Principles of Computational Modelling in Neuroscience

by David Sterratt, Bruce Graham, Andrew Gillies

Taking a step-by-step approach to modelling neurons and neural circuitry, this textbook teaches students how to use computational techniques to understand the nervous system at all levels, using case studies throughout to illustrate fundamental principles. Starting with a simple model of a neuron, the authors gradually introduce neuronal morphology, synapses, ion channels and intracellular signalling. This fully updated new edition contains additional examples and case studies on specific modelling techniques, suggestions on different ways to use this book, and new chapters covering plasticity, modelling extracellular influences on brain circuits, modelling experimental measurement processes, and choosing appropriate model structures and their parameters. The online resources offer exercises and simulation code that recreate many of the book's figures, allowing students to practice as they learn. Requiring an elementary background in neuroscience and high-school mathematics, this is an ideal resource for a course on computational neuroscience.

Genres: Neuroscience, Science, Nonfiction

544 pages, Hardcover

First published March 1, 2008

Reviews

[Yates Buckley · Rating 5 out of 5]

This is a university course textbook in computational neuroscience, it is not accessible to a broader readership but it is the most accessible and integrated presentation of the field of Computational Neuroscience I have come across so far. If you want an idea of what the field is really like, and what the key problems are, this text is perfect.
My only caveat to this book is that today Computational Neuroscience is fragmenting a bit following many other techniques. This book captures the core areas in the field but does not quite clarify the job of a Computational Neuroscientist today, which might involve anything from using machine learning to help with experiments, statistical modelling, evaluating abstract network theory graphs etc...
In any case, the book is a great resource: if you had to read one technical book to learn about Computational Neuroscience, this would be it. Preferred over more classic texts such as Abbott Dayan's book that is much more advanced and difficult to dip into.