Catalogue


Computational modeling methods for neuroscientists /
edited by Erik De Schutter.
imprint
Cambridge, Mass. : MIT Press, c2010.
description
xii, 419 p.
ISBN
0262013274 (hardcover : alk. paper), 9780262013277 (hardcover : alk. paper)
format(s)
Book
Holdings
More Details
added author
imprint
Cambridge, Mass. : MIT Press, c2010.
isbn
0262013274 (hardcover : alk. paper)
9780262013277 (hardcover : alk. paper)
contents note
Introduction to equation solving / Bard Ermentrout and John Rinzel -- Parameter searching / Pablo Achard, Werner Van Geit and Gwendal LeMasson -- Reaction-diffusion modeling / Upinder S. Bhalla and Stefan Wils -- Modeling intracellular calcium dynamics / Erik De Schutter -- Modeling voltage-dependent channels / Alain Destexhe and John R. Huguenard -- Modeling synapses / Arnd Roth and Mark C.W. van Rossum -- Modeling point neurons : from Hodgkin-Huxley to integrate-and-fire / Nicolas Brunel -- Reconstruction of neuronal morphology / Gwen Jacobs, Brenda Claiborne, and Kristen Harris -- An approach to capturing neuron morphological diversity / Haroon Anwar ... [et al.] -- Passive cable modeling / William R. Holmes -- Modeling complex neurons / Erik De Schutter and Werner Van Geit -- Realistic modeling of small neuronal networks / Ronald L. Calabrese and Astrid A. Prinz -- Large-scale network simulations in systems neuroscience / Reinoud Maex, Michiel Berends, and Hugo Cornelis.
catalogue key
6947690
 
Includes bibliographical references and index.
A Look Inside
Reviews
Review Quotes
"Neuroscientists with a computational background will benefit most from this book,and will find it a comprehensive source of information on how to build and critically assess neuronmodels at many levels of description." -- Giancarlo La Camera , TheQuarterly Review of Biology
"[S]uccessfully integrated data-driven modeling with experimental work_all of the material is accessible to experimentalists." -- Mathematical Reviews
"[S]uccessfully integrated data-driven modeling with experimental work...all of the material is accessible to experimentalists." -- Mathematical Reviews
To find out how to look for other reviews, please see our guides to finding book reviews in the Sciences or Social Sciences and Humanities.
Summaries
Main Description
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 Schurmann, Werner Van Geit, Mark C. W. van Rossum, Stefan Wils Computational Neuroscience series
Bowker Data Service Summary
This is a guide to computational modelling methods in neuroscience covering a range of modelling scales from molecular reactions to large neural networks.
Main Description
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.ContributorsPablo 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 Sch rmann, Werner Van Geit, Mark C. W. van Rossum, Stefan Wils
Table of Contents
Series Forewordp. vii
Introductionp. ix
Differential Equationsp. 1
Parameter Searchingp. 31
Reaction-Diffusion Modelingp. 61
Modeling Intracellular Calcium Dynamicsp. 93
Modeling Voltage-Dependent Channelsp. 107
Modeling Synapsesp. 139
Modeling Point Neuronsp. 161
From Hodgkin-Huxley to Integrate-and-Fire
Reconstruction of Neuronal Morphologyp. 187
An Approach to Capturing Neuron Morphological Diversityp. 211
Passive Cable Modelingp. 233
Modeling Complex Neuronsp. 259
Realistic Modeling of Small Neuronal Networksp. 285
Large-Scale Network Simulations in Systems Neurosciencep. 317
Software Appendixp. 355
Referencesp. 367
Contributorsp. 405
Indexp. 409
Table of Contents provided by Publisher. All Rights Reserved.

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