Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Computational Neuroscience Series)

An indispensable resource for anyone delving into the realm of theoretical neuroscience. This book provides a solid foundation in computational and mathematical modeling of neural systems, making it an invaluable tool for both beginners and experts. The clear explanations and insightful examples make complex concepts accessible. A must-have for anyone passionate about understanding the brain's inner workings.

Great textbook for the field - but you better brush up on your maths first!

This book was an eye opener for me. Scientists still fully don't understand how neurons "think" and "learn" but I was shocked to learn how much we _do know_. After reading significant chunks for this book I feel inspired and want to recommend this book to others who have an interest in this subject. This book is a great overview of the field of Computational Neuroscience. The authors convincingly explain the most fundamental theoretical concepts in Computation Neuroscience and back them up by describing some of the major experiments in this field. It does not oversimplify nor does it over-complicate, for a first introduction.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.

神経科学に対する数理的なアプローチを幅広くカバーしていて、とても良い。8章以降の機械学習に関する部分は分量の関係もあり、他の本を参照した方が良いけれど、7章までに関しては self-contained で完璧。Appendixの説明も丁寧。

Excellent product. Good state, I recommend it