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020 _a9789813349766
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024 7 _a10.1007/978-981-33-4976-6
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
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072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aFluctuation-Induced Network Control and Learning
_h[electronic resource] :
_bApplying the Yuragi Principle of Brain and Biological Systems /
_cedited by Masayuki Murata, Kenji Leibnitz.
250 _a1st ed. 2021.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2021.
300 _aXI, 236 p. 104 illus., 86 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 1: Introduction to Yuragi Theory and Yuragi Control -- Chapter 2: Functional Roles of Yuragi in Biosystems -- Chapter 3: Next-Generation Bio- and Brain-Inspired Networking -- Chapter 4: Yuragi-Based Virtual Network Control -- Chapter 5: Introduction to Yuragi Learning -- Chapter 6: Fast/Slow-Pathway Bayesian Attractor Model for IoT Networks Based on Software-Defined Networking with Virtual Network Slicing -- Chapter 7: Application to IoT Network Control -- Chapter 8: Another Prediction Method and Application to Low-Power Wide-Area Networks -- Chapter 9: Artificial Intelligence Platform for Yuragi Learning -- Chapter 10: Bias-Free Yuragi Learning.
520 _aFrom 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.
650 0 _aArtificial intelligence.
650 0 _aComputer networks .
650 0 _aTelecommunication.
650 1 4 _aArtificial Intelligence.
650 2 4 _aComputer Communication Networks.
650 2 4 _aCommunications Engineering, Networks.
700 1 _aMurata, Masayuki.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aLeibnitz, Kenji.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789813349759
776 0 8 _iPrinted edition:
_z9789813349773
776 0 8 _iPrinted edition:
_z9789813349780
856 4 0 _uhttps://doi.org/10.1007/978-981-33-4976-6
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c174520
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