Principles of Computational Modelling in Neuroscience / [electronic resource]
by Sterratt, David [author.]; Graham, Bruce [author.]; Gillies, Andrew [author.]; Willshaw, David [author.].
Material type:
BookPublisher: Cambridge : Cambridge University Press, 2011.Description: 1 online resource (401 pages) : digital, PDF file(s).ISBN: 9780511975899 (ebook).Subject(s): Computational neuroscience | Models, Neurological | Computer Simulation | Neural Conduction | Synaptic TransmissionOnline resources: Cambridge Books Online Summary: 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.
Title from publisher's bibliographic system (viewed on 09 Oct 2015).
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.
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