This book is the first comprehensive book about reservoir computing
(RC). RC is a powerful and broadly applicable computational framework
based on recurrent neural networks. Its advantages lie in small training
data set requirements, fast training, inherent memory and high
flexibility for various hardware implementations. It originated from
computational neuroscience and machine learning but has, in recent
years, spread dramatically, and has been introduced into a wide variety
of fields, including complex systems science, physics, material science,
biological science, quantum machine learning, optical communication
systems, and robotics. Reviewing the current state of the art and
providing a concise guide to the field, this book introduces readers to
its basic concepts, theory, techniques, physical implementations and
applications.
The book is sub-structured into two major parts: theory and physical
implementations. Both parts consist of a compilation of chapters,
authored by leading experts in their respective fields. The first part
is devoted to theoretical developments of RC, extending the framework
from the conventional recurrent neural network context to a more general
dynamical systems context. With this broadened perspective, RC is not
restricted to the area of machine learning but is being connected to a
much wider class of systems. The second part of the book focuses on the
utilization of physical dynamical systems as reservoirs, a framework
referred to as physical reservoir computing. A variety of physical
systems and substrates have already been suggested and used for the
implementation of reservoir computing. Among these physical systems
which cover a wide range of spatial and temporal scales, are mechanical
and optical systems, nanomaterials, spintronics, and quantum many body
systems.
This book offers a valuable resource for researchers (Ph.D. students and
experts alike) and practitioners working in the field of machine
learning, artificial intelligence, robotics, neuromorphic computing,
complex systems, and physics.
