IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-50783-2.html
   My bibliography  Save this article

A 4096 channel event-based multielectrode array with asynchronous outputs compatible with neuromorphic processors

Author

Listed:
  • Matteo Cartiglia

    (University of Zurich and ETH Zurich)

  • Filippo Costa

    (University of Zurich and ETH Zurich
    University Hospital Zurich)

  • Shyam Narayanan

    (University of Zurich and ETH Zurich)

  • Cat-Vu H. Bui

    (ETH Zurich)

  • Hasan Ulusan

    (ETH Zurich)

  • Nicoletta Risi

    (University of Groningen
    University of Groningen)

  • Germain Haessig

    (University of Zurich and ETH Zurich)

  • Andreas Hierlemann

    (ETH Zurich)

  • Fernando Cardes

    (ETH Zurich)

  • Giacomo Indiveri

    (University of Zurich and ETH Zurich)

Abstract

Bio-signal sensing is pivotal in medical bioelectronics. Traditional methods focus on high sampling rates, leading to large amounts of irrelevant data and high energy consumption. We introduce a self-clocked microelectrode array (MEA) that digitizes bio-signals at the pixel level by encoding changes as asynchronous digital address-events only when they exceed a threshold, significantly reducing off-chip data transmission. This novel MEA comprises a 64 × 64 electrode array, an asynchronous 2D-arbiter, and an Address-Event Representation (AER) communication block. Each pixel operates autonomously, monitoring voltage fluctuations from cellular activity and producing digital pulses for significant changes. Positive and negative signal changes are encoded as “up” and “down” events and are routed off-chip via the asynchronous arbiter. We present results from chip characterization and experimental measurements using electrogenic cells. Additionally, we interface the MEA to a mixed-signal neuromorphic processor, demonstrating a prototype for end-to-end event-based bio-signal sensing and processing.

Suggested Citation

  • Matteo Cartiglia & Filippo Costa & Shyam Narayanan & Cat-Vu H. Bui & Hasan Ulusan & Nicoletta Risi & Germain Haessig & Andreas Hierlemann & Fernando Cardes & Giacomo Indiveri, 2024. "A 4096 channel event-based multielectrode array with asynchronous outputs compatible with neuromorphic processors," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50783-2
    DOI: 10.1038/s41467-024-50783-2
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-50783-2
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-50783-2?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
    ---><---

    References listed on IDEAS

    as
    1. Chiara Bartolozzi & Giacomo Indiveri & Elisa Donati, 2022. "Embodied neuromorphic intelligence," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. David Tsai & Daniel Sawyer & Adrian Bradd & Rafael Yuste & Kenneth L. Shepard, 2017. "A very large-scale microelectrode array for cellular-resolution electrophysiology," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
    3. Chiara Bartolozzi & Giacomo Indiveri & Elisa Donati, 2022. "Author Correction: Embodied neuromorphic intelligence," Nature Communications, Nature, vol. 13(1), pages 1-1, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shengbo Wang & Shuo Gao & Chenyu Tang & Edoardo Occhipinti & Cong Li & Shurui Wang & Jiaqi Wang & Hubin Zhao & Guohua Hu & Arokia Nathan & Ravinder Dahiya & Luigi Giuseppe Occhipinti, 2024. "Memristor-based adaptive neuromorphic perception in unstructured environments," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Qian Li & Ting Tan & Benlong Wang & Zhimiao Yan, 2024. "Avian-inspired embodied perception in biohybrid flapping-wing robotics," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    3. Simone D’Agostino & Filippo Moro & Tristan Torchet & Yiğit Demirağ & Laurent Grenouillet & Niccolò Castellani & Giacomo Indiveri & Elisa Vianello & Melika Payvand, 2024. "DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    4. Yue Yang & Fangduo Zhu & Xumeng Zhang & Pei Chen & Yongzhou Wang & Jiaxue Zhu & Yanting Ding & Lingli Cheng & Chao Li & Hao Jiang & Zhongrui Wang & Peng Lin & Tuo Shi & Ming Wang & Qi Liu & Ningsheng , 2024. "Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    5. Man Yao & Ole Richter & Guangshe Zhao & Ning Qiao & Yannan Xing & Dingheng Wang & Tianxiang Hu & Wei Fang & Tugba Demirci & Michele Marchi & Lei Deng & Tianyi Yan & Carsten Nielsen & Sadique Sheik & C, 2024. "Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    6. Ningning Bai & Yiheng Xue & Shuiqing Chen & Lin Shi & Junli Shi & Yuan Zhang & Xingyu Hou & Yu Cheng & Kaixi Huang & Weidong Wang & Jin Zhang & Yuan Liu & Chuan Fei Guo, 2023. "A robotic sensory system with high spatiotemporal resolution for texture recognition," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    7. Romain Beaubois & Jérémy Cheslet & Tomoya Duenki & Giuseppe De Venuto & Marta Carè & Farad Khoyratee & Michela Chiappalone & Pascal Branchereau & Yoshiho Ikeuchi & Timothée Levi, 2024. "BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    8. Imke Krauhausen & Sophie Griggs & Iain McCulloch & Jaap M. J. Toonder & Paschalis Gkoupidenis & Yoeri Burgt, 2024. "Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    9. Elisa Donati & Giacomo Valle, 2024. "Neuromorphic hardware for somatosensory neuroprostheses," Nature Communications, Nature, vol. 15(1), pages 1-18, 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:15:y:2024:i:1:d:10.1038_s41467-024-50783-2. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.