Patterns Information And Chaos In Neuronal Systems
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Patterns, Information and Chaos in Neuronal Systems
This is the second volume in a series intended to give clear expositions of the applications of the new techniques developed to understand nonlinear phenomena in the life sciences. The first paper by West, Mackey and Chen is methodological in nature and reviews how to distinguish between noise in biomedical data sets and irregularities generated by deterministic dynamical equations. The second paper by Hock, Schner, Balz, Eastman and Voss addresses the problem of pattern formation and pattern change in the vision system. The authors emphasize the experimental correspondence between quantifiable perceptual phenomena and certain features of nonlinear dynamical systems theory.The paper by Chay focuses on modeling strategies for biological phenomena that manifest strong nonlinear behavior. Biological rhythms and electrical bursting phenomena are discussed in detail, and certain apparently random processes are shown to be describable by chaos. The final paper is an attempt by Nicolis and Katsikas to use nonlinear dynamics systems theory to develop a general theory of linguistics. The concepts of information and pattern recognition are used in concert with that of a dynamic attractor to argue for the general properties of a cognitive processor.
Disrupted Networks
This book provides a lens through which modern society is shown to depend on complex networks for its stability. One way to achieve this understanding is through the development of a new kind of science, one that is not explicitly dependent on the traditional disciplines of biology, economics, physics, sociology and so on; a science of networks. This text reviews, in non-mathematical language, what we know about the development of science in the twenty-first century and how that knowledge influences our world. In addition, it distinguishes the two-tiered science of the twentieth century, based on experiment and theory (data and knowledge) from the three-tiered science of experiment, computation and theory (data, information and knowledge) of the twenty-first century in everything from psychophysics to climate change. This book is unique in that it addresses two parallel lines of argument. The first line is general and intended for a lay audience, but one that is scientifically sophisticated, explaining how the paradigm of science has been changed to accommodate the computer and large-scale computation.The second line of argument addresses what some consider the seminal scientific problem of climate change. The authors show how a misunderstanding of the change in the scientific paradigm has led to a misunderstanding of complex phenomena in general, and the causes of global warming in particular.
Networks of Echoes
Author: Bruce J. West
language: en
Publisher: Springer Science & Business Media
Release Date: 2014-04-03
Networks of Echoes: Imitation, Innovation and Invisible Leaders is a mathematically rigorous and data rich book on a fascinating area of the science and engineering of social webs. There are hundreds of complex network phenomena whose statistical properties are described by inverse power laws. The phenomena of interest are not arcane events that we encounter only fleetingly, but are events that dominate our lives. We examine how this intermittent statistical behavior intertwines itself with what appears to be the organized activity of social groups. The book is structured as answers to a sequence of questions such as: How are decisions reached in elections and boardrooms? How is the stability of a society undermined by zealots and committed minorities and how is that stability re-established? Can we learn to answer such questions about human behavior by studying the way flocks of birds retain their formation when eluding a predator? These questions and others are answered using a generic model of a complex dynamic network—one whose global behavior is determined by a symmetric interaction among individuals based on social imitation. The complexity of the network is manifest in time series resulting from self-organized critical dynamics that have divergent first and second moments, are non-stationary, non-ergodic and non-Poisson. How phase transitions in the network dynamics influence such activity as decision making is a fascinating story and provides a context for introducing many of the mathematical ideas necessary for understanding complex networks in general. The decision making model (DMM) is selected to emphasize that there are features of complex webs that supersede specific mechanisms and need to be understood from a general perspective. This insightful overview of recent tools and their uses may serve as an introduction and curriculum guide in related courses.