Evidence map›Paper›PMID 41777549›Full record

ArticleNational science review2026

Pavlov's experiment-inspired optical neural networks based on dual-color fluorescence switching effect.

Songrui Wei, Kunbin Huang, Dingchen Wang, Shangcheng Yang, Haiyan Huang, Xiao Tang, Yanqi Ge, Bowen Du, Zhi Chen, Zhongrui Wang and 4 more

Abstract read
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In one paragraph

Article in National science review, 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.

Songrui WeiCollege of Physics and Optoelectronic Engineering, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.
Kunbin HuangCollege of Physics and Optoelectronic Engineering, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.
Dingchen WangDepartment of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong 999077, China.
Shangcheng YangCollege of Physics and Optoelectronic Engineering, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.
Haiyan HuangCollege of Physics and Optoelectronic Engineering, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.
Xiao TangCollege of Physics and Optoelectronic Engineering, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.
Yanqi GeCollege of Physics and Optoelectronic Engineering, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.
Bowen DuCollege of Physics and Optoelectronic Engineering, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.
Zhi ChenCollege of Physics and Optoelectronic Engineering, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.
Zhongrui WangSchool of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China.
Shuqing ChenCollege of Physics and Optoelectronic Engineering, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.
Dror FixlerDepartment of Engineering, Bar-Illan University, Ramat Gan 5290002, Israel.
Dianyuan FanCollege of Physics and Optoelectronic Engineering, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.
Han ZhangCollege of Physics and Optoelectronic Engineering, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cutting-edge optical neural networks are often still trained by backpropagation, which is computationally intensive and originally for conventional artificial neural networks. Inspired by Pavlov's experiment, we drew upon the principles of biological memory to establish an associative learning framework for training optical neural networks that mimics the mechanisms of associative learning and synaptic plasticity using dual-wavelength stimuli (i.e. ultraviolet and visible light) on a dual-color photoinitiator resin. Sequential light irradiation was shown to induce fluorescence switching and encode associative memory in the resin, which can serve as a physical substrate for an optical neural network. In optical experiments, the established framework was applied to pattern recognition of the letters 'N,' 'V,' and 'Z.' Simulations were conducted that extended its application to the recognition of handwritten digits. Compared to the current mainstream 'bottom-up' optical neural network fabrication approach that requires 'weight calculation followed by hardware implementation', this work presents a novel 'top-down'

Indexed as

associative learningdual-color photoinitiatoroptical computingoptical neural networkPavlov’s experiment

Identifiers

PMID41777549
PMCPMC12951521

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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.