6533b856fe1ef96bd12b1d40

RESEARCH PRODUCT

Advances in photonic reservoir computing

Daniel BrunnerGuy Van Der SandeMiguel C. Soriano

subject

Nonlinear opticsQC1-99942.55.pxAnalogue computingMathematicsofComputing_NUMERICALANALYSISOptical computing05.45.-a02 engineering and technologyEuropean Social Fund01 natural sciences020210 optoelectronics & photonics42.79.ta0103 physical sciences0202 electrical engineering electronic engineering information engineeringOptical computing07.05.mh85.60.-qElectrical and Electronic Engineering010306 general physics[PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics]Artificial neural networksPhysicsnonlinear opticsReservoir computing42.79.hpanalogue computingAtomic and Molecular Physics and OpticsElectronic Optical and Magnetic Materials42.65.-kEngineering managementWork (electrical)Research counciloptical computingScience policy42.82.-martificial neural networksBiotechnology

description

We review a novel paradigm that has emerged in analogue neuromorphic optical computing. The goal is to implement a reservoir computer in optics, where information is encoded in the intensity and phase of the optical field. Reservoir computing is a bio-inspired approach especially suited for processing time-dependent information. The reservoir’s complex and high-dimensional transient response to the input signal is capable of universal computation. The reservoir does not need to be trained, which makes it very well suited for optics. As such, much of the promise of photonic reservoirs lies in their minimal hardware requirements, a tremendous advantage over other hardware-intensive neural network models. We review the two main approaches to optical reservoir computing: networks implemented with multiple discrete optical nodes and the continuous system of a single nonlinear device coupled to delayed feedback.

https://doi.org/10.1515/nanoph-2016-0132