Toward all-optical unsupervised Hebbian learning in deep photonic neuromorphic networks
Researchers have developed a deep photonic neuromorphic network (PNN) architecture that utilizes phase-change material (PCM) synapses and local optical feedback for unsupervised Hebbian learning. This novel approach bypasses the need for external gradients or complex electro-optical conversions by directly employing correlated pre- and post-synaptic optical activity for adaptation. Experiments using fiber-optic components and programmable attenuators demonstrated the system's ability to achieve adaptive synaptic evolution, optical inference, and autonomous pattern encoding, paving the way for energy-efficient, integrated photonic neuromorphic systems. AI
IMPACT Enables more energy-efficient and scalable neuromorphic computing for tasks like image recognition.