Evidence map›Paper›PMID 42151176›Full record

ArticleNature communications2026

Compact photonic spiking neuron with inherent stochasticity based on phase-change material for probabilistic computing.

Yunxiao Dong, Tianci Wang, Jian Xia, Zixuan Wang, Chaohui Su, Zhiyuan Li, Gongmin Li, Wei Tang, Jie Gong, Zilong Yu 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.

Yunxiao Dong *School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.
Tianci Wang *School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.
Jian XiaSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.
Zixuan WangSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.
Chaohui SuSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.
Zhiyuan LiSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.
Gongmin LiSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.
Wei TangSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.
Jie GongSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.
Zilong YuSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.
Beining ZhangSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.
Qingyang LiSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.
Xiangshui MiaoSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China. yangrui@hust.edu.cn.ORCID http://orcid.org/0000-0002-3999-7421
Rui YangSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China. miaoxs@hust.edu.cn.ORCID http://orcid.org/0000-0002-1886-5286

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62474073National Natural Science Foundation of China (National Science Foundation of China) U24A20303
6 · The paper itself

Abstract

Introducing probabilistic models into photonic neural networks, harnessing high-throughput and low-latency performance of photons, holds great promise for Bayesian inference. Photonic spiking neurons with stochasticity are essential to realize probabilistic computing. However, existing probabilistic neurons lack intrinsic stochasticity and rely on external entropy sources, adding architectural complexity that impedes high-density integration. Here, we report the first compact on-chip photonic spiking neuron with inherent stochasticity based on a novel phase-change material SbTe

Indexed as

Action PotentialsNeural Networks, ComputerNeuronsBayes TheoremHumansModels, NeurologicalPhotonsStochastic Processes

Identifiers

PMID42151176
PMCPMC13381938

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.