Reservoir computing (RC) studies the properties of large recurrent networks of artificial neurons, with either fixed or random connectivity. Over the last years, reservoirs have become a key tool for pattern recognition and neuroscience problems, being able to develop a rich representation of the temporal information even if left untrained. The common paradigm has been instantiated into several models, among which the Echo State Network and the Liquid State Machine represent the most widely known ones. Nowadays, RC represents the de facto state-of-the-art approach for efficient learning in the temporal domain. Besides, theoretical studies in RC area can contribute to the broader field of Recurrent Neural Networks research by enabling a deeper understanding of the fundamental capabilities of dynamical recurrent models, even in the absence of training of the recurrent connections. RC paradigm also allows using different dynamical systems, including hardware, for computation. This paper is intended to give an overview on the RC research field, high- lighting major frontiers in its development and finally introducing the contributed papers to the ESANN 2020 special session.