Ebook | $35.00 Short | ISBN: 9780262259507 | 432 pp. | 7 x 9 in | 85 b&w illus., 3 tables| September 2009
This book offers an introduction to current methods in computational modeling in neuroscience. The book describes realistic modeling methods at levels of complexity ranging from molecular interactions to large neural networks. A “how to” book rather than an analytical account, it focuses on the presentation of methodological approaches, including the selection of the appropriate method and its potential pitfalls. It is intended for experimental neuroscientists and graduate students who have little formal training in mathematical methods, but it will also be useful for scientists with theoretical backgrounds who want to start using data-driven modeling methods. The mathematics needed are kept to an introductory level; the first chapter explains the mathematical methods the reader needs to master to understand the rest of the book. The chapters are written by scientists who have successfully integrated data-driven modeling with experimental work, so all of the material is accessible to experimentalists. The chapters offer comprehensive coverage with little overlap and extensive cross-references, moving from basic building blocks to more complex applications.ContributorsPablo Achard, Haroon Anwar, Upinder S. Bhalla, Michiel Berends, Nicolas Brunel, Ronald L. Calabrese, Brenda Claiborne, Hugo Cornelis, Erik De Schutter, Alain Destexhe, Bard Ermentrout, Kristen Harris, Sean Hill, John R. Huguenard, William R. Holmes, Gwen Jacobs, Gwendal LeMasson, Henry Markram, Reinoud Maex, Astrid A. Prinz, Imad Riachi, John Rinzel, Arnd Roth, Felix Schürmann, Werner Van Geit, Mark C. W. van Rossum, Stefan Wils
About the Editor
Erik De Schutter is Principal Investigator and Head of the Computational Neuroscience Unit at the Okinawa Institute of Science and Technology, Japan, and Head of the Theoretical Neurobiology Laboratory in the Department of Biomedical Sciences at the University of Antwerp, Belgium.
“[S]uccessfully integrated data-driven modeling with experimental work…all of the material is accessible to experimentalists.” —Mathematical Reviews"—
"Neuroscientists with a computational background will benefit most from this book, and will find it a comprehensive source of information on how to build and critically assess neuron models at many levels of description." — Giancarlo La Camera, The Quarterly Review of Biology"—