Encyclopedia Of Computational Neuroscience

The annual Computational Neuroscience Meeting (CNS) began in 1990 as a small workshop called Analysis and Modeling of Neural Systems. The goal of the workshop was to explore the boundary between neuroscience and computation. Riding on the success of several seminal papers, physicists had made "Neural Networks" fashionable, and soon the quantitative methods used in these abstract model networks started permeating the methods and ideas of experimental neuroscientists. Although experimental neurophysiological approaches provided many advances, it became increasingly evident that mathematical and computational techniques would be required to achieve a comprehensive and quantitative understanding of neural system function. “Computational Neuroscience” emerged to complement experimental neurophysiology. The Encyclopedia of Computational Neuroscience, published in conjunction with the Organization for Computational Neuroscience, will be an extensive reference work consultable by both researchers and graduate level students. It will be a dynamic, living reference, updatable and containing linkouts and multimedia content whenever relevant.

This book will provide answers to various questions related to neuroscience. The concepts covered in the book include computational, cellular, cognitive, and clinical neuroscience. This book also includes researches discussing functions of brain using optical tools and social neuroscience. Recent developments in neuroscience have also been discussed at great length. The focus of the book is not only on the structure of brain but also on the function of brain, thereby presenting chapters which integrate researches of nervous system and brain together.

This 5000-page masterwork is literally the last word on the topic and will be an essential resource for many. Unique in its breadth and detail, this encyclopedia offers a comprehensive and highly readable guide to a complex and fast-expanding field. The five-volume reference work gathers more than 10,000 entries, including in-depth essays by internationally known experts, and short keynotes explaining essential terms and phrases. In addition, expert editors contribute detailed introductory chapters to each of 43 topic fields ranging from the fundamentals of neuroscience to fascinating developments in the new, inter-disciplinary fields of Computational Neuroscience and Neurophilosophy. Some 1,000 multi-color illustrations enhance and expand the writings.

This 5000-page masterwork is literally the last word on the topic and will be an essential resource for many. Unique in its breadth and detail, this encyclopedia offers a comprehensive and highly readable guide to a complex and fast-expanding field. The five-volume reference work gathers more than 10,000 entries, including in-depth essays by internationally known experts, and short keynotes explaining essential terms and phrases. In addition, expert editors contribute detailed introductory chapters to each of 43 topic fields ranging from the fundamentals of neuroscience to fascinating developments in the new, inter-disciplinary fields of Computational Neuroscience and Neurophilosophy. Some 1,000 multi-color illustrations enhance and expand the writings.

"This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro-psychological models and computational models"--

Learn to use computational modelling techniques to understand the nervous system at all levels, from ion channels to networks.

Since the 1970s the cognitive sciences have offered multidisciplinary ways of understanding the mind and cognition. The MIT Encyclopedia of the Cognitive Sciences (MITECS) is a landmark, comprehensive reference work that represents the methodological and theoretical diversity of this changing field. At the core of the encyclopedia are 471 concise entries, from Acquisition and Adaptationism to Wundt and X-bar Theory. Each article, written by a leading researcher in the field, provides an accessible introduction to an important concept in the cognitive sciences, as well as references or further readings. Six extended essays, which collectively serve as a roadmap to the articles, provide overviews of each of six major areas of cognitive science: Philosophy; Psychology; Neurosciences; Computational Intelligence; Linguistics and Language; and Culture, Cognition, and Evolution. For both students and researchers, MITECS will be an indispensable guide to the current state of the cognitive sciences.

Explains the relationship of electrophysiology, nonlinear dynamics, and the computational properties of neurons, with each concept presented in terms of both neuroscience and mathematics and illustrated using geometrical intuition. In order to model neuronal behavior or to interpret the results of modeling studies, neuroscientists must call upon methods of nonlinear dynamics. This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience. It also provides an overview of neuroscience for mathematicians who want to learn the basic facts of electrophysiology. Dynamical Systems in Neuroscience presents a systematic study of the relationship of electrophysiology, nonlinear dynamics, and computational properties of neurons. It emphasizes that information processing in the brain depends not only on the electrophysiological properties of neurons but also on their dynamical properties. The book introduces dynamical systems, starting with one- and two-dimensional Hodgkin-Huxley-type models and continuing to a description of bursting systems. Each chapter proceeds from the simple to the complex, and provides sample problems at the end. The book explains all necessary mathematical concepts using geometrical intuition; it includes many figures and few equations, making it especially suitable for non-mathematicians. Each concept is presented in terms of both neuroscience and mathematics, providing a link between the two disciplines. Nonlinear dynamical systems theory is at the core of computational neuroscience research, but it is not a standard part of the graduate neuroscience curriculum—or taught by math or physics department in a way that is suitable for students of biology. This book offers neuroscience students and researchers a comprehensive account of concepts and methods increasingly used in computational neuroscience. An additional chapter on synchronization, with more advanced material, can be found at the author's website, www.izhikevich.com.