Spring School in Complexity Science, Southampton 2006
49 Lectures · Mar 28, 2006
About
These new courses (designed in conjunction with our new MSc Complexity Science) are designed for postgraduate students (perhaps early in their PhD studies or on masters courses) and industrial employees wishing to learn about the principles and challenges of complexity in natural and engineered systems.
The courses are designed to addresses the critical challenges currently facing the computational and engineering industries by focusing on the underlying principles of complexity in common with biological systems. Emphasis is placed on two complementary objectives. You will learn how to use advanced techniques to understand the robust, adaptive and self-organising properties of biological and natural systems, and also how to exploit these properties to address the pressing problems presented by the increasing scale and connectivity of today's engineered systems. The course focuses on developing core mathematical modelling and simulation skills, providing a firm knowledge base in relevant biology, and command of established techniques in biologically inspired computing.
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Lectures
Introduction
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Introduction to Complexity Science
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Engineered Complexity
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Working with Systems
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Engineered Complexity
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Complexity: Scale and Connectivity
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Adaptive Behaviour and Emergence
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Interdisciplinarity
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Science, Models and Theories
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Simulation Modeling: Issues - Part 1
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Simulation Modeling - Part 2
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Simulation Modeling: Examples
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Designing a Simulation Model
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Simulation Modeling: Hints and Tips
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Simulation Modeling: Group Work
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Plains, Brains and Automobiles
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An Introduction to Network Theory and Spatial Networks
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The Geography of Scientific Productivity: Scaling in U.S. Computer Science
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Maapping European Research Networks
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Constructing a Framework for the Analysis of Complex Geographical Systems
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Understanding Spatiality
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Understanding Spatiality in the Brain or: does Spatiality matter?
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Exploring Spatially Embedded Artificial Neural Networks
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Air Traffic Control: Issues
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Adaptive Modelling via Pattern Analysis and the Kernel Methods approach
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Physical Substrates
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Random Walks
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Random Exploration and Stabilisation of Cellular Architecture
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Probabilistic and Bayesian Modelling I
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Robustness and Adaptation in Biochemical Networks of Bacterial Chemotaxis
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Rectifaying Fluctuations
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Probabilistic and Bayesian Modelling II
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Organisational Resources in Biological System
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Evolution of Complexity
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Evolutionary Algorithms
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Evolution and Evolvability
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Optimisation
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Coevolution
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Coevolution in EC 1
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Coevolution in EC 2
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Analysis of Dynamics
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Evolution of Sex
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Sex in Evolutionary Biology and Evolutionary Computation
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Origins of Sex
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Modularity
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Sex and Units of Selection
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Compositional Evolution
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Fitness Landscapes
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Probing Landscapes
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