
Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an opto-electronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.
Contains main paper and two Supplementary Materials
computation, FOS: Computer and information sciences, Computer Science - Machine Learning, Technology and Engineering, Computer Science - Emerging Technologies, FOS: Physical sciences, Sciences de l'ingénieur, Reservoir Computing, delayed-feedback, delay-coupled systems, Article, Machine Learning (cs.LG), Neural and Evolutionary Computing (cs.NE), Optoelectronic, Physique, Computer Science - Neural and Evolutionary Computing, dynamical systems, Nonlinear Sciences - Chaotic Dynamics, Emerging Technologies (cs.ET), Chaotic Dynamics (nlin.CD), Physics - Optics, Optics (physics.optics)
computation, FOS: Computer and information sciences, Computer Science - Machine Learning, Technology and Engineering, Computer Science - Emerging Technologies, FOS: Physical sciences, Sciences de l'ingénieur, Reservoir Computing, delayed-feedback, delay-coupled systems, Article, Machine Learning (cs.LG), Neural and Evolutionary Computing (cs.NE), Optoelectronic, Physique, Computer Science - Neural and Evolutionary Computing, dynamical systems, Nonlinear Sciences - Chaotic Dynamics, Emerging Technologies (cs.ET), Chaotic Dynamics (nlin.CD), Physics - Optics, Optics (physics.optics)
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