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Reservoir computing : theory, physical implementations, and applications / Kohei Nakajima, Ingo Fischer, editors.

Nakajima, Kohei, editor.; Fischer, Ingo, editor.
2021
QA76.87 .R47 2021
Available Online
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Title
Reservoir computing : theory, physical implementations, and applications / Kohei Nakajima, Ingo Fischer, editors.
ISBN
9789811316876 (electronic bk.)
9811316872 (electronic bk.)
9789811316869
9811316864
DOI
https://doi.org/10.1007/978-981-13-1687-6
Published
Singapore : Springer, [2021]
Copyright
©2021
Language
English
Description
1 online resource (xix, 458 pages) : illustrations (chiefly color).
Item Number
10.1007/978-981-13-1687-6 doi
Call Number
QA76.87 .R47 2021
Dewey Decimal Classification
006.3/2
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed August 20, 2021).
Added Author
Nakajima, Kohei, editor.
Fischer, Ingo, editor.
Series
Natural computing series.
Available in Other Form
Print version: 9789811316869
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Online Access
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All Resources
Part I. Fundamental aspects and new developments in reservoir computing. The cerebral cortex : a delay-coupled recurrent oscillator network?
Cortico-striatal origins of reservoir computing, mixed selectivity, and higher cognitive function
Reservoirs learn to learn
Deep reservoir computing
On the characteristics and structures of dynamical systems suitable for reservoir computing
Reservoir computing for forecasting large spatiotemporal dynamical systems
Part II. Physical implementations of reservoir computing. Reservoir computing in material substrates
Part III. Physical implementations : mechanics and bio-inspired machines. Physical reservoir computing in robotics
Reservoir computing in MEMS
Part IV. Physical implementations : neuromorphic devices and nanotechnology. Neuromorphic electronic systems for reservoir computing
Reservoir computing using autonomous Boolean networks realized on field-programmable gate arrays
Programmable fading memory in atomic switch systems for error checking applications
Part V. Physical implementations : spintronics reservoir computing. Reservoir computing leveraging the transient non-linear dynamics of spin-torque nano-oscillators
Reservoir computing based on spintronics technology
Reservoir computing with dipole-coupled nanomagnets
Part VI. Physical implementations : photonic reservoir computing. Performance improvement of delay-based photonic reservoir computing
Computing with integrated photonic reservoirs
Part VII. Physical implementations : quantum reservoir computing. Quantum reservoir computing : a reservoir approach toward quantum machine learning on near-term quantum devices
Toward NMR quantum reservoir computing.

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