ISBN: 3540672028
TITLE: Complex Systems: Chaos and Beyond
AUTHOR: Kaneko, Kunihiko; Tsuda, Ichiro
TOC:

1. Necessity for a Science of Complex Systems 1
1.1 Introduction 1
1.2 Chaos 4
1.3 Chaos and Complexity 8
1.4 How Has Chaos Changed Our Way of Thinking? 11
1.4.1 Dialectic Method to Overcome the Antithesis Between Determinism and Nondeterminism or Between Programs and Errors 11
1.4.2 Dialectic Method to Overcome the Antithesis Between Order and Randomness 12
1.4.3 Beyond the Antithesis Between Reductionism and Holism 12
1.5 Dynamic Many-to-Many Relations and Bio-networks 13
1.5.1 The Necessity of Dynamic Many-to-Many Relations 13
1.5.2 Metabolic Systems, Differentiation, and Development 15
1.5.3 Ecosystems 16
1.5.4 Immune Systems 17
1.5.5 The Brain 18
1.5.6 Rugged Landscapes and Their Problems 18
1.5.7 Conclusion 20
1.6 The Construction of an Artificial (Virtual) World 21
1.7 A Trigger to Emergence 24
1.8 Beyond Top-Down Versus Bottom-Up 26
1.9 Methodology of Study of Complex Systems 28
1.9.1 Constructive Way of Understanding 29
1.9.2 Plural Views 30
1.9.3 Mathematical Anatomy 31
1.9.4 The Problem of Internal Observers 31
2. Observation Problems from an Information-Theoretical Viewpoint 33
2.1 Observation Problems of Chaos 33
2.2 Undecidability and Entire Description 37
2.3 A Demon in Chaos 38
2.4 Chaos in the BZ Reaction 39
2.5 Noise-Induced Order 43
2.6 Could Structural Stability Lead to an Adequate Notion of a Model? 47
2.7 Information Theory of Chaos 50
3. CMLs: Constructive Approach to Spatiotemporal Chaos 57
3.1 From a Descriptive to a Constructive Approach of Nature 57
3.2 Coupled Map Lattice Approach to Spatiotemporal Chaos 59
3.2.1 Spatiotemporal Chaos 59
3.2.2 Introduction to Coupled Map Lattices 61
3.2.3 Comparison with Other Approaches 64
3.3 Phenomenology of Spatiotemporal Chaos in the Diffusively Coupled Logistic Lattice 65
3.3.1 Introduction 65
3.3.2 Frozen Random Patterns and Spatial Bifurcations 66
3.3.3 Pattern Selection with Suppression of Chaos 69
3.3.4 Brownian Motion of Chaotic Defects and Defect Turbulence 70
3.3.5 Spatiotemporal Intermittency (STI) 71
3.3.6 Stability of Fully Developed Spatiotemporal Chaos (FDSTC) Sustained by the Supertransients 75
3.3.7 Traveling Waves 77
3.3.8 Supertransients 81
3.4 CML Phenomenology as a Problem of Complex Systems 83
3.5 Phenonemology in Open-Flow Lattices 84
3.5.1 Introduction 84
3.5.2 Spatial Bifurcation to Down-Flow 85
3.5.3 Convective Instability and Spatial Amplification of Fluctuations 86
3.5.4 Phase Diagram 89
3.5.5 Spatial Chaos 91
3.5.6 Selective Amplification of Input 93
3.6 Universality 94
3.7 Theory for Spatiotemporal Chaos 97
3.8 Applications of Coupled Map Lattices 100
3.8.1 Pattern Formation (Spinodal Decomposition) 100
3.8.2 Crystal Growth and Boiling 101
3.8.3 Convection 101
3.8.4 Spiral and Traveling Waves in Excitable Media 103
3.8.5 Cloud Dynamics and Geophysics 104
3.8.6 Ecological Systems 104
3.8.7 Evolution 104
3.8.8 Closing Remarks 105
4. Networks of Chaotic Elements 107
4.1 GCM Model 107
4.2 Clustering 111
4.3 Phase Transitions Between Clustering States 115
4.4 Ordered Phase and Cluster Bifurcation 117
