IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-23744-2.html
   My bibliography  Save this article

Self-sustained green neuromorphic interfaces

Author

Listed:
  • Tianda Fu

    (University of Massachusetts)

  • Xiaomeng Liu

    (University of Massachusetts)

  • Shuai Fu

    (University of Massachusetts)

  • Trevor Woodard

    (University of Massachusetts)

  • Hongyan Gao

    (University of Massachusetts)

  • Derek R. Lovley

    (University of Massachusetts
    Institute for Applied Life Sciences (IALS), University of Massachusetts)

  • Jun Yao

    (University of Massachusetts
    Institute for Applied Life Sciences (IALS), University of Massachusetts
    University of Massachusetts)

Abstract

Incorporating neuromorphic electronics in bioelectronic interfaces can provide intelligent responsiveness to environments. However, the signal mismatch between the environmental stimuli and driving amplitude in neuromorphic devices has limited the functional versatility and energy sustainability. Here we demonstrate multifunctional, self-sustained neuromorphic interfaces by achieving signal matching at the biological level. The advances rely on the unique properties of microbially produced protein nanowires, which enable both bio-amplitude (e.g.,

Suggested Citation

  • Tianda Fu & Xiaomeng Liu & Shuai Fu & Trevor Woodard & Hongyan Gao & Derek R. Lovley & Jun Yao, 2021. "Self-sustained green neuromorphic interfaces," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23744-2
    DOI: 10.1038/s41467-021-23744-2
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-23744-2
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-021-23744-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Giovanni Maria Matrone & Eveline R. W. Doremaele & Abhijith Surendran & Zachary Laswick & Sophie Griggs & Gang Ye & Iain McCulloch & Francesca Santoro & Jonathan Rivnay & Yoeri Burgt, 2024. "A modular organic neuromorphic spiking circuit for retina-inspired sensory coding and neurotransmitter-mediated neural pathways," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    2. Yang Gao & Yuchen Zhou & Xudong Ji & Austin J. Graham & Christopher M. Dundas & Ismar E. Miniel Mahfoud & Bailey M. Tibbett & Benjamin Tan & Gina Partipilo & Ananth Dodabalapur & Jonathan Rivnay & Ben, 2024. "A hybrid transistor with transcriptionally controlled computation and plasticity," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    3. Tianyu Wang & Jialin Meng & Xufeng Zhou & Yue Liu & Zhenyu He & Qi Han & Qingxuan Li & Jiajie Yu & Zhenhai Li & Yongkai Liu & Hao Zhu & Qingqing Sun & David Wei Zhang & Peining Chen & Huisheng Peng & , 2022. "Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    4. Xiaomeng Liu & Toshiyuki Ueki & Hongyan Gao & Trevor L. Woodard & Kelly P. Nevin & Tianda Fu & Shuai Fu & Lu Sun & Derek R. Lovley & Jun Yao, 2022. "Microbial biofilms for electricity generation from water evaporation and power to wearables," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23744-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.