Sale!

Reservoir Computing Theory Physical Implementations and Applications 1st Edition by Kohei Nakajima, Ingo Fischer ISBN 9789811316869 9811316864

Name: Reservoir Computing Theory Physical Implementations and Applications 1st Edition by Kohei Nakajima, Ingo Fischer ISBN 9789811316869 9811316864
SKU: EB-33903786
Availability: InStock

Original price was: $50.00.Current price is: $25.00.

Reservoir Computing Theory Physical Implementations and Applications Natural Computing Series Kohei Nakajima (Editor) Digital Instant Download

Author(s): Kohei Nakajima (editor), Ingo Fischer (editor)

ISBN(s): 9789811316869, 9811316864

Edition: 1st ed. 2021

File Details: PDF, 20.71 MB

Year: 2021

Language: English

SKU: EB-33903786 Category: Computers - Computer Science Tags: Ingo Fischer (editor), Kohei Nakajima (editor)

Description

Reservoir Computing Theory Physical Implementations and Applications 1st Edition by Kohei Nakajima, Ingo Fischer – Ebook PDF Instant Download/Delivery: 9789811316869 ,9811316864
Full download Reservoir Computing Theory Physical Implementations and Applications 1st Edition after payment

Product details:

ISBN 10: 9811316864
ISBN 13: 9789811316869
Author: Kohei Nakajima, Ingo Fischer

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 byleading 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.

Reservoir Computing Theory Physical Implementations and Applications 1st Edition Table of contents:

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

People also search for Reservoir Computing Theory Physical Implementations and Applications 1st Edition:

reservoir computing theory physical implementations and applications pdf

reservoir computing explained

what is reservoir computing

reservoir computing applications

physical reservoir computing

Tags: Kohei Nakajima, Ingo Fischer, Reservoir Computing, Physical Implementations