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 optoelectronic 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.

At the boundaries between photonics and dynamic systems theory, we combine recent advances in neural networks with opto-electronic nonlinearities to demonstrate a new way to perform optical information processing. The concept of reservoir computing arose recently as a powerful solution to the issue of training recurrent neural networks. Indeed, it is comparable to, or even outperforms, other state of the art solutions for tasks such as speech recognition or time series prediction. As it is based on a static topology, it allows making the most of very simple physical architectures having complex nonlinear dynamics. The method is inherently robust to noise and does not require explicit programming operations. It is therefore particularly well adapted for analog realizations. Among the various implementations of the concept that have been proposed, we focus on the field of optics. Our experimental reservoir computer is based on opto-electronic technology, and can be viewed as an intermediate step towards an all optical device. Our fiber optics system is based on a nonlinear feedback loop operating at the threshold of chaos. In its present preliminary stage it is already capable of complicated tasks like modeling nonlinear systems with memory. Our aim is to demonstrate that such an analog reservoir can have performances comparable to state of the art digital implementations of Neural Networks. Furthermore, our system can in principle be operated at very high frequencies thanks to the high speed of photonic devices. Thus one could envisage targeting applications such as online information processing in broadband telecommunications

We report phase-sensitive amplification of light using χ((3)) parametric processes in a chalcogenide ridge waveguide. By spectrally slicing pump, signal and idler waves from a single pulsed source, we are able to observe 9.9 dB of on-chip phase-sensitive extinction with a signal-degenerate dual pump four-wave mixing architecture in good agreement with numerical simulations.

We report the demonstration of automatic higher-order dispersion compensation for the transmission of 275 fs pulses associated with a Tbaud Optical Time Division Multiplexed (OTDM) signal. Our approach achieves simultaneous automatic compensation for 2nd, 3rd and 4th order dispersion using an LCOS spectral pulse shaper (SPS) as a tunable dispersion compensator and a dispersion monitor made of a photonic-chip-based all-optical RF-spectrum analyzer. The monitoring approach uses a single parameter measurement extracted from the RF-spectrum to drive a multidimensional optimization algorithm. Because these pulses are highly sensitive to fluctuations in the GVD and higher orders of chromatic dispersion, this work represents a key result towards practical transmission of ultrashort optical pulses. The dispersion can be adapted on-the-fly for a 1.28 Tbaud signal at any place in the transmission line using a black box approach.