Evidence map›Paper›PMID 41022738›Full record

ArticleNature communications2025

Constructing artificial neurons with functional parameters comprehensively matching biological values.

Shuai Fu, Hongyan Gao, Siqi Wang, Xiaoyu Wang, Trevor Woodard, Zhien Wang, Jing Kong, Derek R Lovley, Jun Yao

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Shuai FuDepartment of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA.
Hongyan GaoDepartment of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA.ORCID http://orcid.org/0000-0001-7872-0137
Siqi WangDepartment of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA.
Xiaoyu WangDepartment of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA.
Trevor WoodardDepartment of Microbiology, University of Massachusetts, Amherst, MA, USA.
Zhien WangDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-6120-1952
Jing KongDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-0551-1208
Derek R LovleyDepartment of Microbiology, University of Massachusetts, Amherst, MA, USA.ORCID http://orcid.org/0000-0001-7158-3555
Jun YaoDepartment of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA. juny@umass.edu.ORCID http://orcid.org/0000-0002-5269-3190

Funding

Flexible bioelectronics platform converging capillary-like delivery network and sensing feedback network for improving cardiac tissue engineeringR33HL175683 · NHLBI · UNIVERSITY OF MASSACHUSETTS AMHERST · PI YAO, JUN · 2024 to 2025
$959k
Bioinspired mechanogating biosensors for real-time biodetectionR21EB030216 · NIBIB · UNIVERSITY OF MASSACHUSETTS AMHERST · PI YAO, JUN · 2022 to 2024
$577k
Alfred P. Sloan Foundation FG-2022-18452National Science Foundation (NSF) CCSS-2345733Semiconductor Research Corporation (SRC) 145105-21913United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office (ARO) W911NF2210027U.S. Department of Health & Human Services | National Institutes of Health (NIH) R21EB030216U.S. Department of Health & Human Services | National Institutes of Health (NIH) R33HL175683
6 · The paper itself

Abstract

The efficient signal processing in biosystems is largely attributed to the powerful constituent unit of a neuron, which encodes and decodes spatiotemporal information using spiking action potentials of ultralow amplitude and energy. Constructing devices that can emulate neuronal functions is thus considered a promising step toward advancing neuromorphic electronics and enhancing signal flow in bioelectronic interfaces. However, existent artificial neurons often have functional parameters that are distinctly mismatched with their biological counterparts, including signal amplitude and energy levels that are typically an order of magnitude larger. Here, we demonstrate artificial neurons that not only closely emulate biological neurons in functions but also match their parameters in key aspects such as signal amplitude, spiking energy, temporal features, and frequency response. Moreover, these artificial neurons can be modulated by extracellular chemical species in a manner consistent with neuromodulation in biological neurons. We further show that an artificial neuron can connect to a biological cell to process cellular signals in real-time and interpret cell states. These results advance the potential for constructing bio-emulated electronics to improve bioelectronic interface and neuromorphic integration.

Indexed as

NeuronsAction PotentialsAnimalsElectronicsModels, Neurological

Identifiers

PMID41022738
PMCPMC12480988

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.