Principles of Computational Modelling in Neuroscience

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 signaling. 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 modeling 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.

David Sterratt is a Research Fellow in the School of Informatics at the University of Edinburgh. His computational neuroscience research interests include models of learning and forgetting, and the formation of connections within the developing nervous system.

Bruce Graham is a Reader in Computing Science in the Department of Computing Science and Mathematics at the University of Stirling. Focusing on computational neuroscience, his research covers nervous system modelling at many levels.

Andrew Gillies works at Psymetrix Limited, Edinburgh. He has been actively involved in computational neuroscience research.

David Willshaw is Professor of Computational Neurobiology in the School of Informatics at the University of Edinburgh. His research focuses on the application of methods of computational neurobiology to an understanding of the development and functioning of the nervous system.