Computing using delayed feedback systems: towards photonics
Appeltant, L.; Soriano, M.C.; Van der Sande, G.; Danckaert, J.; Massar, S.; Dambre, J.; Schrauwen, B.; Mirasso, C.R.; Fischer, I.
Nonlinear Optics and Applications VI (Benjamin J. Eggleton; Alexander L. Gaeta; Neil G. Broderick, Editors), SPIE Proceedings Vol.. 8434, 84341W (1-6) (2012)
Nonlinear Optics and Applications VI (Benjamin J. Eggleton; Alexander L. Gaeta; Neil G. Broderick, Editors), SPIE Proceedings Vol.. 8434, 84341W (1-6) (2012)
Reservoir computing has recently been introduced as a new paradigm in the eld of machine learning. It is based on the dynamical properties of a network of randomly connected nodes or neurons and shows to be very promising to solve complex classication problems in a computationally ecient way. The key idea is that an input generates nonlinearly transient behavior rendering transient reservoir states suitable for linear classication. Our goal is to study up to which extent systems with delay, and especially photonic systems, can be used as reservoirs. Recently an new architecture has been proposed1 , based on a single nonlinear node with delayed feedback. An electronic1 and an opto-electronic implementation2, 3 have been demonstrated and both have proven to be very successful in terms of performance. This simple conguration, which replaces an entire network of randomly connected nonlinear nodes with one single hardware node and a delay line, is signicantly easier to implement experimentally. It is no longer necessary to construct an entire network of hundreds or even thousands of circuits, each one representing a node. With this approach one node and a delay line suce to construct a computational unit. In this manuscript, we present a further investigation of the properties of delayed feedback congurations used as a reservoir. Instead of quantifying the performance as an error obtained for a certain benchmark, we now investigate a task-independent property, the linear memory of the system.
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