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Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Computational Neuroscience) 1st Edition by Dayan, Peter, Abbott, Laurence F. (2005) Paperback Paperback – 1707
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Top customer reviews
Coming to the specific merits of the book, what stands out is the quality of the prose and explanations. The book is tightly written, so, it gets to the point fast and explains what it needs to without much ado. Because this book is quite succinct (and does not "over explain") you might need do multiple readings of the chapters to understand the content. Actually, it was only on the repeated readings that I came to appreciate the overall coherence of this book.
In this book you will find that complex math and derivations are often either relegated to the chapter appendix or left to the reader to cover independently. This approach actually makes the book less daunting because you don't need to wade through dozens of pages of topics that are not really computational neuroscience but Math!
Lest someone get the impression that the book is too mathematical I want to point out that you need to have a standard science/engineering background in Calculus and differential equations and basic knowledge of Physics/Chemistry and you should be fine. I personally only had a few problems in the area of dynamical systems which make their appearance in a few places in the book.
On the negative side (though this book definitely deserves its 5 stars), I feel the book lacks a little sparkle and personality and can be a bit dry in places. Luckily there are a lot interesting MOOCs and videos on the Internet on Neuroscience that will provide the necessary background "excitement" and context you need while reading this book.
Another (subjective) thing: I love calculus but I think its slightly overdone here. If you're _really_ doing computational neuroscience, you're probably going to use a lot of summation, simulation, discrete math, data analysis and algorithms but this book loves showing things in terms of Calculus. Yeah, its prettier with integrals but you're going to have to translate that into algorithms eventually. So, ironically, this book on Computational Neuroscience needs to be a bit more "computational."
Finally, if you have some prior knowledge of Machine Learning you are likely to enjoy this book more. This was an unexpected bonus as I didn't realize that was so much overlap between Machine Learning and (real) Neural Systems before diving into the subject.
I know, I know, many people go in to medicine in order to avoid math. I think that is to the eternal shame of the modern practitioner, but just know that computational neuroscience is not for you.
I don't give many reviews five stars, or even one star. Those stars are too many standard deviations from the norm for most work. This is a good book, better than merely competent. With my math background, I am finding it very useful and understandable read.
Most recent customer reviews
1) Given the expectations of some buyers of this book, this book may be usefully thought of as a math textbook.Read more