Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems (Computational Neuroscience Series)

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9780262550604: Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems (Computational Neuroscience Series)

For years, researchers have used the theoretical tools of engineering to understand neural systems, but much of this work has been conducted in relative isolation. In Neural Engineering, Chris Eliasmith and Charles Anderson provide a synthesis of the disparate approaches current in computational neuroscience, incorporating ideas from neural coding, neural computation, physiology, communications theory, control theory, dynamics, and probability theory. This synthesis, they argue, enables novel theoretical and practical insights into the functioning of neural systems. Such insights are pertinent to experimental and computational neuroscientists and to engineers, physicists, and computer scientists interested in how their quantitative tools relate to the brain.

The authors present three principles of neural engineering based on the representation of signals by neural ensembles, transformations of these representations through neuronal coupling weights, and the integration of control theory and neural dynamics. Through detailed examples and in-depth discussion, they make the case that these guiding principles constitute a useful theory for generating large-scale models of neurobiological function. A software package written in MatLab for use with their methodology, as well as examples, course notes, exercises, documentation, and other material, are available on the Web.

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About the Author:

Chris Eliasmith is Assistant Professor in the Department of Philosophy and the Department of Systems Design Engineering at the University of Waterloo.

Review:

From principle component analysis to Kalman filters, information theory to attractor dynamics, this book is a brilliant introduction to the mathematical and engineering methods used to analyze neural function.

(Leif Finkel, Professor, Neuroengineering Research Laboratories, University of Pennsylvania)

In this brilliant volume, Eliasmith and Anderson present a novel theoretical framework for understanding the functional organization and operation of nervous systems, from the cellular level to the level of large-scale networks.

(John P. Miller, Center for Computational Biology, University of Montana)

This book represents a significant advance in computational neuroscience. Eliasmith and Anderson have developed an elegant framework for understanding representation, computation, and dynamics in neurobiological systems. The book is beautifully written and it should be accessible to a wide variety of readers.

(Bruno A. Olshausen, Center for Neuroscience, University of California, Davis)

"Sobre este título" puede pertenecer a otra edición de este libro.

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Descripción MIT Press Ltd, United States, 2004. Paperback. Estado de conservación: New. Language: English . Brand New Book. For years, researchers have used the theoretical tools of engineering to understand neural systems, but much of this work has been conducted in relative isolation. In Neural Engineering, Chris Eliasmith and Charles Anderson provide a synthesis of the disparate approaches current in computational neuroscience, incorporating ideas from neural coding, neural computation, physiology, communications theory, control theory, dynamics, and probability theory. This synthesis, they argue, enables novel theoretical and practical insights into the functioning of neural systems. Such insights are pertinent to experimental and computational neuroscientists and to engineers, physicists, and computer scientists interested in how their quantitative tools relate to the brain.The authors present three principles of neural engineering based on the representation of signals by neural ensembles, transformations of these representations through neuronal coupling weights, and the integration of control theory and neural dynamics. Through detailed examples and in-depth discussion, they make the case that these guiding principles constitute a useful theory for generating large-scale models of neurobiological function. A software package written in MatLab for use with their methodology, as well as examples, course notes, exercises, documentation, and other material, are available on the Web. Nº de ref. de la librería AAC9780262550604

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Descripción MIT Press Ltd, United States, 2004. Paperback. Estado de conservación: New. Language: English . Brand New Book. For years, researchers have used the theoretical tools of engineering to understand neural systems, but much of this work has been conducted in relative isolation. In Neural Engineering, Chris Eliasmith and Charles Anderson provide a synthesis of the disparate approaches current in computational neuroscience, incorporating ideas from neural coding, neural computation, physiology, communications theory, control theory, dynamics, and probability theory. This synthesis, they argue, enables novel theoretical and practical insights into the functioning of neural systems. Such insights are pertinent to experimental and computational neuroscientists and to engineers, physicists, and computer scientists interested in how their quantitative tools relate to the brain.The authors present three principles of neural engineering based on the representation of signals by neural ensembles, transformations of these representations through neuronal coupling weights, and the integration of control theory and neural dynamics. Through detailed examples and in-depth discussion, they make the case that these guiding principles constitute a useful theory for generating large-scale models of neurobiological function. A software package written in MatLab for use with their methodology, as well as examples, course notes, exercises, documentation, and other material, are available on the Web. Nº de ref. de la librería AAC9780262550604

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Descripción MIT Press Ltd, United States, 2004. Paperback. Estado de conservación: New. Language: English . This book usually ship within 10-15 business days and we will endeavor to dispatch orders quicker than this where possible. Brand New Book. For years, researchers have used the theoretical tools of engineering to understand neural systems, but much of this work has been conducted in relative isolation. In Neural Engineering, Chris Eliasmith and Charles Anderson provide a synthesis of the disparate approaches current in computational neuroscience, incorporating ideas from neural coding, neural computation, physiology, communications theory, control theory, dynamics, and probability theory. This synthesis, they argue, enables novel theoretical and practical insights into the functioning of neural systems. Such insights are pertinent to experimental and computational neuroscientists and to engineers, physicists, and computer scientists interested in how their quantitative tools relate to the brain.The authors present three principles of neural engineering based on the representation of signals by neural ensembles, transformations of these representations through neuronal coupling weights, and the integration of control theory and neural dynamics. Through detailed examples and in-depth discussion, they make the case that these guiding principles constitute a useful theory for generating large-scale models of neurobiological function. A software package written in MatLab for use with their methodology, as well as examples, course notes, exercises, documentation, and other material, are available on the Web. Nº de ref. de la librería BZV9780262550604

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Descripción Paperback. Estado de conservación: New. Paperback. For years, researchers have used the theoretical tools of engineering to understand neural systems, but much of the work has been conducted in relative isolation. In Neural Engineering, Ch.Shipping may be from multiple locations in the US or from the UK, depending on stock availability. 356 pages. 0.553. Nº de ref. de la librería 9780262550604

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Descripción MIT Press Ltd. Paperback. Estado de conservación: new. BRAND NEW, Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems, Chris Eliasmith, Charles H. Anderson, For years, researchers have used the theoretical tools of engineering to understand neural systems, but much of this work has been conducted in relative isolation. In Neural Engineering, Chris Eliasmith and Charles Anderson provide a synthesis of the disparate approaches current in computational neuroscience, incorporating ideas from neural coding, neural computation, physiology, communications theory, control theory, dynamics, and probability theory. This synthesis, they argue, enables novel theoretical and practical insights into the functioning of neural systems. Such insights are pertinent to experimental and computational neuroscientists and to engineers, physicists, and computer scientists interested in how their quantitative tools relate to the brain. The authors present three principles of neural engineering based on the representation of signals by neural ensembles, transformations of these representations through neuronal coupling weights, and the integration of control theory and neural dynamics. Through detailed examples and in-depth discussion, they make the case that these guiding principles constitute a useful theory for generating large-scale models of neurobiological function. A software package written in MatLab for use with their methodology, as well as examples, course notes, exercises, documentation, and other material, are available on the Web. Nº de ref. de la librería B9780262550604

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