Optimising complexity and learning for photonic reservoir computing with gain-controlled multimode fibres

Optimising complexity and learning for photonic reservoir computing with gain-controlled multimode fibres

Nonlinear photonics is a promising platform for neuromorphic hardware, offering high-speed processing, broad bandwidth, and scalable integration. Within this framework, Reservoir Computing (RC) and Extreme Learning Machines (ELM) are powerful approaches that leverage the dynamics of a complex nonlin...

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Bibliographic Details
Main Authors: Giulia Marcucci, Luana Olivieri, Juan Sebastian Totero Gongora
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Nanotechnology
Subjects:
reservoir computing (RC)
optical computing algorithms
photonic neural network
multimode fibre (MMF)
erbium-doped fibre (EDF)
nonlinear optics and laser properties
Online Access:https://www.frontiersin.org/articles/10.3389/fnano.2025.1631564/full
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https://www.frontiersin.org/articles/10.3389/fnano.2025.1631564/full

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