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210901 ||| eng |
| 020 |
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|a 9789811316876
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| 100 |
1 |
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|a Nakajima, Kohei
|e [editor]
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| 245 |
0 |
0 |
|a Reservoir Computing
|h Elektronische Ressource
|b Theory, Physical Implementations, and Applications
|c edited by Kohei Nakajima, Ingo Fischer
|
| 250 |
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|a 1st ed. 2021
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| 260 |
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|a Singapore
|b Springer Nature Singapore
|c 2021, 2021
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| 300 |
|
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|a XIX, 458 p. 161 illus., 127 illus. in color
|b online resource
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| 505 |
0 |
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|a Chapter 1: The cerebral cortex: A delay coupled recurrent oscillator network? -- Chapter 2: Cortico-Striatal Origins of Reservoir Computing, Mixed Selectivity and Higher Cognitive Function -- Chapter 3: Reservoirs learn to learn -- Chapter 4: Deep Reservoir Computing -- Chapter 5: On the characteristics and structures of dynamical systems suitable for reservoir computing -- Chapter 6: Reservoir Computing for Forecasting Large Spatiotemporal Dynamical Systems -- Chapter 7: Reservoir Computing in Material Substrates -- Chapter 8: Physical Reservoir Computing in Robotics -- Chapter 9: Reservoir Computing in MEMS -- Chapter 10: Neuromorphic Electronic Systems for Reservoir Computing -- Chapter 11: Reservoir Computing using Autonomous Boolean Networks Realized on Field-Programmable Gate Arrays -- Chapter 12: Programmable Fading Memory in Atomic Switch Systems for Error Checking Applications -- Chapter 13: Reservoir computing leveraging the transient non-linear dynamics of spin-torque nano-oscillators.-Chapter 14: Reservoir computing based on spintronics technology -- Chapter 15: Reservoir computing with dipole-coupled nanomagnets -- Chapter 16: Performance improvement of delay-based photonic reservoir computing -- Chapter 17: Computing with integrated photonic reservoirs -- Chapter 18: Quantum reservoir computing -- Chapter 19: Towards NMR Quantum Reservoir Computing
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| 653 |
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|a Machine learning
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| 653 |
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|a Control, Robotics, Automation
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| 653 |
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|a Machine Learning
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| 653 |
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|a Spintronics
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Mathematical Models of Cognitive Processes and Neural Networks
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| 653 |
|
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|a Neural networks (Computer science)
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| 653 |
|
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|a Control engineering
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| 653 |
|
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|a Artificial intelligence
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| 653 |
|
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|a Robotics
|
| 653 |
|
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|a Automation
|
| 700 |
1 |
|
|a Fischer, Ingo
|e [editor]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
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|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
|
|a Natural Computing Series
|
| 028 |
5 |
0 |
|a 10.1007/978-981-13-1687-6
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-13-1687-6?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
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|a 006.3
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| 520 |
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|a 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.
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| 520 |
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|a students and experts alike) and practitioners working in the field of machine learning,artificial intelligence, robotics, neuromorphic computing, complex systems, and physics
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| 520 |
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|a 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.
|