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Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain
This coherent mathematical and statistical approach aimed at graduate students incorporates regression and topology as well as graph theory.
Provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. Over the past few years, computer modeling has become more prevalent in the clinical sciences as an alternative to traditional symbol-processing models. This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computer scientists, mathematicians, and interdisciplinary cognitive neuroscientists. The editors (in their introduction) and contributors explain the basic concepts behind modeling and avoid the use of high-level mathematics. The book is divided into four parts. Part I provides an extensive but basic overview of neural network modeling, including its history, present, and future trends. It also includes chapters on attention, memory, and primate studies. Part II discusses neural network models of behavioral states such as alcohol dependence, learned helplessness, depression, and waking and sleeping. Part III presents neural network models of neuropsychological tests such as the Wisconsin Card Sorting Task, the Tower of Hanoi, and the Stroop Test. Finally, part IV describes the application of neural network models to dementia: models of acetycholine and memory, verbal fluency, Parkinsons disease, and Alzheimer's disease. Contributors J. Wesson Ashford, Rajendra D. Badgaiyan, Jean P. Banquet, Yves Burnod, Nelson Butters, John Cardoso, Agnes S. Chan, Jean-Pierre Changeux, Kerry L. Coburn, Jonathan D. Cohen, Laurent Cohen, Jose L. Contreras-Vidal, Antonio R. Damasio, Hanna Damasio, Stanislas Dehaene, Martha J. Farah, Joaquin M. Fuster, Philippe Gaussier, Angelika Gissler, Dylan G. Harwood, Michael E. Hasselmo, J, Allan Hobson, Sam Leven, Daniel S. Levine, Debra L. Long, Roderick K. Mahurin, Raymond L. Ownby, Randolph W. Parks, Michael I. Posner, David P. Salmon, David Servan-Schreiber, Chantal E. Stern, Jeffrey P. Sutton, Lynette J. Tippett, Daniel Tranel, Bradley Wyble
For convenience, many of the proofs of the key theorems have been rewritten so that the entire book uses a relatively uniform notion.
Over 220,000 entries representing some 56,000 Library of Congress subject headings. Covers all disciplines of science and technology, e.g., engineering, agriculture, and domestic arts. Also contains at least 5000 titles published before 1876. Has many applications in libraries, information centers, and other organizations concerned with scientific and technological literature. Subject index contains main listing of entries. Each entry gives cataloging as prepared by the Library of Congress. Author/title indexes.
Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound impact in the elucidation of complex biological, chemical, and environmental processes. Artificial Neural Networks in Biological and Environmental Analysis provides an in-depth and timely perspective on the fundamental, technological, and applied aspects of computational neural networks. Presenting the basic principles of neural networks together with applications in the field, the book stimulates communication and partnership among scientists in fields as diverse as biology, chemistry, mathematics, medicine, and environmental science. This interdisciplinary discourse is essential not only for the success of independent and collaborative research and teaching programs, but also for the continued interest in the use of neural network tools in scientific inquiry. The book covers: A brief history of computational neural network models in relation to brain function Neural network operations, including neuron connectivity and layer arrangement Basic building blocks of model design, selection, and application from a statistical perspective Neurofuzzy systems, neuro-genetic systems, and neuro-fuzzy-genetic systems Function of neural networks in the study of complex natural processes Scientists deal with very complicated systems, much of the inner workings of which are frequently unknown to researchers. Using only simple, linear mathematical methods, information that is needed to truly understand natural systems may be lost. The development of new algorithms to model such processes is needed, and ANNs can play a major role. Balancing basic principles and diverse applications, this text introduces newcomers to the field and reviews recent developments of interest to active neural network practitioners.
As seen on "CBS This Morning" Worldwide, depression will be the single biggest cause of disability in the next twenty years. But treatment for it has not changed much in the last three decades. In the world of psychiatry, time has apparently stood still...until now with Edward Bullmore's The Inflamed Mind: A Radical New Approach to Depression. A Sunday Times (London) Top Ten Bestseller In this game-changing book, University of Cambridge profressor of psychiatry Edward Bullmore reveals the breakthrough new science on the link between depression and inflammation of the body and brain. He explains how and why we now know that mental disorders can have their root cause in the immune system, and outlines a future revolution in which treatments could be specifically targeted to break the vicious cycles of stress, inflammation, and depression. The Inflamed Mind goes far beyond the clinic and the lab, representing a whole new way of looking at how mind, brain, and body all work together in a sometimes misguided effort to help us survive in a hostile world. It offers insights into how we could start getting to grips with depression and other mental disorders much more effectively in the future.
This book applies the design principles of cellular image operators to a hardware platform called cellular neural network (CNN). CNN is a member of the hardware family called vision chips. Based on state-of-the-art technology, a vision chip is defined as a VLSI chip that can perform image processing tasks. CNN is mostly nourished from two main fields: One is cellular automata and the other is neural network. As an interdisciplinary product, the study of CNN mainly focuses on finding specialised design principle called template design. CNN utilises cellular hardware structures to gain ultrahigh image processing speed.
An introduction to neural networking for systems designers, software developers, programmers, and advanced hobbyists. The authors explain how "brain-style" computing will revolutionize information processing in the 21st century. The disk includes programs for simulating artificial neural networks.
The rapid and continuing growth on liquid crystal research is not only the result of the high success of liquid crystal display technology, but also because of the great potential for new and improved applications. This is a unique area of scientific research in which the joint research efforts of chemists, physicists and material scientists have led to spectacular practical developments which are been exploited commercially. This two-volume set of the series Structure and Bonding focuses on the structural properties of liquid crystals. The balanced, in-depth coverage of both theoretical and experimental aspects by leading experts serves as a basis for further innovations in this dynamic field and makes these volumes an essential resource for both academic and industrial researchers.
Fundamentals of Cognitive Neuroscience is a comprehensive and easy-to-follow guide to cognitive neuroscience. Winner of a 2013 Most Promising New Textbook Award from the Text and Academic Authors Association, this book was written by two leading experts in the field to be highly accessible to undergraduates with limited neuroscience training. It covers all aspects of the field—the neural framework, sight, sound, consciousness, learning/memory, problem solving, speech, executive control, emotions, socialization and development—in a student-friendly format with extensive pedagogy and ancillaries to aid both the student and professor. This introductory text takes a unique thematic approach, guiding students along a clear path to understand the latest findings whether or not they have a background in neuroscience. It includes case studies and everyday examples designed to help students understand the more challenging aspects of the material. It is richly illustrated with carefully selected color graphics to enhance understanding. Enhanced pedagogy highlights key concepts for the student and aids in teaching. Chapter outlines, study questions, glossary, and image collection are also available on the student's companion website. Ancillary support saves instructors time and facilitates learning; test questions, image collection, and lecture slides are available on the instructor's manual website. This book will be of interest to undergraduate students in Neuroscience, Psychology, and related disciplines that teach cognitive neuroscience. Provides a complete introduction to mind-brain science, written to be highly accessible to undergraduates with limited neuroscience training Richly illustrated with carefully selected color graphics to enhance understanding Enhanced pedagogy highlights key concepts for the student and aids in teaching - chapter outlines, study questions, glossary, and image collection are also available on student's companion website Ancillary support saves instructors time and facilitates learning - test questions, image collection, and lecture slides available on instructor's manual website

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