- Hardcover: 498 pages
- Publisher: Academic Press; 1 edition (August 9, 2010)
- Language: English
- ISBN-10: 0123748828
- ISBN-13: 978-0123748829
- Product Dimensions: 10.9 x 8.7 x 1.3 inches
- Shipping Weight: 3.2 pounds
- Average Customer Review: 4 customer reviews
- Amazon Best Sellers Rank: #1,279,980 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Mathematics for Neuroscientists 1st Edition
Use the Amazon App to scan ISBNs and compare prices.
Customers who bought this item also bought
What other items do customers buy after viewing this item?
"Mathematics for Neuroscientists by Fabrizio Gabbiani and Steven Cox (GC) was developed over 8 years of teaching courses on the topic. This experience, as well as the wide-ranging research contributions of the authors, clearly shines through―the text is a landmark for the field in its scope, rigor, and accessibility. . . .This is a hallmark of the book: elegance, completeness, and economy that leave the reader with much more mathematics and science than one might expect even in a work of this size. The book further benefits from the availability of MATLAB code provided to regenerate almost every figure. . . . This integration of code and text is by far the best we’ve seen. It brings alive the science, the mathematical tools, the models, and their implementation."―Society for Industrial and Applied Mathematics SIAM Review, 2011 (Vol 53, No. 3)
About the Author
Dr. Gabbiani is Professor in the Department of Neuroscience at the Baylor College of Medicine. Having received the prestigious Alexander von Humboldt Foundation research prize in 2012, he just completed a one-year cross appointment at the Max Planck Institute of Neurobiology in Martinsried and has international experience in the computational neuroscience field. Together with Dr. Cox, Dr. Gabbiani co-authored the first edition of this bestselling book in 2010.
Dr. Cox is Professor of Computational and Applied Mathematics at Rice University. Affiliated with the Center for Neuroscience, Cognitive Sciences Program, and the Ken Kennedy Institute for Information Technology, he is also Adjunct Professor of Neuroscience at the Baylor College of Medicine. In addition, Dr. Cox has served as Associate Editor for a number of mathematics journals, including the Mathematical Medicine and Biology and Inverse Problems. He previously authored the first edition of this title with Dr. Gabbiani.
Top customer reviews
My advisor warned me when we started 3 years ago --- there was no "easy way" to introduce someone from my background (Mathematics) to neuroscience. I had no concept of the mathematical "standards" (and I use that word very loosely for this field), much less the biology. I had some knowledge of modeling and programming, and that was it. I was, as he described it, to be "thrown in the deep end."
Drs. Cox and Gabbiani have gone to great lengths to provide the "shallow end" of neuroscience. I believe whether you will be in the experimental or computational side of the field, you'll want a basic grasp of both, and this book supplies exactly that. Along the way, historical context is provided and comprehensive (and numerous) exercises will test your knowledge. If this is a field that interests you, I believe there is no better place to start.
On one hand, it is one of the few sources that cover all of the math you will need to do research in computational neuroscience. (In fact, it pretty much covers all the math anyone would ever need to do research in computational neuroscience - you don't necessarily need to go this deep into mathematical methods to do comp. neurosci. research.) On the other hand, I feel that the title is somewhat misleading - a better title might be "Neuroscience for Mathematicians and Physicists". I suspect that it would be hard to actually learn the mathematics covered in this book from this book. Rather, this seems more appropriate for researchers who already have a solid understanding of the relevant mathematical methods and who want to learn how to apply them to problems in neuroscience. That isn't a bad thing, (indeed, this probably has the most encyclopedic coverage of mathematical methods for neuroscience), but I doubt I would recommend this to graduate students coming into computational neuroscience from the neuroscience/biology side of the field.
Readers who would prefer an approach that assumes an understanding of the biological systems, rather than an understanding of the math, would appreciate Sterratt et al.'s "Principles of Computational Modelling in Neuroscience" and Izhikevich's "Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting". The latter, in particular, does a superb job of developing readers' understanding of the math without assuming a particularly strong math background.