This book, first published in 2000, explores the exciting field of complexity. It features in-depth coverage of important theoretical areas, including fractals, chaos, non-linear dynamics, artificial life and self-organization. It also provides overviews of complexity in several applied areas, including parallel computation, control systems, neural systems and ecosystems. Some of the properties that best characterize complex systems, including algorithmic richness, non-linearity and abundant interactions between components are examined. In this way the book draws out themes, especially the ideas of connectivity and natural computation, that reveal deep, underlying similarities between phenomena that have formerly been treated as completely distinct. The idea of natural computation is particularly rich in fresh approaches applicable to both biology and computing. Analogies such as the DNA code as life's underlying program, or organisms as automata, are very compelling. Conversely, biologically inspired ideas such as cellular automata, genetic algorithms and neural networks are at the forefront of advanced computing.

This book explores the exciting new field of complexity. It features in-depth coverage of important theoretical areas, including fractals, chaos, nonlinear dynamics, artificial life, and self organization. It also provides overviews of complexity in several applied areas, including parallel computation, control systems, neural systems, and ecosystems. Contributors examine some of the properties that best characterize complex systems, including algorithmic richness, nonlinearity, and abundant interactions between components. In this way the book draws themes, especially the ideas of connectivity and natural computation, that reveal deep, underlying similarities among phenomena that have formerly been treated as completely distinct. Researchers in a wide array of fields, including ecology, neuroscience, computer science, and mathematics, will find this volume to be a fascinating collection of ideas.

An understanding of the senses - vision, hearing, touch, chemical and other non-human senses - is important not only for many fields of biology but also in applied areas such as human-computer interaction, robotics and computer games. Using information theory as a unifying framework, this is a wide-ranging survey of sensory systems, covering all known senses. The book draws on three unifying principles to examine senses: the Nyquist sampling theorem, Shannon's information theory, and the creation of different streams of information to subserve different tasks. This framework is used to discuss the fascinating role of sensory adaptation in the context of environment and lifestyle. Providing a fundamental grounding in sensory perception, the book then demonstrates how this knowledge can be applied to the design of human-computer interfaces and virtual environments. It is an ideal resource for both graduate and undergraduate students of biology, engineering (robotics) and computer science.