Evidence map›Paper›PMID 39169023›Full record

ArticleNature communications2024

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

Matteo Cartiglia, Filippo Costa, Shyam Narayanan, Cat-Vu H Bui, Hasan Ulusan, Nicoletta Risi, Germain Haessig, Andreas Hierlemann, Fernando Cardes, Giacomo Indiveri

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  2. Article
  3. Article
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Matteo CartigliaInstitute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland. camatteo@ini.uzh.ch.ORCID http://orcid.org/0000-0001-8936-6727
Filippo CostaInstitute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.ORCID http://orcid.org/0009-0005-5001-5950
Shyam NarayananInstitute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0001-7601-1644
Cat-Vu H BuiDepartment of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland.
Hasan UlusanDepartment of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0001-8788-8863
Nicoletta RisiBio-Inspired Circuits and Systems Lab, Zernike Institute for Advanced Materials, University of Groningen, Groningen, Netherlands.
Germain HaessigInstitute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
Andreas HierlemannDepartment of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0002-3838-2468
Fernando CardesDepartment of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0003-1880-6734
Giacomo IndiveriInstitute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0002-7109-1689

Funding

EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) neuroXscales'(694829)Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) 205320_188910/1Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) Sinergia project CRSII5-18O316Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) SNF 325230_204651/1Universität Zürich (University of Zurich) Candoc FK-22-084
6 · The paper itself

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.

Identifiers

PMID39169023
PMCPMC11339437

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.