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1.

図書
図書
1. Sync : how order emerges from chaos in the universe, nature, and daily life (: pbk)
Steven Strogatz
出版情報: New York : Hachette Books, 2015, c2003  viii, 338 p. ; 21 cm
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2.

図書
図書
2. Self-organization and associative memory. 2nd ed (: us ; : gw)
Teuvo Kohonen
出版情報: Berlin ; New York ; Tokyo : Springer-Verlag, c1988  xv, 312 p. ; 24 cm
シリーズ名: Springer series in information sciences ; v. 8
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3.

図書
図書
3. Self-organization, computational maps, and motor control
edited by Pietro Morasso and Vittorio Sanguineti
出版情報: Amsterdam ; New York : Elsevier, 1997  xvii, 635 p. ; 24 cm
シリーズ名: Advances in psychology ; 119
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Cortical maps of sensorimotor spaces / V. Sanguineti et al.Part I：
Field computation in motor control / B. MacLennan
A probability interpretation of neural population coding for movement / T. Sanger
Computational models of sensorimotor integration / Z. Ghahramani et al.
How relevant are subcortical maps for the cortical machinery? An hypothesis based on parametric study of extra-relay afferents to primary sensory areas / D. Minciacchi ; A. Granato
Artificial force-field based methods in robotics / T. Tsuji et al.Part II：
Learning Newtownian mechanics / F.A. Mussa Ivaldi ; E. Bizzi
Motor intelligence in a simple distributed control system: walking machines and stick insects / H. Cruse ; J. Dean
The dynamic neural field theory of motor programming: arm and eye movements / G. Schoner et al.
Network models in motor control and music / A. Camurri
Human arm impedance in multi-joint movement / T. TsujiPart III：
Neural Models for flexible control of redundant systems / F.H. Guenthner ; D. Micci Barreca
Models of motor adaptation and impedance control in human arm movements / T. Flash ; I. Gurevich
Control of human arm and jaw motion: issues related to musculo-skeletal geometry / P.L. Gribble et al.
Computational maps and target fields for reaching movements / V. Sanguineti ; P. Morasso
From cortical maps to the control of muscles
Learning to speak: speech production and sensori-motor representations / G. Bailly, et al.
Author Index
Subject Index
Cortical maps of sensorimotor spaces / V. Sanguineti et al.Part I：
Field computation in motor control / B. MacLennan
A probability interpretation of neural population coding for movement / T. Sanger
4.

図書
図書
4. Dynamic patterns : the self-organization of brain and behavior. 1st MIT Press paperback ed (: pbk)
J.A. Scott Kelso
出版情報: Cambridge, Mass. : MIT Press, 1997  xvii, 334 p., [4] p. of plates ; 26 cm
シリーズ名: Bradford book
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5.

図書
図書
5. The language of shape : the role of curvature in condensed matter: physics, chemistry, and biology
Stephen Hyde ... [et al.]
出版情報: Amsterdam ; New York : Elsevier, 1997  xii, 383 p. ; 25 cm
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Preface
The Mathematics of Curvature / 1：
The Lessons of Chemistry / 2：
Inorganic Chemistry: From the discrete lattice of crystal symmetry to the continuous manifolds of differential geometry
Organic Chemistry: The shape of molecules
Molecular Forces and Selfndash;Assembly / 3：
Beyond Flatland. The Geometric Forms due to Selfndash;Assembly / 4：
Lipid Selfndash;Assembly and Function in Biological Systems. Self-association of lipids in an aqueous environment. Cell membranes / 5：
Folding and Function in Proteins and DNA / 6：
Cytomembranes and Cubic Membrane Systems Revisited / 7：
Some Miscellaneous Speculations. Templating. Supra self-assembly / 8：
Index
Preface
The Mathematics of Curvature / 1：
The Lessons of Chemistry / 2：
6.

図書
図書
6. Synergetics of measurement, prediction and control (: pbk)
Igor Grabec, Wolfgang Sachse
出版情報: Berlin ; New York : Springer-Verlag, c1997  xx, 458 p. ; 24 cm
シリーズ名: Springer series in synergetics
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7.

