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Tutorial on Neural Systems Modeling Hardcover – 1 Dec 2009


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About the Author

THOMAS J. ANASTASI is Associate Professor at the University of Illinois at Urbana--Champaign, USA, affiliated with the Department of Molecular and Integrative Physiology and the Beckman Institute for Advanced Science and Technology. He earned a BSc. in Psychology at McGill University, and a Ph.D. in Physiology and Biophysics from the University of Texas at Galveston. A teacher of courses in computational neuroscience for nearly two decades, Dr. Anastasio has received the James E. Heath Award for Excellence in Teaching Physiology at the University of Illinois. His research focuses on the computational modeling of the nervous system in health and disease.

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Amazon.com: 5 reviews
10 of 10 people found the following review helpful
great introductory tutorial in computational neuroscience. 14 May 2010
By M. Kouh - Published on Amazon.com
Format: Hardcover
We used this book for the upper-level undergraduate students in an interdisciplinary computational neuroscience course at a small liberal arts college. This is a fine, well-written book. One of the strengths of this book is that it starts with very basic programming in Matlab, so that the students without programming backgrounds can easily follow along. The programs in the book are well commented, and they progress slowly and logically in complexity. More advanced math topics are well separated out in Math Boxes. The examples of the neural systems are covered in enough (but not too much) details to be interesting and accessible to the readers. The book is definitely considerate of and sensitive to the wonderfully interdisciplinary nature of this field, so that the materials can be digested by people with different backgrounds. One can not contain all the topics in computational neuroscience in a single book, but this book does a great job of covering many important and interesting ideas/areas (Hebbian learning, Hopfield model, lateral inhibition, adaptation, supervised and unsupervised learning, etc.).

It works very well as an introductory textbook (or tutorial) of the field. The codes and the discussions are clear and simple (not intended as an advanced textbook), and to me, that's the strength and unique quality of this book.

By the way, most of the computer programs listed in the book work well with Octave (as well as Matlab).
2 of 2 people found the following review helpful
great book for beginner 19 April 2011
By K Tian - Published on Amazon.com
Format: Hardcover Verified Purchase
I studied this book with Dr. Anastasio himself last semester and learned a lot! This is a detailed and well-organized book that fits perfectly for beginners. It concentrates both on concepts and practices. Don't miss it if you're interested in computational neurosci!
Computational Neurobio book 10 May 2013
By Grazyna Zmyslowski - Published on Amazon.com
Format: Hardcover Verified Purchase
It explains concepts in fairly good depth for an introductory book, but the exercises from the first part seem a little slow. Quite literally a tutorial in the most basic sense.
2 of 7 people found the following review helpful
Depends on the Student having access to Matlab 9 May 2013
By Graeme E. Smith - Published on Amazon.com
Format: Hardcover Verified Purchase
I haven't been able to really evaluate this book yet because I am still trying to figure out how to afford Matlab. I thought I had it figured out or I wouldn't have bought the book, but I had inadvertently bought Mathematica instead. Licensing for Matlab is much less liberal than Mathematica, their requirement that you be registered as a student for the evaluation copy is difficult for people looking to take coursera courseware.
1 of 6 people found the following review helpful
tutorial 19 April 2013
By Ernest Bylander - Published on Amazon.com
Format: Hardcover Verified Purchase
Probably as difficult to follow as Matlab itself. After finally getting a Matlab lease, and trying to learn enough of it to follow the story in Chapter 2, I am now buying two more "how to do Matlab" which has a steeper learning curve than Mathematica.
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