

  • Zhaokun Jing

    (Peking University)

  • Ke Yang

    (Peking University)

  • Chang Liu

    (Peking University)

  • Chen Ge

    (Chinese Academy of Sciences)

  • Ru Huang

    (Peking University
    Peking University
    Chinese Institute for Brain Research (CIBR), Beijing)

  • Yuchao Yang

    (Peking University
    Peking University
    Chinese Institute for Brain Research (CIBR), Beijing
    Beijing Academy of Artificial Intelligence)

Abstract

Neuromorphic perception systems inspired by biology have tremendous potential in efficiently processing multi-sensory signals from the physical world, but a highly efficient hardware element capable of sensing and encoding multiple physical signals is still lacking. Here, we report a spike-based neuromorphic perception system consisting of calibratable artificial sensory neurons based on epitaxial VO2, where the high crystalline quality of VO2 leads to significantly improved cycle-to-cycle uniformity. A calibration resistor is introduced to optimize device-to-device consistency, and to adapt the VO2 neuron to different sensors with varied resistance level, a scaling resistor is further incorporated, demonstrating cross-sensory neuromorphic perception component that can encode illuminance, temperature, pressure and curvature signals into spikes. These components are utilized to monitor the curvatures of fingers, thereby achieving hand gesture classification. This study addresses the fundamental cycle-to-cycle and device-to-device variation issues of sensory neurons, therefore promoting the construction of neuromorphic perception systems for e-skin and neurorobotics.

Suggested Citation

  • Rui Yuan & Qingxi Duan & Pek Jun Tiw & Ge Li & Zhuojian Xiao & Zhaokun Jing & Ke Yang & Chang Liu & Chen Ge & Ru Huang & Yuchao Yang, 2022. "A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31747-w
    DOI: 10.1038/s41467-022-31747-w
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    References listed on IDEAS

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    Cited by:

    1. Chang Liu & Pek Jun Tiw & Teng Zhang & Yanghao Wang & Lei Cai & Rui Yuan & Zelun Pan & Wenshuo Yue & Yaoyu Tao & Yuchao Yang, 2024. "VO2 memristor-based frequency converter with in-situ synthesize and mix for wireless internet-of-things," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Rui Yuan & Pek Jun Tiw & Lei Cai & Zhiyu Yang & Chang Liu & Teng Zhang & Chen Ge & Ru Huang & Yuchao Yang, 2023. "A neuromorphic physiological signal processing system based on VO2 memristor for next-generation human-machine interface," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Yuan-Hang Zhang & Chesson Sipling & Erbin Qiu & Ivan K. Schuller & Massimiliano Di Ventra, 2024. "Collective dynamics and long-range order in thermal neuristor networks," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    4. Muhtasim Ul Karim Sadaf & Najam U Sakib & Andrew Pannone & Harikrishnan Ravichandran & Saptarshi Das, 2023. "A bio-inspired visuotactile neuron for multisensory integration," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Ke Yang & Yanghao Wang & Pek Jun Tiw & Chaoming Wang & Xiaolong Zou & Rui Yuan & Chang Liu & Ge Li & Chen Ge & Si Wu & Teng Zhang & Ru Huang & Yuchao Yang, 2024. "High-order sensory processing nanocircuit based on coupled VO2 oscillators," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    6. Zhiyuan Li & Zhongshao Li & Wei Tang & Jiaping Yao & Zhipeng Dou & Junjie Gong & Yongfei Li & Beining Zhang & Yunxiao Dong & Jian Xia & Lin Sun & Peng Jiang & Xun Cao & Rui Yang & Xiangshui Miao & Ron, 2024. "Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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