ReviewNanophotonics (Berlin, Germany)2022
Photonic neuromorphic technologies in optical communications.
Review in Nanophotonics (Berlin, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Compound meta-optics: there is plenty of room at the top.Nanophotonics (Berlin, Germany) · 2025Article
- Optical neuromorphic computing via temporal up-sampling and trainable encoding on a telecom device platform.Nanophotonics (Berlin, Germany) · 2025Article
- Neuromorphic Photonics Circuits: Contemporary Review.Nanomaterials (Basel, Switzerland) · 2023Review
- Deriving task specific performance from the information processing capacity of a reservoir computer.Nanophotonics (Berlin, Germany) · 2023Article
- Photons guided by axons may enable backpropagation-based learning in the brain.Scientific reports · 2022Article
- Deep learning in light-matter interactions.Nanophotonics (Berlin, Germany) · 2022Review
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in terms of computational speed and complexity. The latest breakthroughs are strongly supported by advanced signal processing, implemented in the digital domain. Algorithms of different levels of complexity aim at improving data recovery, expanding the reach of transmission, validating the integrity of the optical network operation, and monitoring data transfer faults. Lately, the concept of reservoir computing (RC) inspired hardware implementations in photonics that may offer revolutionary solutions in this field. In a brief introduction, I discuss some of the established digital signal processing (DSP) techniques and some new approaches based on ML and neural network (NN) architectures. In the main part, I review the latest neuromorphic computing proposals that specifically apply to photonic hardware and give new perspectives on addressing signal processing in optical communications. I discuss the fundamental topologies in photonic feed-forward and recurrent network implementations. Finally, I review the photonic topologies that were initially tested for channel equalization benchmark tasks, and then in fiber transmission systems, for optical header recognition, data recovery, and modulation format identification.
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Registered trials
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.