Reservoir computing : theory, physical implementations, and applications

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

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 ha...

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Bibliographic Details
Other Authors: Nakajima, Kohei (Editor), Fischer, Ingo (Editor)
Format: eBook
Language:English
Published: Singapore : Springer, [2021]
Series:Natural computing series.
Subjects:
Neural networks (Computer science)
Machine learning.
Machine learning
Online Access:Click for online access
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.

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