## Complex Systems: From Biology to ComputationAlthough the fields of synergetics and co-operative behaviour in neural systems are far from new, the last few years have seen an extraordinary growth of interest in many areas of complex systems. From ecology to economics, from particle physics to parallel computing, a new vocabulary is emerging to describe discoveries about wide-ranging and fundamental phenomena. Many of the terms have already become familiar: artificial life, biocomplexity, cellular automata, chaos, criticality, fractals, learning systems, neural networks, non-linear dynamics, parallel computation, percolation, self-organization and many more. Together they point to the emergence of new paradigms, cutting across traditional disciplines, for dealing with complex systems. |

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### Contents

The Emergence of Connectivity and Fractal Time in the Evolution of Random | 12 |

The WaveCluster Model of WaterProtein Interactions | 36 |

Pattern Formation in Physical and Biological Growth | 55 |

Complex Behaviours from Interaction | 69 |

Methodological Issues Within a Framework to Support a Class of ArtificialLife | 82 |

Computation in Inhomogeneous Cellular Automata | 97 |

Interactive Evolution of LSystem Grammars for Computer Graphics Modelling | 118 |

The Effect of Permeability Heterogeneity on Viscous Fingers in Porous Media | 131 |

An Alternative Approach to Knowledge Elicitation | 232 |

A SelfOrganising Load Balancing System | 242 |

Central Fusion of Sensor Information using Reasoned Feedback | 248 |

Reduction of Modelling Error of Complex Biosystems by an AI Approach | 260 |

Parallel and Emergent Computation | 270 |

Parallel Algorithms for the Distance Embedding Problem | 288 |

Convergence of Symmetric Shunting Competitive Neural Networks | 301 |

The Evolution of Learning Algorithms for Artificial Neural Networks | 313 |

From ?Expansions to Chaos and Fractals | 153 |

The Seduction and Reduction of NonLinear Models | 173 |

Steps to an Ecology of Form | 181 |

Fractal Computer Image Analysis of Particle Morphology | 193 |

Information and Control Systems | 209 |

Complexity in C3I Systems | 223 |

Self Annealing When Learning a Markov Random Field Image Model | 327 |

External Inputs to Attractor Neural Networks | 341 |

A Computer Simulation of Plasticity in the Primary Motor Cortex | 351 |

Neural Dynamics in Biological Visual Information Processing | 361 |

373 | |

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activity algorithm analysis application artificial Australia automaton behaviour binary biological boundary bounded brooding corals C3I systems cell cellular automata chaos chaos theory cluster complex systems components connectivity consider constraints converge cortex curve database defined digraph distance distribution domain dynamical systems effect emergent entities environment equation equilibrium points evolution evolving example excitatory expansion Figure finite fractal dimensions function genetic algorithm given global graph growth image model information systems input interactions iterations L-Systems language larvae layers learning Liapunov function mapping matrix method moment space mutation neural networks neurons nodes output parallel computer parameters patterns permeability pixels problem processors properties protein random reefs represent rules scale Science Section sensor sequence shown shows simulated annealing simulation Soft Systems Methodology space spatial species string structure theory topology trajectories transitions Turing Machine values vector virus viruses wear particles