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Principles of Computational Modelling in Neuroscience

2023-10-05 Science
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Release : 2023-10-05
Genre : Science
Kind : eBook
Book Rating : 143/5 ( reviews)

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Book Synopsis Principles of Computational Modelling in Neuroscience by : David Sterratt

Download or read book Principles of Computational Modelling in Neuroscience written by David Sterratt. This book was released on 2023-10-05. Available in PDF, EPUB and Kindle. Book excerpt: Learn to use computational modelling techniques to understand the nervous system at all levels, from ion channels to networks.

Principles of Computational Modelling in Neuroscience

2011-06-30 Medical
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Release : 2011-06-30
Genre : Medical
Kind : eBook
Book Rating : 791/5 ( reviews)

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Book Synopsis Principles of Computational Modelling in Neuroscience by : David Sterratt

Download or read book Principles of Computational Modelling in Neuroscience written by David Sterratt. This book was released on 2011-06-30. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Principles of Computational Modelling in Neuroscience

2011-06-30 Medical
Download Principles of Computational Modelling in Neuroscience PDF Online Free

Author :
Release : 2011-06-30
Genre : Medical
Kind : eBook
Book Rating : 954/5 ( reviews)

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Book Synopsis Principles of Computational Modelling in Neuroscience by : David Sterratt

Download or read book Principles of Computational Modelling in Neuroscience written by David Sterratt. This book was released on 2011-06-30. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Principles of Computational Modelling in Neuroscience

2011 Computational neuroscience
Download Principles of Computational Modelling in Neuroscience PDF Online Free

Author :
Release : 2011
Genre : Computational neuroscience
Kind : eBook
Book Rating : 782/5 ( reviews)

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Book Synopsis Principles of Computational Modelling in Neuroscience by :

Download or read book Principles of Computational Modelling in Neuroscience written by . This book was released on 2011. Available in PDF, EPUB and Kindle. Book excerpt: "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"--

Computational Modeling Methods for Neuroscientists

2009-09-04 Medical
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Release : 2009-09-04
Genre : Medical
Kind : eBook
Book Rating : 274/5 ( reviews)

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Book Synopsis Computational Modeling Methods for Neuroscientists by : Erik De Schutter

Download or read book Computational Modeling Methods for Neuroscientists written by Erik De Schutter. This book was released on 2009-09-04. Available in PDF, EPUB and Kindle. Book excerpt: A guide to computational modeling methods in neuroscience, covering a range of modeling scales from molecular reactions to large neural networks. This book offers an introduction to current methods in computational modeling in neuroscience. The book describes realistic modeling methods at levels of complexity ranging from molecular interactions to large neural networks. A “how to” book rather than an analytical account, it focuses on the presentation of methodological approaches, including the selection of the appropriate method and its potential pitfalls. It is intended for experimental neuroscientists and graduate students who have little formal training in mathematical methods, but it will also be useful for scientists with theoretical backgrounds who want to start using data-driven modeling methods. The mathematics needed are kept to an introductory level; the first chapter explains the mathematical methods the reader needs to master to understand the rest of the book. The chapters are written by scientists who have successfully integrated data-driven modeling with experimental work, so all of the material is accessible to experimentalists. The chapters offer comprehensive coverage with little overlap and extensive cross-references, moving from basic building blocks to more complex applications. Contributors Pablo Achard, Haroon Anwar, Upinder S. Bhalla, Michiel Berends, Nicolas Brunel, Ronald L. Calabrese, Brenda Claiborne, Hugo Cornelis, Erik De Schutter, Alain Destexhe, Bard Ermentrout, Kristen Harris, Sean Hill, John R. Huguenard, William R. Holmes, Gwen Jacobs, Gwendal LeMasson, Henry Markram, Reinoud Maex, Astrid A. Prinz, Imad Riachi, John Rinzel, Arnd Roth, Felix Schürmann, Werner Van Geit, Mark C. W. van Rossum, Stefan Wils

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