ABSTRACT
Reservoir computing represents a subset of neural network models. In this framework, a fixed, recurrent network of nonlinear nodes performs a diversity of computations, from which linear classifiers extract the most useful information to perform a given algorithm. These systems maintain many of the advantages of neural networks, including adaptability and robustness to noise. In a hardware context, reservoirs require far fewer tunable elements than traditional neural network models to run effectively. Even simple physical systems can represent more complex virtual networks, and thereby perform a variety of complex tasks. Over the past several years, reservoir computers have been constructed that exploit the incredible bandwidths and speeds available to photonic signals. These ‘photonic reservoirs’ utilized optical multiplexing strategies to form highly complex virtual networks. Experimentally demonstrated systems have displayed state-of-the-art performance in a variety of areas, including speech recognition, time-series prediction, Boolean logic operations, and nonlinear channel equalization. In this chapter, we review the recent progress and achievements of this field.
