## Complex Systems: From Biology to Computation |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

Emergent Behaviour in Biological Systems | 24 |

the Inevitability of Evolution? | 46 |

Complex Behaviours from Interaction | 68 |

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

Computation in Inhomogeneous Cellular Automata | 98 |

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

The Effect of Permeability Heterogeneity on Viscous Fingers in Porous Media | 130 |

From BExpansions to Chaos and Fractals | 152 |

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 | 302 |

The Evolution of Learning Algorithms for Artificial Neural Networks | 314 |

Self Annealing When Learning a Markov Random Field Image Model | 328 |

Steps to an Ecology of Form | 180 |

Fractal Computer Image Analysis of Particle Morphology | 192 |

Information and Control Systems | 208 |

Complexity in C31 Systems | 222 |

An Alternative Approach to Knowledge Elicitation | 232 |

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 | |

### Other editions - View all

### Common terms and phrases

activity algorithm analysis application approach artificial automata behaviour boundary bounded called cell cellular changes chaos communication complex systems connectivity consider consists corals curve defined depends described determined dimension directed discussion distance distribution domain dynamics effect emergent environment equation evolution example exist field Figure fractal function given graph growth important increasing initial input interactions language learning limit machine mapping means method natural neural neurons nodes observed occur operating organism output parallel parameters particles particular patterns physical points positive possible present probability problem produce properties random reefs References represent rules scale Science selection sensor sequence shown shows simple simulation social space spatial step string structure surface theory trajectories transitions units University values weights