*Organizers*
Claudio Gallicchio (University of Pisa, Italy), Mantas Lukoševičius (Kaunas University of Technology , Lithuania), Simone Scardapane (La Sapienza University of Rome, Italy)

*Description*
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 special session targets the latest contributions emerging in the field of RC from all points of view, including advancements in theory, applications, and hardware implementations. Comparisons with traditional and deep learning models, analyzing the impact of the topology of the reservoir are also welcomed. Overall, the special session aims at stimulating an open discussion in the neural network community, pushing to a broader understanding of RC models and neural networks in general.

*Topics of the special session*
-Novel Reservoir Computing models
-Deep Reservoir Computing
-Reservoir Computing for Big Data
-Physical, Neuromorphic and Photonic implementations of Reservoir Computing
-Theoretical analysis of Reservoir Computing models (e.g., approximation abilities, asymptotic properties, etc.)
-Echo State Property and stability of input-driven reservoirs
-Architectural design of reservoirs
-Evolutions of the RC paradigm (e.g., conceptors)
-Biological motivations and applications in neuroscience
-Adaptation of reservoir dynamics and of system dynamics
-Non-iterative methods for readout training
-Novel application fields for the RC paradigm

*Important Dates*
Paper submission deadline: 18 November 2019
Notification of acceptance: 31 January 2020
ESANN 2020 Conference dates: 22-24 April 2020, Bruges, Belgium

*Papers Submission*
Potential authors are invited to submit their paper through the ESANN portal following the instructions provided on www.esann.org. Each paper will undergo a peer-reviewing process for its acceptance. Authors who submit papers to this special session are invited to mention it on the author submission form; submissions to the special sessions must follow the same format, instructions, and deadlines as any other submission to ESANN 2020, and must be sent according to the same procedure.