図書
図書
7. Self-organizing maps
Teuvo Kohonen
出版情報: Berlin ; New York : Springer, c1995  ix, 362 p. ; 25 cm
シリーズ名: Springer series in information sciences ; 30
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目次情報: 続きを見る
Mathematical Preliminaries / 1：
Mathematical Concepts and Notations / 1.1：
Vector Space Concepts / 1.1.1：
Matrix Notations / 1.1.2：
Eigenvectors and Eigenvalues of Matrices / 1.1.3：
Further Properties of Matrices / 1.1.4：
On Matrix Differential Calculus / 1.1.5：
Distance Measures for Patterns / 1.2：
Measures of Similarity and Distance in Vector Spaces / 1.2.1：
Measures of Similarity and Distance Between Symbol Strings / 1.2.2：
Averages Over Nonvectorial Variables / 1.2.3：
Statistical Pattern Analysis / 1.3：
Basic Probabilistic Concepts / 1.3.1：
Projection Methods / 1.3.2：
Supervised Classification / 1.3.3：
Unsupervised Classification / 1.3.4：
The Subspace Methods of Classification / 1.4：
The Basic Subspace Method / 1.4.1：
Adaptation of a Model Subspace to Input Subspace / 1.4.2：
The Learning Subspace Method (LSM) / 1.4.3：
Vector Quantization / 1.5：
Definitions / 1.5.1：
Derivation of the VQ Algorithm / 1.5.2：
Point Density in VQ / 1.5.3：
Dynamically Expanding Context / 1.6：
Setting Up the Problem / 1.6.1：
Automatic Determination of Context-Independent Productions / 1.6.2：
Conflict Bit / 1.6.3：
Construction of Memory for the Context-Dependent Productions / 1.6.4：
The Algorithm for the Correction of New Strings / 1.6.5：
Estimation Procedure for Unsuccessful Searches / 1.6.6：
Practical Experiments / 1.6.7：
Neural Modeling / 2：
Models, Paradigms, and Methods / 2.1：
A History of Some Main Ideas in Neural Modeling / 2.2：
Issues on Artificial Intelligence / 2.3：
On the Complexity of Biological Nervous Systems / 2.4：
What the Brain Circuits Are Not / 2.5：
Relation Between Biological and Artificial Neural Networks / 2.6：
What Functions of the Brain Are Usually Modeled? / 2.7：
When Do We Have to Use Neural Computing? / 2.8：
Transformation, Relaxation, and Decoder / 2.9：
Categories of ANNs / 2.10：
A Simple Nonlinear Dynamic Model of the Neuron / 2.11：
Three Phases of Development of Neural Models / 2.12：
Learning Laws / 2.13：
Hebb's Law / 2.13.1：
The Riccati-Type Learning Law / 2.13.2：
The PCA-Type Learning Law / 2.13.3：
Some Really Hard Problems / 2.14：
Brain Maps / 2.15：
The Basic SOM / 3：
A Qualitative Introduction to the SOM / 3.1：
The Original Incremental SOM Algorithm / 3.2：
The "Dot-Product SOM" / 3.3：
Other Preliminary Demonstrations of Topology-Preserving Mappings / 3.4：
Ordering of Reference Vectors in the Input Space / 3.4.1：
Demonstrations of Ordering of Responses in the Output Space / 3.4.2：
Basic Mathematical Approaches to Self-Organization / 3.5：
One-Dimensional Case / 3.5.1：
Constructive Proof of Ordering of Another One-Dimensional SOM / 3.5.2：
The Batch Map / 3.6：
Initialization of the SOM Algorithms / 3.7：
On the "Optimal" Learning-Rate Factor / 3.8：
Effect of the Form of the Neighborhood Function / 3.9：
Does the SOM Algorithm Ensue from a Distortion Measure? / 3.10：
An Attempt to Optimize the SOM / 3.11：
Point Density of the Model Vectors / 3.12：
Earlier Studies / 3.12.1：
Numerical Check of Point Densities in a Finite One-Dimensional SOM / 3.12.2：
Practical Advice for the Construction of Good Maps / 3.13：
Examples of Data Analyses Implemented by the SOM / 3.14：
Attribute Maps with Full Data Matrix / 3.14.1：
Case Example of Attribute Maps Based on Incomplete Data Matrices (Missing Data): "Poverty Map" / 3.14.2：
Using Gray Levels to Indicate Clusters in the SOM / 3.15：
Interpretation of the SOM Mapping / 3.16：
"Local Principal Components" / 3.16.1：
Contribution of a Variable to Cluster Structures / 3.16.2：
Speedup of SOM Computation / 3.17：
Shortcut Winner Search / 3.17.1：
Increasing the Number of Units in the SOM / 3.17.2：
Smoothing / 3.17.3：
Combination of Smoothing, Lattice Growing, and SOM Algorithm / 3.17.4：
Physiological Interpretation of SOM / 4：
Conditions for Abstract Feature Maps in the Brain / 4.1：
Two Different Lateral Control Mechanisms / 4.2：
The WTA Function, Based on Lateral Activity Control / 4.2.1：
Lateral Control of Plasticity / 4.2.2：
Learning Equation / 4.3：
System Models of SOM and Their Simulations / 4.4：
Recapitulation of the Features of the Physiological SOM Model / 4.5：
Similarities Between the Brain Maps and Simulated Feature Maps / 4.6：
Magnification / 4.6.1：
Imperfect Maps / 4.6.2：
Overlapping Maps / 4.6.3：
Variants of SOM / 5：
Overview of Ideas to Modify the Basic SOM / 5.1：
Adaptive Tensorial Weights / 5.2：
