Complex systems : from biology to computation
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Complex systems : from biology to computation
IOS Press, 1993
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Includes bibliographical references and index
Description and Table of Contents
Description
Although 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. One of the contributing factors to this rapid growth is the extraordinary increase in computing power over the last decade. Microprocessors have of course become much faster, but parallel computing has also come of age. Previously intractable non-linear systems are now amenable to analysis and simulation and parallel computers are ever more important in these areas.
But at a more fundamental level, we see that parallel computation is intrinsic to many natural phenomena. The papers in this volume explore many aspects of complex systems. They cover both theory and applications and deal with material drawn from many different disciplines and specialities. Throughout all the papers, however, runs the common theme of "emergent computation". Each paper deals with some aspect of this theme. The distinguishing feature of complex systems is that patterns and behaviours emerge from nonlinearities or interactions between the components. In this respect, complex systems research is inherently anti-reductionalist. The subtlety of the world we live in comes from the parallel interaction of many individuals.
Table of Contents
- Part 1 Life - natural and artificial: as large as life and twice as natural - bioinformatics and the artificial life, Paulien Hogeweg
- the emergence of connectivity and fractal time in the evolution of random diagraphs, Doug Seeley and Simon Ronald
- emergent behaviour in biological systems, David G. Green
- the wave-cluster model of water-protein interactions, John Watterson
- computer viruses - the inevitability of evolution?, Paul-Michael Agapow
- pattern formation in physical and biological growth, Tony Roberts and Mark A. Knackstedt
- recovery of model coral communities - complex behaviours from interaction of parameters operating at different spatial scales, Ann L. Preece and Craig R. Johnson
- methodological issues within a framework to support a class of artificial-life worlds in cellular automata, Pedro Paulo Balbi de Oliveira
- computation in inhomogeneous cellular automata, Zoran Aleksic. Part 2 Fractals, chaos and nonlinear dynamics: nonlinear dynamics and chaos in musical instruments, Neville H. Fletcher
- interactive evolution of L-system grammars for computer graphics modelling, Jon McCormack
- the effect of permeability heterogeneity on viscous fingers in porous media, Mark Knackstedt and Muhammad Sahimi
- recognition and generation of fractal patterns by using syntactic techniques, Jacques Blanc-Talon
- from beta-expansions to chaos and fractals, Dominique Luzeaux
- the uniform emergence of points on a circle, Keith Tognetti and Graham Winley
- complexity and emergence - the seduction and reduction of nonlinear models in the social sciences, Margot L. Lyon
- steps to an ecology of form, Jean Pierre Paillet
- fractal computer image analysis of particle morphology, Thomas B. Kirk and Gwidon W. Stachowiak. Part 3 Information and control systems: taming large complex information systems, C.N.G. (Kit) Dampney et al
- complexity in C31 systems, Clive Cooper
- soft systems methodology - an alternative approach to knowledge elicitation in complex and poorly defined systems, Andrew Finegan
- a self-organizing load balancing system, George M. Bryan and Wane E. Moore
- central fusion of sensor information using reasoned feedback, Tim Payne
- reduction of modelling error of complex biosystems by an AI approach, Patrick C. Fu and John P. Barford. Part 4 Parallel and emergent computation: parallel computers and complex systems, Geoffrey C. Fox
- parallel algorithms for distance embedding problem, Hong Xie
- convergence of symmetric shunting in competitive neural networks, Abdesselam Bouzerdoum
- the evolution of learning algorithms for artificial neural networks, Jonathan Baxter
- self-annealing when learning a Markov random field image model, David Howard and William Moran
- external inputs to attractor neural networks, Anthony N. Burkitt
- a computer simulation of plasticity in the primary motor cortex, David Cake
- neural dynamics in biological visual information processing, Terry Bossomaier et al.
by "Nielsen BookData"
