IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v11y2020i1d10.1038_s41467-020-15105-2.html
   My bibliography  Save this article

Tactile sensory coding and learning with bio-inspired optoelectronic spiking afferent nerves

Author

Listed:
  • Hongwei Tan

    (Aalto University School of Science)

  • Quanzheng Tao

    (Linköping University)

  • Ishan Pande

    (Aalto University School of Science)

  • Sayani Majumdar

    (Aalto University School of Science
    VTT Technical Research Centre of Finland Ltd.)

  • Fu Liu

    (Aalto University)

  • Yifan Zhou

    (Aalto University School of Science)

  • Per O. Å. Persson

    (Linköping University)

  • Johanna Rosen

    (Linköping University)

  • Sebastiaan van Dijken

    (Aalto University School of Science)

Abstract

The integration and cooperation of mechanoreceptors, neurons and synapses in somatosensory systems enable humans to efficiently sense and process tactile information. Inspired by biological somatosensory systems, we report an optoelectronic spiking afferent nerve with neural coding, perceptual learning and memorizing capabilities to mimic tactile sensing and processing. Our system senses pressure by MXene-based sensors, converts pressure information to light pulses by coupling light-emitting diodes to analog-to-digital circuits, then integrates light pulses using a synaptic photomemristor. With neural coding, our spiking nerve is capable of not only detecting simultaneous pressure inputs, but also recognizing Morse code, braille, and object movement. Furthermore, with dimensionality-reduced feature extraction and learning, our system can recognize and memorize handwritten alphabets and words, providing a promising approach towards e-skin, neurorobotics and human-machine interaction technologies.

Suggested Citation

  • Hongwei Tan & Quanzheng Tao & Ishan Pande & Sayani Majumdar & Fu Liu & Yifan Zhou & Per O. Å. Persson & Johanna Rosen & Sebastiaan van Dijken, 2020. "Tactile sensory coding and learning with bio-inspired optoelectronic spiking afferent nerves," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15105-2
    DOI: 10.1038/s41467-020-15105-2
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-020-15105-2
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-020-15105-2?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. Fakun Wang & Fangchen Hu & Mingjin Dai & Song Zhu & Fangyuan Sun & Ruihuan Duan & Chongwu Wang & Jiayue Han & Wenjie Deng & Wenduo Chen & Ming Ye & Song Han & Bo Qiang & Yuhao Jin & Yunda Chua & Nan C, 2023. "A two-dimensional mid-infrared optoelectronic retina enabling simultaneous perception and encoding," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    3. Zhongfang Zhang & Xiaolong Zhao & Xumeng Zhang & Xiaohu Hou & Xiaolan Ma & Shuangzhu Tang & Ying Zhang & Guangwei Xu & Qi Liu & Shibing Long, 2022. "In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    4. Hongwei Tan & Sebastiaan van Dijken, 2023. "Dynamic machine vision with retinomorphic photomemristor-reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    5. 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.
    6. 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.

    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:11:y:2020:i:1:d:10.1038_s41467-020-15105-2. 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.