Tree-Structured SOM in Searching / 5.3：
Different Definitions of the Neighborhood / 5.4：
Neighborhoods in the Signal Space / 5.5：
Dynamical Elements Added to the SOM / 5.6：
The SOM for Symbol Strings / 5.7：
Initialization of the SOM for Strings / 5.7.1：
The Batch Map for Strings / 5.7.2：
Tie-Break Rules / 5.7.3：
A Simple Example: The SOM of Phonemic Transcriptions / 5.7.4：
Operator Maps / 5.8：
Evolutionary-Learning SOM / 5.9：
Evolutionary-Learning Filters / 5.9.1：
Self-Organization According to a Fitness Function / 5.9.2：
Supervised SOM / 5.10：
The Adaptive-Subspace SOM (ASSOM) / 5.11：
The Problem of Invariant Features / 5.11.1：
Relation Between Invariant Features and Linear Subspaces / 5.11.2：
The ASSOM Algorithm / 5.11.3：
Derivation of the ASSOM Algorithm by Stochastic Approximation / 5.11.4：
ASSOM Experiments / 5.11.5：
Feedback-Controlled Adaptive-Subspace SOM (FASSOM) / 5.12：
Learning Vector Quantization / 6：
Optimal Decision / 6.1：
The LVQ1 / 6.2：
The Optimized-Learning-Rate LVQ1 (OLVQ1) / 6.3：
The Batch-LVQ1 / 6.4：
The Batch-LVQ1 for Symbol Strings / 6.5：
The LVQ2 (LVQ 2.1) / 6.6：
The LVQ3 / 6.7：
Differences Between LVQ1, LVQ2 and LVQ3 / 6.8：
General Considerations / 6.9：
The Hypermap-Type LVQ / 6.10：
The "LVQ-SOM" / 6.11：
Applications / 7：
Preprocessing of Optic Patterns / 7.1：
Blurring / 7.1.1：
Expansion in Terms of Global Features / 7.1.2：
Spectral Analysis / 7.1.3：
Expansion in Terms of Local Features (Wavelets) / 7.1.4：
Recapitulation of Features of Optic Patterns / 7.1.5：
Acoustic Preprocessing / 7.2：
Process and Machine Monitoring / 7.3：
Selection of Input Variables and Their Scaling / 7.3.1：
Analysis of Large Systems / 7.3.2：
Diagnosis of Speech Voicing / 7.4：
Transcription of Continuous Speech / 7.5：
Texture Analysis / 7.6：
Contextual Maps / 7.7：
Artifically Generated Clauses / 7.7.1：
Natural Text / 7.7.2：
Organization of Large Document Files / 7.8：
Statistical Models of Documents / 7.8.1：
Construction of Very Large WEBSOM Maps by the Projection Method / 7.8.2：
The WEBSOM of All Electronic Patent Abstracts / 7.8.3：
Robot-Arm Control / 7.9：
Simultaneous Learning of Input and Output Parameters / 7.9.1：
Another Simple Robot-Arm Control / 7.9.2：
Telecommunications / 7.10：
Adaptive Detector for Quantized Signals / 7.10.1：
Channel Equalization in the Adaptive QAM / 7.10.2：
Error-Tolerant Transmission of Images by a Pair of SOMs / 7.10.3：
The SOM as an Estimator / 7.11：
Symmetric (Autoassociative) Mapping / 7.11.1：
Asymmetric (Heteroassociative) Mapping / 7.11.2：
Software Tools for SOM / 8：
Necessary Requirements / 8.1：
Desirable Auxiliary Features / 8.2：
SOM Program Packages / 8.3：
SOM_PAK / 8.3.1：
SOM Toolbox / 8.3.2：
Nenet (Neural Networks Tool) / 8.3.3：
Viscovery SOMine / 8.3.4：
Examples of the Use of SOMLPAK / 8.4：
File Formats / 8.4.1：
Description of the Programs in SOM_PAK / 8.4.2：
A Typical Training Sequence / 8.4.3：
Neural-Networks Software with the SOM Option / 8.5：
Hardware for SOM / 9：
An Analog Classifier Circuit / 9.1：
Fast Digital Classifier Circuits / 9.2：
SIMD Implementation of SOM / 9.3：
Transputer Implementation of SOM / 9.4：
Systolic-Array Implementation of SOM / 9.5：
The COKOS Chip / 9.6：
The TInMANN Chip / 9.7：
NBISOM_25 Chip / 9.8：
An Overview of SOM Literature / 10：
Books and Review Articles / 10.1：
Early Works on Competitive Learning / 10.2：
Status of the Mathematical Analyses / 10.3：
Zero-Order Topology (Classical VQ) Results / 10.3.1：
Alternative Topological Mappings / 10.3.2：
Alternative Architectures / 10.3.3：
Functional Variants / 10.3.4：
Theory of the Basic SOM / 10.3.5：
The Learning Vector Quantization / 10.4：
Diverse Applications of SOM / 10.5：
Machine Vision and Image Analysis / 10.5.1：
Optical Character and Script Reading / 10.5.2：
Speech Analysis and Recognition / 10.5.3：
Acoustic and Musical Studies / 10.5.4：
Signal Processing and Radar Measurements / 10.5.5：
Industrial and Other Real-World Measurements / 10.5.6：
Process Control / 10.5.8：
Robotics / 10.5.9：
Electronic-Circuit Design / 10.5.10：
Physics / 10.5.11：
Chemistry / 10.5.12：
Biomedical Applications Without Image Processing / 10.5.13：
Neurophysiological Research / 10.5.14：
Data Processing and Analysis / 10.5.15：
Linguistic and AI Problems / 10.5.16：
Mathematical and Other Theoretical Problems / 10.5.17：
Applications of LVQ / 10.6：
Survey of SOM and LVQ Implementations / 10.7：
Glossary of "Neural" Terms / 11：
References
Index
Mathematical Preliminaries / 1：
Mathematical Concepts and Notations / 1.1：
Vector Space Concepts / 1.1.1：
8.

