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Bioinspired bio-voltage memristors

Author

Listed:
  • Tianda Fu

    (University of Massachusetts)

  • Xiaomeng Liu

    (University of Massachusetts)

  • Hongyan Gao

    (University of Massachusetts)

  • Joy E. Ward

    (University of Massachusetts)

  • Xiaorong Liu

    (University of Massachusetts)

  • Bing Yin

    (University of Massachusetts)

  • Zhongrui Wang

    (University of Massachusetts)

  • Ye Zhuo

    (University of Massachusetts)

  • David J. F. Walker

    (University of Massachusetts)

  • J. Joshua Yang

    (University of Massachusetts)

  • Jianhan Chen

    (University of Massachusetts
    University of Massachusetts
    University of Massachusetts)

  • Derek R. Lovley

    (University of Massachusetts
    University of Massachusetts)

  • Jun Yao

    (University of Massachusetts
    University of Massachusetts)

Abstract

Memristive devices are promising candidates to emulate biological computing. However, the typical switching voltages (0.2-2 V) in previously described devices are much higher than the amplitude in biological counterparts. Here we demonstrate a type of diffusive memristor, fabricated from the protein nanowires harvested from the bacterium Geobacter sulfurreducens, that functions at the biological voltages of 40-100 mV. Memristive function at biological voltages is possible because the protein nanowires catalyze metallization. Artificial neurons built from these memristors not only function at biological action potentials (e.g., 100 mV, 1 ms) but also exhibit temporal integration close to that in biological neurons. The potential of using the memristor to directly process biosensing signals is also demonstrated.

Suggested Citation

  • Tianda Fu & Xiaomeng Liu & Hongyan Gao & Joy E. Ward & Xiaorong Liu & Bing Yin & Zhongrui Wang & Ye Zhuo & David J. F. Walker & J. Joshua Yang & Jianhan Chen & Derek R. Lovley & Jun Yao, 2020. "Bioinspired bio-voltage memristors," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15759-y
    DOI: 10.1038/s41467-020-15759-y
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    Cited by:

    1. Shuzhi Liu & Jianmin Zeng & Zhixin Wu & Han Hu & Ao Xu & Xiaohe Huang & Weilin Chen & Qilai Chen & Zhe Yu & Yinyu Zhao & Rong Wang & Tingting Han & Chao Li & Pingqi Gao & Hyunwoo Kim & Seung Jae Baik , 2023. "An ultrasmall organic synapse for neuromorphic computing," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. Yan Wang & Yue Gong & Shenming Huang & Xuechao Xing & Ziyu Lv & Junjie Wang & Jia-Qin Yang & Guohua Zhang & Ye Zhou & Su-Ting Han, 2021. "Memristor-based biomimetic compound eye for real-time collision detection," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    3. Dong Gue Roe & Dong Hae Ho & Yoon Young Choi & Young Jin Choi & Seongchan Kim & Sae Byeok Jo & Moon Sung Kang & Jong-Hyun Ahn & Jeong Ho Cho, 2023. "Humanlike spontaneous motion coordination of robotic fingers through spatial multi-input spike signal multiplexing," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    4. Shao, Yan & Wu, Fuqiang & Wang, Qingyun, 2024. "Dynamics and stability of neural systems with indirect interactions involved energy levels," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    5. 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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