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, Schöner, 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.

This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.

The idea of complexity states that most things tend to organize themselves into recurring patterns, even when these patterns are not immediately visible to an external observer. The general name for the scientific field concerned with the behaviour over time of a dynamic system is complexity theory. The dynamic systems - systems capable of changing over time - are the focus of this approach, and its concern is with the predictability of their behaviour. The systems of interest to the complexity theory, under certain conditions, perform in regular, predictable ways; under other conditions they exhibit behaviour in which regularity and predictability is lost. The concepts of stable and unstable behaviour are part of the traditional repertoire of physical science. What is novel is the concept of something in between - chaotic behaviour. For chaos here we refer to systems which display behaviour that, though it has certain regularities, defies prediction. How does the order emerge from the chaos? How can we predict the behaviour of a chaotic system?Over the last 30 years and more, trying to identify the hidden patterns behind chaotic behaviour became the focus of attention in a number of scientific disciplines. These range as widely as astronomy, chemistry, evolutionary biology, geology and psychology.

This book focuses on the successes and difficulties of nonlinear studies, particularly in the areas of Mind Sciences. It atttempts to answer the following questions: is an interdisciplinary contamination of complexity studies in different disciplines useful? Does this contamination originate in a transdisciplinary toolbox of methods and models which is worth calling it ?Nonlinear Science?? What are the relations between the metaphoric approach and the mathematical approach in natural sciences and humanities? Complexity in the Life Sciences represents a fundamental workbench for these kinds of problems. The fascinating challenge in these areas is represented by studies on mind functioning.

Proceedings of the 2002 Neural Information Processing Systems Conference.

This book presents the proceedings of the “5th International Interdisciplinary Chaos Symposium on Chaos and Complex Systems (CCS).” All Symposia in the series bring together scientists, engineers, economists and social scientists, creating a vivid forum for discussions on the latest insights and findings obtained in the areas of complexity, nonlinear dynamics and chaos theory, as well as their interdisciplinary applications. The scope of the latest Symposium was enriched with a variety of contemporary, interdisciplinary topics, including but not limited to: fundamental theory of nonlinear dynamics, networks, circuits, systems, biology, evolution and ecology, fractals and pattern formation, nonlinear time series analysis, neural networks, sociophysics and econophysics, complexity management and global systems.

The authors describe mostly in non-technical language the development of a new scientific paradigm based on nonlinear deterministic dynamics and fractal geometry. The concepts from these two mathematical disciplines are interwoven with data from the physical, social and life sciences. In this way rather sophisticated mathematical concepts are made accessible through experimental data from various disciplines, and the formalism is relegated to appendices. It is shown that the complexity of natural and social phenomena invariably lead to inverse power law distributions, both in terms of probabilities and spectra. This book tries to show how to think differently about familiar phenomena, such as why the bell-shape curve ought not to be used in teaching or in the characterization of such complex phenomena as intelligence.

Written for those interested in designing machines to perform intelligent functions & those interested in studying how these functions are performed by living organisms,this bk dicusses the mathematical structure & functional significance of neural oscil

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.

Pulse-coupled neural networks; A neural network model for optical flow computation; Temporal pattern matching using an artificial neural network; Patterns of dynamic activity and timing in neural network processing; A macroscopic model of oscillation in ensembles of inhibitory and excitatory neurons; Finite state machines and recurrent neural networks: automata and dynamical systems approaches; biased random-waldk learning; a neurobiological correlate to trial-and-error; Using SONNET 1 to segment continuous sequences of items; On the use of high-level petri nets in the modeling of biological neural networks; Locally recurrent networks: the gmma operator, properties, and extensions.