Evidence map›Paper›PMID 37862413›Full record

ArticleScience advances2023

Event-driven adaptive optical neural network.

Frank Brückerhoff-Plückelmann, Ivonne Bente, Marlon Becker, Niklas Vollmar, Nikolaos Farmakidis, Emma Lomonte, Francesco Lenzini, C David Wright, Harish Bhaskaran, Martin Salinga and 2 more

Abstract read
In one paragraph

Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. The Photoinduced Response of Antimony from Femtoseconds to Minutes.Advanced materials (Deerfield Beach, Fla.) · 2025
    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.

Frank Brückerhoff-PlückelmannPhysical Institute, University of Münster, Heisenbergstraße 11, 48149 Münster, Germany.ORCID 0009-0006-5748-7069
Ivonne BentePhysical Institute, University of Münster, Heisenbergstraße 11, 48149 Münster, Germany.ORCID 0000-0003-3198-4244
Marlon BeckerInstitute for Geoinformatics, University of Münster, Heisenbergstraße 2, 48149 Münster, Germany.
Niklas VollmarInstitute of Materials Physics, University of Münster, Wilhelm-Klemm-Straße 10, 48149 Münster, Germany.
Nikolaos FarmakidisDepartment of Material, University of Oxford, Parks Road, Oxford OX1 3PH, UK.ORCID 0000-0001-9974-1607
Emma LomontePhysical Institute, University of Münster, Heisenbergstraße 11, 48149 Münster, Germany.ORCID 0000-0001-8238-4690
Francesco LenziniPhysical Institute, University of Münster, Heisenbergstraße 11, 48149 Münster, Germany.ORCID 0000-0002-1448-5788
C David WrightDepartment of Engineering, University of Exeter, North Park Road, Exeter EX4 4QF, UK.ORCID 0000-0003-4087-7467
Harish BhaskaranDepartment of Material, University of Oxford, Parks Road, Oxford OX1 3PH, UK.ORCID 0000-0003-0774-8110
Martin SalingaInstitute of Materials Physics, University of Münster, Wilhelm-Klemm-Straße 10, 48149 Münster, Germany.ORCID 0000-0002-2228-6244
Benjamin RisseInstitute for Geoinformatics, University of Münster, Heisenbergstraße 2, 48149 Münster, Germany.ORCID 0000-0001-5691-4029
Wolfram H P PernicePhysical Institute, University of Münster, Heisenbergstraße 11, 48149 Münster, Germany.ORCID 0000-0003-4569-4213

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present an adaptive optical neural network based on a large-scale event-driven architecture. In addition to changing the synaptic weights (synaptic plasticity), the optical neural network's structure can also be reconfigured enabling various functionalities (structural plasticity). Key building blocks are wavelength-addressable artificial neurons with embedded phase-change materials that implement nonlinear activation functions and nonvolatile memory. Using multimode focusing, the activation function features both excitatory and inhibitory responses and shows a reversible switching contrast of 3.2 decibels. We train the neural network to distinguish between English and German text samples via an evolutionary algorithm. We investigate both the synaptic and structural plasticity during the training process. On the basis of this concept, we realize a large-scale network consisting of 736 subnetworks with 16 phase-change material neurons each. Overall, 8398 neurons are functional, highlighting the scalability of the photonic architecture.

Identifiers

PMID37862413
PMCPMC10588940

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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.