図書
図書
8. Self-organization in nonequilibrium systems : from dissipative structures to order through fluctuations
G. Nicolis, I. Prigogine
出版情報: New York : Wiley, c1977  xii, 491 p. ; 24 cm
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Conservation Equations
Thermodynamics of Irreversible Processes: The Linear Region
Nonlinear Thermodynamics
Systems Involving Chemical Reactions and Diffusion-Stability
Mathematical Tools
Simple Autocatalytic Models
Some further Aspects of Dissipative Structures and Self-Organization Phenomena
General Comments
Birth and Death Descriptions of Fluctuations: Nonlinear Master Equation
Self-Organization in Chemical Reactions
Regulatory Processes at the Subcellular Level
Regulatory Processes at the Cellular Level
Cellular Differentiation and Patter Formation
Thermodynamics of Evolution
Thermodynamics of Ecosystems
Perspectives and Concluding Remarks
References
Index
Conservation Equations
Thermodynamics of Irreversible Processes: The Linear Region
Nonlinear Thermodynamics
9.

図書
図書
9. Synergetics : an introduction : nonequilibrium phase transitions and self-organization in physics, chemistry, and biology. 3rd rev. and enl. ed (: us ; : gw)
Hermann Haken
出版情報: Berlin ; Tokyo : Springer, 1983  xiv, 371 p. ; 25 cm
シリーズ名: Springer series in synergetics ; v. 1
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10.

図書
図書
10. Self-modifying systems in biology and cognitive science : a new framework for dynamics, information and complexity
George Kampis
出版情報: Oxford : Pergamon Press, 1991  xix, 543 p. ; 24 cm
シリーズ名: IFSR international series on systems science and engineering ; 6
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Preface
Introduction
Models of life and mind
Pitfalls of dynamical models
A Constructive Approach to Models
Foundations
Causality and determinism: 'why'-s and 'how'-s
Time and information
Observables
The concept of information set
Encodings of observables into variables
The modelling relation
From Observations to a Theory of Dynamics
The Zeno paradoxes and the shuttle principle
On the notion of state
Anticipation and the existence proof for states
Material and formal implications
The Mechanistic Universe
An alternative view of mechanics
Dynamics as statics: the walled-in universe
The atomistic perspective
The nature of mechanistic systems
Component-Systems: Beyond Algorithmic Dynamics
The concept of component-system
Origins of the concept
Problem properties
The concept of immensity
The main theorem
Universal libraries
Creation and non-algorithmic self-modification
Complexity and Its Increase in Systems
The concept of complexity
Mathematical complexity theory
Relative complexity
Dynamic complexity
The increase of complexity
Self-Reproduction and Computation
Self-replication as a means for existence
Self-reproducing automata
Construction, reproduction and computation
Self-reference and autopoiesis
A model of self-reproduction
The Wigner paradox
The Concept of Information
Syntactic information concepts
Semantic concepts
Towards a causal theory
Two types of information: knowledge and action
Perspective
Law, form, and meaning: three vistas of causality
The Church-Turing hypothesis
References
Index
Authors index
Preface
Introduction
Models of life and mind
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