The essence of brain function consists in how information is processed, transferred and stored. Current neurophysiological doctrine remains focused within a spike timing paradigm, but this has a limited capacity for advancing the understanding of how the brain works. This book puts forward a new model; the neuroelectrodynamic model (NED), which describes the intrinsic computational processes by the dynamics and interaction of charges. It uses established laws of physics, such as those of classical mechanics, thermodynamics and quantum physics, as the guiding principle to develop a general theoretical construct of the brain s computational model, which incorporates the neurobiology of the cells and the molecular machinery itself, along with the electrical activity in neurons, to explain experimental results and predict the organization of the system. After addressing the deficiencies of current approaches, the laws and principles required to build a new model are discussed. In addition, as well as describing experiments which provide the required link between computation and semantics, the book highlights important concepts relating the theory of information with computation and the electrical properties of neurons. The NED model is explained and expounded and several examples of its application are shown. Of interest to all those involved in the fields of neuroscience, neurophysiology, computer science and the development of artificial intelligence, NED is a step forward in understanding the mind in computational terms. IOS Press is an international science, technical and medical publisher of high-quality books for academics, scientists, and professionals in all fields. Some of the areas we publish in: -Biomedicine -Oncology -Artificial intelligence -Databases and information systems -Maritime engineering -Nanotechnology -Geoengineering -All aspects of physics -E-governance -E-commerce -The knowledge economy -Urban studies -Arms control -Understanding and responding to terrorism -Medical informatics -Computer Sciences

Neural field theory has a long-standing tradition in the mathematical and computational neurosciences. Beginning almost 50 years ago with seminal work by Griffiths and culminating in the 1970ties with the models of Wilson and Cowan, Nunez and Amari, this important research area experienced a renaissance during the 1990ties by the groups of Ermentrout, Robinson, Bressloff, Wright and Haken. Since then, much progress has been made in both, the development of mathematical and numerical techniques and in physiological refinement und understanding. In contrast to large-scale neural network models described by huge connectivity matrices that are computationally expensive in numerical simulations, neural field models described by connectivity kernels allow for analytical treatment by means of methods from functional analysis. Thus, a number of rigorous results on the existence of bump and wave solutions or on inverse kernel construction problems are nowadays available. Moreover, neural fields provide an important interface for the coupling of neural activity to experimentally observable data, such as the electroencephalogram (EEG) or functional magnetic resonance imaging (fMRI). And finally, neural fields over rather abstract feature spaces, also called dynamic fields, found successful applications in the cognitive sciences and in robotics. Up to now, research results in neural field theory have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. There is no comprehensive collection of results or reviews available yet. With our proposed book Neural Field Theory, we aim at filling this gap in the market. We received consent from some of the leading scientists in the field, who are willing to write contributions for the book, among them are two of the founding-fathers of neural field theory: Shun-ichi Amari and Jack Cowan.

Neural Network Simulation Environments describes some of the best examples of neural simulation environments. All current neural simulation tools can be classified into four overlapping categories of increasing sophistication in software engineering. The least sophisticated are undocumented and dedicated programs, developed to solve just one specific problem; these tools cannot easily be used by the larger community and have not been included in this volume. The next category is a collection of custom-made programs, some perhaps borrowed from other application domains, and organized into libraries, sometimes with a rudimentary user interface. More recently, very sophisticated programs started to appear that integrate advanced graphical user interface and other data analysis tools. These are frequently dedicated to just one neural architecture/algorithm as, for example, three layers of interconnected artificial `neurons' learning to generalize input vectors using a backpropagation algorithm. Currently, the most sophisticated simulation tools are complete, system-level environments, incorporating the most advanced concepts in software engineering that can support experimentation and model development of a wide range of neural networks. These environments include sophisticated graphical user interfaces as well as an array of tools for analysis, manipulation and visualization of neural data. Neural Network Simulation Environments is an excellent reference for researchers in both academia and industry, and can be used as a text for advanced courses on the subject.

