Skip to main content
SpringerLink
Log in
Book cover

Fluctuation-Induced Network Control and Learning

Applying the Yuragi Principle of Brain and Biological Systems

  • Book
  • © 2021

Overview

Editors:
  1. Masayuki Murata

    1. Graduate School of Information Science and Technology, Osaka University, Suita, Japan

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Kenji Leibnitz

    1. Center for Information and Neural Networks, National Institute of Information and Communications Technology (NICT), Suita, Japan

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Provides an interdisciplinary computational approach applying bio- and neuroscience to communication network control

  • Proposes noise-driven machine-learning methods utilizing the latest findings in human brain research

  • Extends theoretical concepts to various research applications in a step-by-step approach

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (10 chapters)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. Fluctuation-Based Control Systems: Yuragi Concept

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Introduction to Yuragi Theory and Yuragi Control

      • Kenji Leibnitz
      Pages 3-30

    3. Functional Roles of Yuragi in Biosystems

      • Toshio Yanagida, Tsutomu Murata
      Pages 31-47

    4. Next-Generation Bio- and Brain-Inspired Networking

      • Naoki Wakamiya
      Pages 49-87

    5. Yuragi-Based Virtual Network Control

      • Yuki Koizumi
      Pages 89-110

  3. Yuragi Learning: Extension to Artificial Intelligence

    1. Front Matter

      Pages 111-111
      Download chapter PDF

    2. Introduction to Yuragi Learning

      • Shin’ichi Arakawa, Tatsuya Otoshi
      Pages 113-133

    3. Fast/Slow-Pathway Bayesian Attractor Model for IoT Networks Based on Software-Defined Networking with Virtual Network Slicing

      • Onur Alparslan, Shin’ichi Arakawa
      Pages 135-154

    4. Application to IoT Network Control: Predictive Network Control Based on Real-World Information

      • Yuichi Ohsita
      Pages 155-179

    5. Another Prediction Method and Application to Low-Power Wide-Area Networks

      • Daichi Kominami
      Pages 181-198

    6. Artificial Intelligence Platform for Yuragi Learning

      • Toshiyuki Kanoh
      Pages 199-211

    7. Bias-Free Yuragi Learning

      • Tatsuya Otoshi
      Pages 213-232

  4. Back Matter

    Pages 233-236
    Download chapter PDF
Back to top

Keywords

About this book

From theory to application, this book presents research on biologically and brain-inspired networking and machine learning based on Yuragi, which is the Japanese term describing the noise or fluctuations that are inherently used to control the dynamics of a system. The Yuragi mechanism can be found in various biological contexts, such as in gene expression dynamics, molecular motors in muscles, or the visual recognition process in the brain. Unlike conventional network protocols that are usually designed to operate under controlled conditions with a predefined set of rules, the probabilistic behavior of Yuragi-based control permits the system to adapt to unknown situations in a distributed and self-organized manner leading to a higher scalability and robustness.

The book consists of two parts. Part 1 provides in four chapters an introduction to the biological background of the Yuragi concept as well as how these are applied to networking problems. Part 2 provides additional contributions that extend the original Yuragi concept to a Bayesian attractor model from human perceptual decision making. In the six chapters of the second part, applications to various fields in information network control and artificial intelligence are presented, ranging from virtual network reconfigurations, a software-defined Internet of Things, and low-power wide-area networks.

This book will benefit those working in the fields of information networks, distributed systems, and machine learning who seek new design mechanisms for controlling large-scale dynamically changing systems.

Editors and Affiliations

  • Graduate School of Information Science and Technology, Osaka University, Suita, Japan

    Masayuki Murata

  • Center for Information and Neural Networks, National Institute of Information and Communications Technology (NICT), Suita, Japan

    Kenji Leibnitz

About the editors

Masayuki Murata received the M.E. and D.E. degrees in information and computer science from Osaka University, Japan, in 1984 and 1988, respectively. In April 1984, he joined the Tokyo Research Laboratory, IBM Japan, as a researcher. From September 1987 to January 1989, he was an assistant professor with the Computation Center, Osaka University. In February 1989, he moved to the Department of Information and Computer Sciences, Faculty of Engineering Science, Osaka University. In April 1999, he became a professor in the Cybermedia Center, Osaka University, and he now has been with the Graduate School of Information Science and Technology, Osaka University, since April 2004. He has published or presented more than 900 papers in international and domestic journals and at conferences. His research interests include computer network architecture and bio- and brain-inspired computing platforms. He is a member of IEEE, ACM, and IEICE.

Kenji Leibnitz received his M.Sc. and Ph.D. degrees in information science from the University of Würzburg, Germany, in 2003. In 2004, he joined the research group of Prof. Murata at Osaka University, where he became a specially appointed associate professor in 2006. In 2010, he moved to the National Institute of Information and Communications Technology (NICT) as a senior researcher, and since 2013 he has been a principal investigator at the Center for Information and Neural Networks (CiNet) of NICT and Osaka University. His research interests include modeling and performance analysis of communication networks, especially biologically and brain-inspired mechanisms for self-organization in future networks.




Bibliographic Information

  • Book Title: Fluctuation-Induced Network Control and Learning

  • Book Subtitle: Applying the Yuragi Principle of Brain and Biological Systems

  • Editors: Masayuki Murata, Kenji Leibnitz

  • DOI: https://doi.org/10.1007/978-981-33-4976-6

  • Publisher: Springer Singapore

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Singapore Pte Ltd. 2021

  • Hardcover ISBN: 978-981-33-4975-9Published: 17 March 2021

  • Softcover ISBN: 978-981-33-4978-0Published: 18 March 2022

  • eBook ISBN: 978-981-33-4976-6Published: 16 March 2021

  • Edition Number: 1

  • Number of Pages: XI, 236

  • Number of Illustrations: 18 b/w illustrations, 86 illustrations in colour

  • Topics: Artificial Intelligence, Computer Communication Networks, Communications Engineering, Networks

Publish with us

Policies and ethics

Back to top

Search

Navigation