Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems

Eliasmith, Chris; Anderson, C. H.

ISBN 10: 0262050714 / ISBN 13: 9780262050715

Published by Bradford Books, 2002

About this Item

The MIT Press (A Bradford Book); Cambridge, 2003. Hardcover. A Near Fine, binding sturdy and intact, trace handling/scuffing, mild shelf wear, slightly cocked, without Dust wrapper. A nice, clean and unmarked copy. 8vo[octavo or approx. 6 x 9 inches]. 356pp., references, indexed. We pack securely and ship daily with delivery confirmation on every book. The picture on the listing page is of the actual book for sale. Additional Scan(s) are available for any item, please inquire.Please note: Oversized books/sets MAY require additional postage then what is quoted for 2.2lb book.

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Bibliographic Details

• Title: Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems
• Publisher: Bradford Books
• Publication Date: 2002
• Language: English
• ISBN 10: 0262050714
• ISBN 13: 9780262050715
• Binding: hardcover
• Condition: As New
• Weight: 27 ounces
• Seller catalogs:
  • Math Text Books

About this title

Synopsis

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.

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.