Methods in Neuronal Modeling - 2nd Edition: From Ions to Networks (Computational Neuroscience) [Hardcover]

Idan Segev (Editor)

Book Description

ISBN-10: 0262112310 | ISBN-13: 978-0262112314 | Publication Date: June 4, 1998 | Edition: second edition

Much research focuses on the question of how information is processed in nervous systems, from the level of individual ionic channels to large-scale neuronal networks, and from "simple" animals such as sea slugs and flies to cats and primates. New interdisciplinary methodologies combine a bottom-up experimental methodology with the more top-down-driven computational and modeling approach. This book serves as a handbook of computational methods and techniques for modeling the functional properties of single and groups of nerve cells.The contributors highlight several key trends: (1) the tightening link between analytical/numerical models and the associated experimental data, (2) the broadening of modeling methods, at both the subcellular level and the level of large neuronal networks that incorporate real biophysical properties of neurons as well as the statistical properties of spike trains, and (3) the organization of the data gained by physical emulation of the nervous system components through the use of very large scale circuit integration (VLSI) technology.The field of neuroscience has grown dramatically since the first edition of this book was published nine years ago. Half of the chapters of the second edition are completely new; the remaining ones have all been thoroughly revised. Many chapters provide an opportunity for interactive tutorials and simulation programs. They can be accessed via Christof Koch's Website.Contributors : Larry F. Abbott, Paul R. Adams, Hagai Agmon-Snir, James M. Bower, Robert E. Burke, Erik de Schutter, Alain Destexhe, Rodney Douglas, Bard Ermentrout, Fabrizio Gabbiani, David Hansel, Michael Hines, Christof Koch, Misha Mahowald, Zachary F. Mainen, Eve Marder, Michael V. Mascagni, Alexander D. Protopapas, Wilfrid Rall, John Rinzel, Idan Segev, Terrence J. Sejnowski, Shihab Shamma, Arthur S. Sherman, Paul Smolen, Haim Sompolinsky, Michael Vanier, Walter M. Yamada.

Editorial Reviews

Review

"An admirable concentration of expertise on state-of-the-art neuronalmodeling techniques—a must on the bookshelf of computationalneuroscientists and theoretical neurobiologists." Wolf Singer , M.D., Ph.D., Max-Planck-Institute for Brain Research

About the Author

Christof Koch is Professor of Computation and Neural Systems at the California Institute of Technology and the author of Biophysics of Computation.

Product Details

• Reading level: Ages 18 and up
  
• Hardcover: 844 pages
• Publisher: A Bradford Book; second edition edition (June 4, 1998)
• Language: English
• ISBN-10: 0262112310
• ISBN-13: 978-0262112314
• Product Dimensions: 9.2 x 7.2 x 1.6 inches
• Shipping Weight: 3 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #1,767,997 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

8 of 10 people found the following review helpful:

5.0 out of 5 stars Computational Modeling, June 10, 2004

By 

Joseph J Grenier "jjg MD PhD" (Aurora, IL United States) - See all my reviews
(REAL NAME)   

This review is from: Methods in Neuronal Modeling - 2nd Edition: From Ions to Networks (Computational Neuroscience) (Hardcover)

Great book for the theorist and experimentalist! I used the section on Epilepsy and the Neural Code for a grant I wrote. This book is a great reference and time spent reading it is very well rewarded. I bought the 1st & 2nd editions which are very different. Both editions are worth buying if one is involved with computer modeling, computation, mathematics, and plain old fashion recording neurophysiology.

11 of 39 people found the following review helpful:

5.0 out of 5 stars good backround, May 23, 2000

By 

"pyramidl" - See all my reviews

This review is from: Methods in Neuronal Modeling - 2nd Edition: From Ions to Networks (Computational Neuroscience) (Hardcover)

How do you write a review by multiple authors who are authorities in the respective fields of each chapter? Carefully. Does the review need to contain the usual metaphors which when finally finished leave a prospective researcher with feelings of Oprah's book store. No. So let's start. The book contains general overall experimental setups on the "dry" sort, giving the broad over all design or outline of the direction of the model (read calculations) then includes the formulas at the end each chapter. Extremely nice for incorporation into MathCad or Matlab. (this is not as simple as it sounds, moving a high verbage book into something meaningful on a computer can't always be with as much style and grace as one wants). Most of the models however are, for want of a better discripter, "realistic".I guess this means they are not movies. Whether a model is done using ionic balanced equations or transfer functions seems somewhat academic to me as long as the information gained is useful. The level at which one can "view" these models certainly depends on the math and assets one can throw at the problem. As an example without to much "sweat" most of the cable equations, represented either as diffusion or electrical can be simulated far better in Matlabs PDE toolbox using Finite Element Methods. This allows the model to be viewed from the same point of view at different aspects. And why some kind of transfer functions cannot be made, and used in classical control theory, I'm working on now. Most of the equations using standard algebraic formulas work better in Mathcad using iterative range variables in extinction type modeling. This allows for the injection of pharmalogical testing data based on frequency manipulation, along digital signal processing lines. And running out of room, why not just take the data, use it to train a neural net and then use a Segueno fuzzy inference system to solve for the equations of state, which works very well for large systems (heart)such as the McKay-Glass non-linear types?