4.5 Hierarchical Clustering and Chaotic Itinerancy 122
4.5.1 Partition Complexity 122
4.5.2 Hierarchical Clustering 125
4.5.3 Hierarchical Dynamics 128
4.5.4 Chaotic Itinerancy 132
4.6 Marginal Stability and Information Cascade 135
4.6.1 Marginal Stability 135
4.6.2 Information Cascade 139
4.7 Collective Dynamics 143
4.7.1 Remnant Mean-Field Fluctuation 143
4.7.2 Hidden Coherence 146
4.7.3 Instability of the Fixed Point of the PerronFrobenius Operator 150
4.7.4 Destruction of Hidden Coherence by Noise and Anomalous Fluctuations 153
4.7.5 Heterogeneous Systems155
4.7.6 Significance of Collective Dynamics 156
4.8 Universality and Nonuniversality 157
4.8.1 Universality of Clustering and Other Transitions 157
4.8.2 Globally Coupled Tent Map: Novelty Within Universality 159
5. Significance of Coupled Chaotic Systems to Biological Networks 163
5.1 Relevance of Coupled Maps to Biological Information Processing 163
5.2 Application of Coupled Maps to Information Processing 164
5.2.1 Memory to Attractor Mapping and the Switching Process 164
5.2.2 Chaotic Itinerancy and Spontaneous Recall 168
5.2.3 Optimization and Search by Spatiotemporal Chaos as Spatiotemporally Structured Noise 170
5.2.4 LocalGlobal Transformation by Traveling Waves  Information Creation and Transmission by Chaotic Traveling Waves 170
5.2.5 Selective Amplification of Input Signals by the Unidirectionally Coupled Map Lattice 170
5.3 Information Dynamics of a CML with One-Way Coupling 171
5.4 Design of Coupled Maps and Plastic Dynamics 175
5.5 Construction of Dynamic Many-to-Many Logic and Information Processing 178
5.6 Implications to Biological Networks 179
5.6.1 Prototype of Hierarchical Structures 180
5.6.2 Prototype of Diversity and Differentiation 180
5.6.3 Formation and Collapse of Relationships 184
5.6.4 Clustering in Hypercubic Coupled Maps; Self-organizing Genetic Algorithms 184
5.6.5 Homeochaos 186
5.6.6 Summing Up 189
6. Chaotic Information Processing in the Brain 191
6.1 Hermeneutics of the Brain 191
6.2 A Brief Comment on Hermeneutics (the Inside and the Outside) 194
6.3 A Method for Understanding the Brain and Mind  Internal Description 195
6.4 Evidence of Chaos in Nervous Systems 196
6.5 The Origin of Neurochaos 198
6.6 The Implications of Stochastic Renewal of Maps 203
6.6.1 Chaotic Game 203
6.6.2 Skew-Product Transformations 204
6.7 A Model for Dynamic Memory 205
6.8 A Model for Dynamically Linking Memories 206
6.9 Significance of Neurochaos 212
6.10 Temporal Coding 214
6.11 Capillary Chaos as a Complex Dynamics 219
6.11.1 Significance of Capillary Pulsation in the Brain Functions 219
6.11.2 Embedding Theorems 220
6.11.3 Experimental Systems 221
6.11.4 Reconstruction of the Dynamics 222
6.11.5 Calculations of Lyapunov Exponents 224
6.11.6 The Condition Dependence 226
6.11.7 Cardiac Chaos 230
6.11.8 Information Structure 231
6.11.9 Implications of Capillary Chaos 235
7. Conversations with Authors 237
7.1 Concluding Discussions237
7.2 Questions and Answers 239
7.2.1 The Significance of Models
in Complex Systems Research 239
7.2.2 Chaotic Itinerancy 243
7.2.3 New Information Theory and Internal Observation 246
References 251
Index 267
END
