Evidence map›Paper›PMID 38821961›Full record

ArticleNature communications2024

Memristor-based adaptive neuromorphic perception in unstructured environments.

Shengbo Wang, Shuo Gao, Chenyu Tang, Edoardo Occhipinti, Cong Li, Shurui Wang, Jiaqi Wang, Hubin Zhao, Guohua Hu, Arokia Nathan and 2 more

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 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Review
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  16. Nature-Inspired Innovation in Electrical Engineering Technologies and Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  17. Article
  18. Additive Manufacturing of Neuromorphic Systems.Advanced materials (Deerfield Beach, Fla.) · 2025
    Review
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  20. 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

12 authors.

Shengbo Wang *School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.ORCID http://orcid.org/0000-0003-1212-138X
Shuo Gao *School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China. shuo_gao@buaa.edu.cn.ORCID http://orcid.org/0000-0003-3096-4700
Chenyu TangDepartment of Engineering, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0002-6368-5639
Edoardo OcchipintiUKRI Centre for Doctoral Training in AI for Healthcare, Department of Computing, Imperial College London, London, UK.ORCID http://orcid.org/0000-0003-4691-9007
Cong LiSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.
Shurui WangSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.
Jiaqi WangSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.
Hubin ZhaoHUB of Intelligent Neuro-engineering (HUBIN), CREATe, Division of Surgery and Interventional Science, UCL, HA7 4LP, Stanmore, UK.ORCID http://orcid.org/0000-0001-9408-4724
Guohua HuDepartment of Electronic Engineering, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong S. A. R., China.ORCID http://orcid.org/0000-0001-9296-1236
Arokia NathanDarwin College, University of Cambridge, Cambridge, UK.
Ravinder DahiyaBendable Electronics and Sustainable Technologies (BEST) Group, Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA.ORCID http://orcid.org/0000-0002-3858-3841
Luigi Giuseppe OcchipintiDepartment of Engineering, University of Cambridge, Cambridge, UK. lgo23@cam.ac.uk.ORCID http://orcid.org/0000-0002-9067-2534

Funding

Beihang University (Beijing University of Aeronautics and Astronautics) KG161250Beihang University (Beijing University of Aeronautics and Astronautics) ZG16S2103RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/K03099X/1RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/P027628/1RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/S023283/1RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/W024284/1
6 · The paper itself

Abstract

Efficient operation of control systems in robotics or autonomous driving targeting real-world navigation scenarios requires perception methods that allow them to understand and adapt to unstructured environments with good accuracy, adaptation, and generality, similar to humans. To address this need, we present a memristor-based differential neuromorphic computing, perceptual signal processing, and online adaptation method providing neuromorphic style adaptation to external sensory stimuli. The adaptation ability and generality of this method are confirmed in two application scenarios: object grasping and autonomous driving. In the former, a robot hand realizes safe and stable grasping through fast ( ~ 1 ms) adaptation based on the tactile object features with a single memristor. In the latter, decision-making information of 10 unstructured environments in autonomous driving is extracted with an accuracy of 94% with a 40×25 memristor array. By mimicking human low-level perception mechanisms, the electronic neuromorphic circuit-based method achieves real-time adaptation and high-level reactions to unstructured environments.

Identifiers

PMID38821961
PMCPMC11143376

What OpenQuestion holds

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Registered trials

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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.