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).