Evidence map›Paper›PMID 42431950›Full record

ArticleNature communications2026

Cerebellum-inspired memtransistors enable emergent differentiation for hardware-efficient novelty detection.

Min-A Kang, Spencer T Brown, Nethmi Jayasinghe, Meghana R Holla, Thang T Pham, Thomas T Zeng, Ruiqin Wu, Zachary J Trdinich, Xudong Zhuang, Vinayak P Dravid and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Min-A Kang *Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA.ORCID http://orcid.org/0000-0002-1661-3771
Spencer T Brown *Department of Neurobiology, Northwestern University, Evanston, IL, USA.
Nethmi Jayasinghe *Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, USA.ORCID http://orcid.org/0009-0001-6316-5959
Meghana R HollaDepartment of Neurobiology, Northwestern University, Evanston, IL, USA.
Thang T PhamDepartment of Materials Science and Engineering, Northwestern University, Evanston, IL, USA.ORCID http://orcid.org/0000-0001-5940-6206
Thomas T ZengDepartment of Materials Science and Engineering, Northwestern University, Evanston, IL, USA.ORCID http://orcid.org/0009-0002-6374-1175
Ruiqin WuDepartment of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA.
Zachary J TrdinichDepartment of Materials Science and Engineering, Northwestern University, Evanston, IL, USA.
Xudong ZhuangDepartment of Materials Science and Engineering, Northwestern University, Evanston, IL, USA.ORCID http://orcid.org/0009-0008-8643-8412
Vinayak P DravidDepartment of Materials Science and Engineering, Northwestern University, Evanston, IL, USA.ORCID http://orcid.org/0000-0002-6007-3063
Indira M RamanDepartment of Neurobiology, Northwestern University, Evanston, IL, USA. i-raman@northwestern.edu.ORCID http://orcid.org/0000-0001-5245-8177
Amit R TrivediDepartment of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, USA. amitrt@uic.edu.ORCID http://orcid.org/0000-0001-5436-7922
Vinod K SangwanDepartment of Materials Science and Engineering, Northwestern University, Evanston, IL, USA. vinod.sangwan@northwestern.edu.ORCID http://orcid.org/0000-0002-5623-5285
Mark C HersamDepartment of Materials Science and Engineering, Northwestern University, Evanston, IL, USA. m-hersam@northwestern.edu.ORCID http://orcid.org/0000-0003-4120-1426

Funding

National Science Foundation (NSF) DMR-2308691National Science Foundation (NSF) ECCS-2025633National Science Foundation (NSF) EFMA-2317974U.S. Department of Energy (DOE) DE-AC02-06CH11357
6 · The paper itself

Abstract

Artificial intelligence (AI) algorithms are currently executed using silicon-based hardware, resulting in excessively high energy demand for data centers. Edge computing AI for healthcare, robotics, and autonomous vehicles presents even stricter power and latency constraints, which are unmet by incumbent computing architectures. With vastly superior energy efficiency, biological neuronal networks provide hints towards alternative computational approaches including memory-logic colocation, asynchronous parallelism, and spike-triggered computation. Here, we draw inspiration from the cerebellum to demonstrate asymmetric-contact-gated MoS

Indexed as

Artificial IntelligenceCerebellumTransistors, ElectronicAlgorithmsAnimalsHumans

Identifiers

PMID42431950
PMCPMC13483114

What OpenQuestion holds

Textmetadata
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.