Evidence map›Paper›PMID 37061543›Full record

ArticleNature communications2023

Dynamic machine vision with retinomorphic photomemristor-reservoir computing.

Hongwei Tan, Sebastiaan van Dijken

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
14.3field-weighted citation impact, top 1% of its field
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

24 citing papers in PubMed, 110 citations in OpenAlex.

  1. Article
  2. Interface Engineering for Scalable Optoelectronic Reservoir Computing.Small (Weinheim an der Bergstrasse, Germany) · 2026
    Article
  3. Photo-Induced Valence Changed Memristor Based on WOSmall (Weinheim an der Bergstrasse, Germany) · 2026
    Article
  4. Optoelectronic Nanofluidic Neural Networks for Ionic Computing.Advanced materials (Deerfield Beach, Fla.) · 2026
    Article
  5. Article
  6. Review
  7. Article
  8. Ion Crowding Effect in Unilaterally Downsized Perovskite Memristors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
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  10. Article
  11. Article
  12. Review
  13. Article
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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

2 authors at 1 institution in 1 country.

Hongwei TanNanoSpin, Department of Applied Physics, Aalto University School of Science, P.O. Box 15100, FI-00076, Aalto, Finland. hongwei.tan@aalto.fi.ORCID http://orcid.org/0000-0002-7725-387X
Sebastiaan van DijkenNanoSpin, Department of Applied Physics, Aalto University School of Science, P.O. Box 15100, FI-00076, Aalto, Finland. sebastiaan.van.dijken@aalto.fi.ORCID http://orcid.org/0000-0001-6372-2252
Aalto University · FI

Funding

Academy of Finland (Suomen Akatemia) 13293916Academy of Finland (Suomen Akatemia) 316973
6 · The paper itself

Abstract

Dynamic machine vision requires recognizing the past and predicting the future of a moving object based on present vision. Current machine vision systems accomplish this by processing numerous image frames or using complex algorithms. Here, we report motion recognition and prediction in recurrent photomemristor networks. In our system, a retinomorphic photomemristor array, working as dynamic vision reservoir, embeds past motion frames as hidden states into the present frame through inherent dynamic memory. The informative present frame facilitates accurate recognition of past and prediction of future motions with machine learning algorithms. This in-sensor motion processing capability eliminates redundant data flows and promotes real-time perception of moving objects for dynamic machine vision.

Identifiers

PMID37061543
PMCPMC10105772
OpenAlexW4365815462

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.