Theoretical Neuroscience: Computational And Mathematical Modeling of Neural Systems

by Peter Dayan, Laurence F. Abbott

Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.

The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.

Genres: Neuroscience, Science, Textbooks, Nonfiction, Biology, Psychology, Mathematics

480 pages, Paperback

First published September 1, 2001

Reviews

[Ilknur · Rating 4 out of 5]

This is the textbook for coursera's computational neuroscience online course, which is by the way, a great course

[Analkumar Kumar · Rating 4 out of 5]

For physicists and mathematicians trying to switch to this subject. Would not recommend this to biologists(with little or no training in math) as the chapters use a sufficient amount of mathematical concepts. It covers a broad range of topics and is an excellent introduction to the subject.

[Will Dorrell · Rating 1 out of 5]

This is the classic textbook in theoretical neuroscience: i.e. for people into using mathematical ideas to understand neural systems.

I do not get why this book is considered the classic. I've been to two semester long theoretical neuroscience courses now at different universities, both of them were much more interesting than this book and had very little overlap either with the content of the book or with each other.

I admit, I did not read all of it, the bits I did read were while revising for an exam.

But even so, it sometimes feels like they're just looking for any place they can shove a few equations into the way we understand the brain. It seems like they're are much deeper and nicer places were maths has played a role in understanding the brain than the ones covered here.

On the plus side, it is the only book to collect this set of information in a relatively palatable form. So, while there are no others, it might well be the best way to learn this material if courses are not an option.

[DJ]

THE intro to computational neuroscience

[Nick Black]

surprisingly cheap! hurrah for the mit press. i think i must acquire it.
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Won't be able to get to this before 2010, almost certainly. ARGH so much to learn!

[Jona]

Only read a few chapters and skimmed through the rest to prepare for my thesis. It helped to go over some of the anatomical and physiological basics of neural information processing again, having been introduced to them in high school. Of course, this book leaves these fundamentals quickly behind and dives heads-first into the mathematical modeling of neural systems ranging from single neurons to networks thereof. The math used is not necessarily out-of-this-world complex in itself and the authors attempt to provide structure to the individual chapters with short introductions and conclusions as well as an extra column to highlight new concepts that are presented. However, despite these efforts I found the sections that I read to be not particularly engaging and on multiple instances far too lengthy in terms of mathematical derivations, especially compared to the neuroscientific/biological motivation and interpretation that is provided at the same time. Given this, I would not recommend this as a stand-alone read, but can see the appeal of using it as a central reference in a computational neuroscience class, primarily due to the sheer breadth of topics covered and the detailed mathematical treatment which is great when looking up things.

[Scott Holstad · Rating 5 out of 5]

Just one word: WOW!!!

[Kjn · Rating 4 out of 5]

A nice introduction. Quite a bit of math and maybe a bit dated, but overall very readable.