Evidence map›Paper›PMID 42426011›Full record

ArticleNature communications2026

Realization of the Bienenstock-Cooper-Munro rule in a single memristor.

Jiangshun Huang, Anping Huang, Qiaofeng Yang, Pengzhan Li, Qin Gao, Mei Wang, Zhisong Xiao, Xingwang Zhang, Juan Gao, Xueli Geng and 5 more

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Jiangshun HuangSchool of Physics, Beihang University, Beijing, 100191, China.
Anping HuangSchool of Physics, Beihang University, Beijing, 100191, China. aphuang@buaa.edu.cn.ORCID http://orcid.org/0000-0002-1365-3575
Qiaofeng YangSchool of Physics, Beihang University, Beijing, 100191, China.
Pengzhan LiSchool of Physics, Beihang University, Beijing, 100191, China.
Qin GaoSchool of Applied Science, Beijing Information Science & Technology University, Beijing, 102206, China.
Mei WangSchool of Physics, Beihang University, Beijing, 100191, China.
Zhisong XiaoSchool of Physics, Beihang University, Beijing, 100191, China.
Xingwang ZhangState Key Laboratory of Semiconductor Physics and Chip Technologies, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China. xwzhang@semi.ac.cn.ORCID http://orcid.org/0000-0001-7873-5566
Juan GaoSchool of Physics, Beihang University, Beijing, 100191, China.
Xueli GengSchool of Physics, Beihang University, Beijing, 100191, China.
Limin LiuSchool of Physics, Beihang University, Beijing, 100191, China.ORCID http://orcid.org/0000-0003-3925-5310
Yi DuSchool of Physics, Beihang University, Beijing, 100191, China.ORCID http://orcid.org/0000-0003-1932-6732
Ruifeng LuShanghai Institute of Satellite Engineering, Shanghai, 201109, China.
Paul K ChuDepartment of Physics, Department of Materials Science and Engineering, and Department of Biomedical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China.ORCID http://orcid.org/0000-0002-5581-4883
Zengfeng DiState Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China. zfdi@mail.sim.ac.cn.ORCID http://orcid.org/0000-0002-9357-5107

Funding

National Natural Science Foundation of China (National Science Foundation of China) 52473251
6 · The paper itself

Abstract

Neuromorphic computing aims to build electronic systems that mimic the brain's remarkable efficiency and adaptability. A key feature of brain is homeostatic plasticity, which stabilizes neural activity; the Bienenstock-Cooper-Munro (BCM) rule accurately describes this by adjusting a neuron's sensitivity threshold based on past activity. However, implementing the homeostatic rule in synaptic device remains challenging. Here we show that a CuInP₂S₆ memristor naturally realizes the complete BCM rule. We reveal that the device's intrinsic junction capacitance generates reverse spikes that cause spike-rate-dependent conductance depression, combining with second-order ionic dynamics to replicate key BCM features. Through simulations, we demonstrate that adding a similar capacitance can universally enable this behavior in conventional memristors. Finally, we build a physical reservoir computing system based on this strategy that exhibits robust, noise-tolerant spatiotemporal processing. This work provides a viable path for deploying adaptive homeostatic plasticity in hardware, enhancing the reliability of neuromorphic systems.

Identifiers

PMID42426011
PMCPMC13478535

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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.