Toward all-optical unsupervised Hebbian learning in deep photonic neuromorphic networks

[2601.22300] Toward all-optical unsupervised Hebbian learning in deep photonic neuromorphic networks Skip to main content Search Submit Donate Log in Physics > Optics arXiv:2601.22300 (physics) [Submitted on 29 Jan 2026 ( v1 ), last revised 27 Aug 2026 (this version, v4)] Title: Toward all-optical unsupervised Hebbian learning in deep photonic neuromorphic networks Authors: Xi Li , Disha Biswas , Peng Zhou , Wesley H. Brigner , Anna Capuano , Joseph S. Friedman , Qing Gu View PDF Abstract: We propose a deep photonic neuromorphic network (PNN) architecture based on phase-change material (PCM) synapses and local optical feedback for online, unsupervised Hebbian learning. The proposed architecture combines optical vector-matrix multiplication, non-volatile PCM synaptic weighting, and local coincidence-driven synaptic adaptation within a multilayer photonic crossbar framework compatible with photonic integrated circuits. Unlike conventional PNNs that rely on externally computed gradients, repeated optical-electrical-optical conversions, or global backpropagation, the proposed framework employs local Hebbian learning governed directly by correlated pre- and post-synaptic optical activity. To investigate the feasibility of the proposed learning mechanism, we implemented the PNN design using fiber-optic components, programmable variable optical attenuators, and real-time software control that incorporates PCM thermal dynamics. Supervised and unsupervised learning behaviors were experimentally evaluated under both offline and online learning conditions using representative image-recognition tasks. The experimental results demonstrate adaptive synaptic evolution, successful optical inference, and autonomous pattern encoding through local Hebbian learning under realistic fiber-optic hardware conditions. These results establish a pathway toward future integrated photonic neuromorphic systems capable of scalable and energy-efficient online Hebbian learning. Comments: 16 pages, 4 figures Subjects: Optics (physics.optics) ; Disordered Systems and Neural Networks (cond-mat.dis-nn); Emerging Technologies (cs.ET); Machine Learning (cs.LG) Cite as: arXiv:2601.22300 [physics.optics] (or arXiv:2601.22300v4 [physics.optics] for this version) https://doi.org/10.48550/arXiv.2601.22300 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Qing Gu [ view email ] [v1] Thu, 29 Jan 2026 20:26:36 UTC (1,180 KB) [v2] Fri, 6 Mar 2026 04:42:38 UTC (973 KB) [v3] Thu, 18 Jun 2026 04:39:30 UTC (1,792 KB) [v4] Thu, 27 Aug 2026 01:49:39 UTC (1,793 KB) Full-text links: Access Paper: View PDF view license Current browse context: physics.optics < prev | next > new | recent | 2026-01 Change to browse by: cond-mat cond-mat.dis-nn cs cs.ET cs.LG physics References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Toward all-optical unsupervised Hebbian learning in deep photonic neuromorphic networks#674290
• Photonic quantum convolutional neural networks with adaptive state injection#620287
• Accelerating spiking neural networks with photonic reconfigurable devices.#295304
• Integrated Neuromorphic Photonic Computing for AI Acceleration: Emerging Devices, Network Architectures, and Future Paradigms.#671216
• Scalable neuromorphic computing from autonomous spiking dynamics in a clockless reconfigurable chip#196451