Article in Science advances, 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.
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
17 authors.
Jungmin LeeDepartments of Materials Science and Engineering, Mechanical and Aerospace Engineering, Electrical and Computer Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0001-6318-4554
Rahul ShenoyDepartments of Materials Science and Engineering, Mechanical and Aerospace Engineering, Electrical and Computer Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0003-1355-1162
Atharva DeoDepartments of Materials Science and Engineering, Mechanical and Aerospace Engineering, Electrical and Computer Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0002-2999-7030
Suin YiDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.ORCID 0000-0001-7957-8953
Dawei GaoDepartments of Materials Science and Engineering, Mechanical and Aerospace Engineering, Electrical and Computer Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0002-5525-8717
David QiaoDepartments of Materials Science and Engineering, Mechanical and Aerospace Engineering, Electrical and Computer Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0001-5001-9074
Mingjie XuIrvine Materials Research Institute, University of California, Irvine, Irvine, CA 92697, USA.ORCID 0000-0001-8334-6936
Shiva AsapuDepartment of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003, USA.ORCID 0000-0002-6041-2619
Zixuan RongDepartments of Materials Science and Engineering, Mechanical and Aerospace Engineering, Electrical and Computer Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Dhruva NathanDepartments of Materials Science and Engineering, Mechanical and Aerospace Engineering, Electrical and Computer Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0002-1670-2117
Yong HeiDepartments of Materials Science and Engineering, Mechanical and Aerospace Engineering, Electrical and Computer Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Dharma PaladuguDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
Jian-Guo ZhengIrvine Materials Research Institute, University of California, Irvine, Irvine, CA 92697, USA.ORCID 0000-0001-6610-9162
J Joshua YangDepartment of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA.ORCID 0000-0001-8242-7531
R Stanley WilliamsDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.ORCID 0000-0003-0213-4259
Qing WuAir Force Research Lab, Information Directorate, Rome, NY 13441, USA.ORCID 0000-0001-7248-9755
Yong ChenDepartments of Materials Science and Engineering, Mechanical and Aerospace Engineering, Electrical and Computer Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0002-4846-7131
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
Computers based on the Turing model execute artificial intelligence (AI) algorithms that are either programmed by humans or derived from machine learning. These AI algorithms cannot be modified during the operation process according to environmental changes, resulting in significantly poorer adaptability to new environments, longer learning latency, and higher power consumption compared to the human brain. In contrast, neurobiological circuits can function while simultaneously adapting to changing conditions. Here, we present a brain-inspired Super-Turing AI model based on a synaptic resistor circuit, capable of concurrent real-time inference and learning. Without any prior learning, a circuit of synaptic resistors integrating ferroelectric HfZrO materials was demonstrated to navigate a drone toward a target position while avoiding obstacles in a simulated environment, exhibiting significantly superior learning speed, performance, power consumption, and adaptability compared to computer-based artificial neural networks. Synaptic resistor circuits enable efficient and adaptive Super-Turing AI systems in uncertain and dynamic real-world environments.
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.
HfZrO-based synaptic resistor circuit for a Super-Turing intelligent system. · full record | OpenQuestion