Evidence map›Paper›PMID 40212323›Full record

ArticleSmall science2022

Nonlinearity in Memristors for Neuromorphic Dynamic Systems.

Ke Yang, J Joshua Yang, Ru Huang, Yuchao Yang

Abstract read
In one paragraph

Article in Small science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. HfOScience advances · 2026
    Article
  5. Review
  6. Li-Well ZnO Memtransistors: High Reliability for Neuromorphic Applications.Advanced materials (Deerfield Beach, Fla.) · 2026
    Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Artificial sensory system based on memristive devices.Exploration (Beijing, China) · 2024
    Review
  17. Article
  18. Article
  19. 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

4 authors.

Ke YangDepartment of Micro/nanoelectronics Peking University Beijing 100871 China.
J Joshua YangElectrical and Computer Engineering Department University of Southern California Los Angeles CA 90089 USA.
Ru HuangDepartment of Micro/nanoelectronics Peking University Beijing 100871 China.
Yuchao YangDepartment of Micro/nanoelectronics Peking University Beijing 100871 China.ORCID 0000-0003-4674-4059

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As semiconductor technology enters the more than Moore era, there exists an apparent contradiction between the rapidly growing demands for data processing and the visible inefficiency rooted in traditional computing architecture. Neuromorphic systems hold great prospects in enabling a new generation of computing paradigm that can address this issue, which demands device components with rich dynamics and nonlinearity. Herein, the nonlinearity in memristive devices and their application in building neuromorphic dynamic systems are reviewed. The internal mechanisms that endow memristive devices with nonlinearity and rich dynamics are reviewed and subsequently the nonlinear spiking neurons that are implemented utilizing the physical processes in memristors are shown. Typical examples on neuromorphic dynamic systems based on nonlinear memristors are summarized, including memristive reservoir, memristive oscillatory neural network, and memristive chaotic computing. Finally, an outlook in the development of neuromorphic dynamic systems is given.

Indexed as

chaoscoupled oscillatory networksneuromorphic dynamic systemsnonlinearity in memristorsreservoir computing

Identifiers

PMID40212323
PMCPMC11936018

What OpenQuestion holds

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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.