Principles of Computational Modelling in Neuroscience

Principles of Computational Modelling in Neuroscience

3.75 (8 ratings by Goodreads)
By (author)  , By (author)  , By (author)  , By (author) 

List price: US$52.00

Currently unavailable

We can notify you when this item is back in stock

Add to wishlist

AbeBooks may have this title (opens in new window).

Try AbeBooks

Description

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.
show more

Product details

  • Electronic book text | 408 pages
  • CAMBRIDGE UNIVERSITY PRESS
  • Cambridge University Press (Virtual Publishing)
  • Cambridge, United Kingdom
  • English
  • 178 b/w illus. 7 tables
  • 1139037099
  • 9781139037099

Review quote

'Here at last is a book that is aware of my problem, as an experimental neuroscientist, in understanding the maths ... I expect it to be as mind expanding as my involvement with its authors was over the years. I only wish I had had the whole book sooner - then my students and post-docs would have been able to understand what I was trying to say and been able to derive the critical tests of the ideas that only the rigor of the mathematical formulation of them could have generated.' Gordon W. Arbuthnott, Okinawa Institute of Science and Technology 'This is a wonderful, clear and compelling text on mathematically-minded computational modelling in neuroscience. It is beautifully aimed at those engaged in capturing quantitatively, and thus simulating, complex neural phenomena at multiple spatial and temporal scales, from intracellular calcium dynamics and stochastic ion channels, through compartmental modelling, all the way to aspects of development. It takes particular care to define the processes, potential outputs and even some pitfalls of modelling; and can be recommended for containing the key lessons and pointers for people seeking to build their own computational models. By eschewing issues of coding and information processing, it largely hews to concrete biological data, and it nicely avoids sacrificing depth for breadth. It is very suitably pitched as a Master's level text, and its two appendices, on mathematical methods and software resources, will rapidly become dog-eared.' Peter Dayan, University College London 'Principles of Computational Modelling in Neuroscience sets a new standard of clarity and insight in explaining biophysical models of neurons. This provides a firm foundation for network models of brain function and brain development. I plan to use this textbook in my course on computational neurobiology.' Terrence Sejnowski, Salk Institute for Biological Studies and University of California, San Diego
show more

Table of contents

Preface; 1. Introduction; 2. The basis of electrical activity in the neuron; 3. The Hodgkin Huxley model of the action potential; 4. Compartmental models; 5. Models of active ion channels; 6. Intracellular mechanisms; 7. The synapse; 8. Simplified models of neurons; 9. Networks; 10. The development of the nervous system; Appendix A. Resources; Appendix B. Mathematical methods; References.
show more

About David Willshaw

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.
show more

Rating details

8 ratings
3.75 out of 5 stars
5 50% (4)
4 12% (1)
3 12% (1)
2 12% (1)
1 12% (1)
Book ratings by Goodreads
Goodreads is the world's largest site for readers with over 50 million reviews. We're featuring millions of their reader ratings on our book pages to help you find your new favourite book. Close X