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Reservoir Computing

Reservoir Computing


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About the Book

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.
About the Author:

Kohei Nakajima is an Associate Professor in the Graduate School of Information Science and Technology at the University of Tokyo. He received BS, MS, and PhD degrees from the University of Tokyo in 2004, 2006, and 2009, respectively. After obtaining his PhD, he spent five years as a post-doctoral fellow and a JSPS Postdoctoral Fellow for Research Abroad at the University of Zurich and at ETH Zurich in Switzerland. In 2013, he was awarded the title of the Hakubi researcher at Kyoto University, and until 2017, he was an Assistant Professor at the Hakubi Center for Advanced Research at Kyoto University. He was also a JST PRESTO researcher from 2015 to 2019. His research interests include nonlinear dynamical systems, information theory, reservoir computing, physical reservoir computing, and soft robotics.

Ingo Fischer has since 2009 been a Research Professor of the Spanish National Research Council (CSIC) at the Institute for Cross-Disciplinary Physics and Complex Systems IFISC (UIB-CSIC) in Palma de Mallorca (Spain). Moreover, he is head of the University group 'Experimental Physics of Complex Systems' at the Universitat de les Illes Balears (UIB) and currently Deputy Scientific Director of the Maria de Maeztu Unit of Excellence on 'Information Processing in and by Complex Systems'. His research has been covering nonlinear photonics, brain-inspired information processing, in particular reservoir computing, complex systems and their applications, broad area semiconductor lasers and quantum chaos. He received his diploma and Ph.D. degrees in physics from Philipps-University Marburg (Germany) in 1992 and 1995, respectively. He was a Post-doctoral researcher at Advanced Telecommunication Research Labs at Kyoto (Japan), was Hochschul-Assistent (Assistant Professor) at TU Darmstadt (Germany), senior visiting scientist at Vrije Universiteit Brussel (Belgium), and from 2007 to 2009 full professor (chair) for photonics and integrated systems at Heriot-Watt University, Edinburgh (U.K.) In 2017 and 2018, he has also been Distinguished Visiting Professor at the Yukawa Institute, Kyoto University (Japan). He received several research prizes, including the first Hassian Industry Cooperation Prize for Technology Transfer.





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Product Details
  • ISBN-13: 9789811316869
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Height: 234 mm
  • No of Pages: 458
  • Series Title: Natural Computing
  • Sub Title: Theory, Physical Implementations, and Applications
  • Width: 156 mm
  • ISBN-10: 9811316864
  • Publisher Date: 07 Jun 2021
  • Binding: Hardback
  • Language: English
  • Returnable: Y
  • Spine Width: 27 mm
  • Weight: 893 gr


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