Evidence map›Paper›PMID 40523910›Full record

ArticleCommunications engineering2025

Super-Turing synaptic resistor circuits for intelligent morphing wing.

Atharva Deo, Jungmin Lee, Dawei Gao, Rahul Shenoy, Kevin Pt Haughn, Zixuan Rong, Yong Hei, D Qiao, Tanay Topac, Fu-Kuo Chang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications engineering, 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

12 authors.

Atharva DeoDepartments of Mechanical and Aerospace Engineering, Electrical and Computer Engineering, Materials Science and Engineering, California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA.
Jungmin LeeDepartments of Mechanical and Aerospace Engineering, Electrical and Computer Engineering, Materials Science and Engineering, California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA.
Dawei GaoDepartments of Mechanical and Aerospace Engineering, Electrical and Computer Engineering, Materials Science and Engineering, California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA.
Rahul ShenoyDepartments of Mechanical and Aerospace Engineering, Electrical and Computer Engineering, Materials Science and Engineering, California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA.
Kevin Pt HaughnDepartment of Aerospace Engineering, University of Michigan, Ann Arbor, MI, 48104, USA.ORCID http://orcid.org/0000-0002-8219-8983
Zixuan RongDepartments of Mechanical and Aerospace Engineering, Electrical and Computer Engineering, Materials Science and Engineering, California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA.
Yong HeiDepartments of Mechanical and Aerospace Engineering, Electrical and Computer Engineering, Materials Science and Engineering, California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA.
D QiaoDepartments of Mechanical and Aerospace Engineering, Electrical and Computer Engineering, Materials Science and Engineering, California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA.ORCID http://orcid.org/0000-0001-5001-9074
Tanay TopacDepartment of Aeronautics and Astronautics, Stanford University, Stanford, CA, 94305, USA.ORCID http://orcid.org/0000-0001-8304-0910
Fu-Kuo ChangDepartment of Aeronautics and Astronautics, Stanford University, Stanford, CA, 94305, USA.
Daniel J InmanDepartment of Aerospace Engineering, University of Michigan, Ann Arbor, MI, 48104, USA.ORCID http://orcid.org/0000-0001-6195-1334
Yong ChenDepartments of Mechanical and Aerospace Engineering, Electrical and Computer Engineering, Materials Science and Engineering, California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA. yongchen@seas.ucla.edu.ORCID http://orcid.org/0000-0002-4846-7131

Funding

United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (AF Office of Scientific Research) FA9550-23-1-0638United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (AF Office of Scientific Research) FA9550-24-1-0322
6 · The paper itself

Abstract

Neurobiological circuits in the brain, operating in Super-Turing mode, process information while simultaneously modifying their synaptic connections through learning, allowing them to dynamically adapt to changes. In contrast, artificial intelligence systems based on computers operate in Turing mode and lack the ability to concurrently infer and learn, making them vulnerable to failure under dynamically changing conditions. Here we show a synaptic resistor circuit that operates in Super-Turing mode, enabling concurrent learning and inference. The circuit controls a morphing wing to reduce its drag-to-lift force ratio and recover from stalls in complex aerodynamic environments. The synaptic resistor circuit demonstrates superior performance, faster learning speeds, enhanced adaptability, and reduced power consumption compared to artificial neural networks and human operators on the same task. By overcoming the fundamental limitations of computers, synaptic resistor circuits offer high-speed concurrent learning and inference, ultra-low power consumption, error correction, and agile adaptability for artificial intelligence systems.

Identifiers

PMID40523910
PMCPMC12170896

What OpenQuestion holds

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LicenceCC BY
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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.