Principles of Computational Modelling in Neuroscience

by David Sterratt, Bruce Graham, Andrew Gillies

The nervous system is made up of a large number of interacting elements. To understand how such a complex system functions requires the construction and analysis of computational models at many different levels. This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signalling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modelling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience.

Genres: Neuroscience, Science, Nonfiction

404 pages, Kindle Edition

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.