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
Recently an new architecture has been proposed<sup>1</sup> , based on a single nonlinear node with delayed feedback.
An electronic<sup>1</sup> and an opto-electronic implementation<sup>2, 3</sup> 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
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.