In recent years there has been tremendous activity in computational neuroscience resulting from two parallel developments. On the one hand, our knowledge of real nervous systems has increased dramatically over the years; on the other, there is now enough computing power available to perform realistic simulations of actual neural circuits. This is leading to a revolution in quantitative neuroscience, which is attracting a growing number of scientists from non-biological disciplines. These scientists bring with them expertise in signal processing, information theory, and dynamical systems theory that has helped transform our ways of approaching neural systems. New developments in experimental techniques have enabled biologists to gather the data necessary to test these new theories. While we do not yet understand how the brain sees, hears or smells, we do have testable models of specific components of visual, auditory, and olfactory processing. Some of these models have been applied to help construct artificial vision and hearing systems. Similarly, our understanding of motor control has grown to the point where it has become a useful guide in the development of artificial robots. Many neuroscientists believe that we have only scratched the surface, and that a more complete understanding of biological information processing is likely to lead to technologies whose impact will propel another industrial revolution. Neural Systems: Analysis and Modeling contains the collected papers of the 1991 Conference on Analysis and Modeling of Neural Systems (AMNS), and the papers presented at the satellite symposium on compartmental modeling, held July 23-26, 1992, in San Francisco, California. The papers included, present an update of the most recent developments in quantitative analysis and modeling techniques for the study of neural systems.

This unique volume brings together eastern and western perspectives on consciousness with essays from philosophers and scientists which emphasize different aspects of the integration. The overarching aim of this book is to provide direction toward integrating Eastern philosophical and religious practice with philosophies and science of Western culture, an aim that could be pivotal in understanding consciousness and its place in nature. A unifying approach is adopted to the study of consciousness, integrating the wisdom of the sages of the east, and the scientists of the west and the stupendous east-west integration that has been achieved is indeed a milestone. The book will appeal to the rapidly growing mass of scientists and students in this upcoming field, both in the east and west, as well as the general inquisitive reader. Courses in consciousness studies are being promoted in leading Universities all over the world. It will also interest the followers and adherents of Eastern Philosophy of Saints and Radhasoami Faith numbering in a few millions around the globe.

Habitability of the Universe before Earth: Astrobiology: Exploring Life on Earth and Beyond (series) examines the times and places—before life existed on Earth—that might have provided suitable environments for life to occur, addressing the question: Is life on Earth de novo, or derived from previous life? The universe changed considerably during the vast epoch between the Big Bang 13.8 billion years ago and the first evidence of life on Earth 4.3 billion years ago, providing significant time and space to contemplate where, when and under what circumstances life might have arisen. No other book covers this cosmic time period from the point of view of its potential for life. The series covers a broad range of topics encompassing laboratory and field research into the origins and evolution of life on Earth, life in extreme environments and the search for habitable environments in our solar system and beyond, including exoplanets, exomoons and astronomical biosignatures. - Provides multiple hypotheses on the origin of life and distribution of living organisms in space - Explores the diversity of physical environments that may support the origin and evolution of life - Integrates contemporary views in biology and cosmology, and provides reasons that life is far more mobile in space than most people expect - Includes access to a companion web site featuring supplementary information such as animated computer simulations

The purpose of the book is to advance in the understanding of brain function by defining a general framework for representation based on category theory. The idea is to bring this mathematical formalism into the domain of neural representation of physical spaces, setting the basis for a theory of mental representation, able to relate empirical findings, uniting them into a sound theoretical corpus. The innovative approach presented in the book provides a horizon of interdisciplinary collaboration that aims to set up a common agenda that synthesizes mathematical formalization and empirical procedures in a systemic way. Category theory has been successfully applied to qualitative analysis, mainly in theoretical computer science to deal with programming language semantics. Nevertheless, the potential of category theoretic tools for quantitative analysis of networks has not been tackled so far. Statistical methods to investigate graph structure typically rely on network parameters. Category theory can be seen as an abstraction of graph theory. Thus, new categorical properties can be added into network analysis and graph theoretic constructs can be accordingly extended in more fundamental basis. By generalizing networks using category theory we can address questions and elaborate answers in a more fundamental way without waiving graph theoretic tools. The vital issue is to establish a new framework for quantitative analysis of networks using the theory of categories, in which computational neuroscientists and network theorists may tackle in more efficient ways the dynamics of brain cognitive networks. The intended audience of the book is researchers who wish to explore the validity of mathematical principles in the understanding of cognitive systems. All the actors in cognitive science: philosophers, engineers, neurobiologists, cognitive psychologists, computer scientists etc. are akin to discover along its pages new unforeseen connections through the development of concepts and formal theories described in the book. Practitioners of both pure and applied mathematics e.g., network theorists, will be delighted with the mapping of abstract mathematical concepts in the terra incognita of cognition.