Online unsupervised Hebbian learning in deep photonic neuromorphic networks

2026-01-29

AI Summary

This work proposes a purely photonic deep neuromorphic network architecture that overcomes the speed and energy-efficiency bottlenecks inherent in conventional von Neumann systems. Unlike existing photonic neuromorphic approaches—which predominantly rely on supervised learning and optoelectronic conversion—the proposed system enables online, unsupervised Hebbian learning entirely in the optical domain. It leverages non-volatile phase-change materials to implement photonic synapses and incorporates an all-optical local feedback mechanism, allowing direct processing of optical signals on a commercial fiber-optic platform without any optoelectronic conversion. Demonstrated on an alphabet recognition task, the system achieves 100% accuracy, highlighting its potential for high-throughput, low-latency, and fully optical intelligent information processing.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

While software implementations of neural networks have driven significant advances in computation, the von Neumann architecture imposes fundamental limitations on speed and energy efficiency. Neuromorphic networks, with structures inspired by the brain's architecture, offer a compelling solution with the potential to approach the extreme energy efficiency of neurobiological systems. Photonic neuromorphic networks (PNNs) are particularly attractive because they leverage the inherent advantages of light, namely high parallelism, low latency, and exceptional energy efficiency. Previous PNN demonstrations have largely focused on device-level functionalities or system-level implementations reliant on supervised learning and inefficient optical-electrical-optical (OEO) conversions. Here, we introduce a purely photonic deep PNN architecture that enables online, unsupervised learning. We propose a local feedback mechanism operating entirely in the optical domain that implements a Hebbian learning rule using non-volatile phase-change material synapses. We experimentally demonstrate this approach on a non-trivial letter recognition task using a commercially available fiber-optic platform and achieve a 100 percent recognition rate, showcasing an all-optical solution for efficient, real-time information processing. This work unlocks the potential of photonic computing for complex artificial intelligence applications by enabling direct, high-throughput processing of optical information without intermediate OEO signal conversions.

Problem

• photonic neuromorphic networks
• unsupervised learning
• online learning
• optical-electrical-optical conversion
• Hebbian learning

Innovation

• photonic neuromorphic networks
• unsupervised Hebbian learning
• all-optical computing
• phase-change material synapses
• online learning