AI RESEARCH
Online unsupervised Hebbian learning in deep photonic neuromorphic networks
arXiv CS.LG
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ArXi:2601.22300v2 Announce Type: replace-cross 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.