Computational Modeling Methods for Neuroscientists

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9780262013277: Computational Modeling Methods for Neuroscientists
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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 neuron models at many levels of description. -- Giancarlo La Camera * The Quarterly Review of Biology * [S]uccessfully integrated data-driven modeling with experimental work...all of the material is accessible to experimentalists. * Mathematical Reviews *

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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 Schurmann, Werner Van Geit, Mark C. W. van Rossum, Stefan Wils

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Publicado por MIT Press Ltd, United States (2009)
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Descripción MIT Press Ltd, United States, 2009. Hardback. Condición: New. Language: English . Brand New Book. 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.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 Schurmann, Werner Van Geit, Mark C. W. van Rossum, Stefan Wils. Nº de ref. del artículo: AAH9780262013277

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Publicado por MIT Press Ltd, United States (2009)
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Descripción MIT Press Ltd, United States, 2009. Hardback. Condición: New. Language: English . Brand New Book. 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.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 Schurmann, Werner Van Geit, Mark C. W. van Rossum, Stefan Wils. Nº de ref. del artículo: AAH9780262013277

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Erik De Schutter (editor), John Rinzel (contributions), Bard Ermentrout (contributions), Pablo Achard (contributions), Werner Van Geit (contributions), Gwendal LeMasson (contributions), Upinder Bhalla (contributions), Stefan Wils (contributions), Erik De Schutter (contributions), Alain Destexhe (contributions), John R Huguenard (contributions), Arnd Roth (contributions), Mark C. W. van Rossum (contributions), Nicolas Brunel (contributions), Gwen Jacobs (contributions), Brenda Claiborne (contributions), Kristen Harris (contributions), Haroon Anwar (contributions), Imad Riachi (contributions), Sean Hill (contributions), Felix Schürmann (contributions), Henry Markram (contributions), William R. Holmes (contributions), Ronald L. Calabrese (cont
Publicado por The MIT Press 2009-09-04, Cambridge, Mass. |London (2009)
ISBN 10: 0262013274 ISBN 13: 9780262013277
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Descripción The MIT Press 2009-09-04, Cambridge, Mass. |London, 2009. hardback. Condición: New. Nº de ref. del artículo: 9780262013277

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Publicado por The MIT Press (2009)
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Descripción MIT Press Ltd. Hardback. Condición: New. New copy - Usually dispatched within 2 working days. Nº de ref. del artículo: B9780262013277

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Publicado por MIT Press Ltd, United States (2009)
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Descripción MIT Press Ltd, United States, 2009. Hardback. Condición: New. Language: English . This book usually ship within 10-15 business days and we will endeavor to dispatch orders quicker than this where possible. Brand New Book. 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.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 Schurmann, Werner Van Geit, Mark C. W. van Rossum, Stefan Wils. Nº de ref. del artículo: BTE9780262013277

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Descripción 2009. Hardback. Condición: NEW. 9780262013277 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. For all enquiries, please contact Herb Tandree Philosophy Books directly - customer service is our primary goal. Nº de ref. del artículo: HTANDREE01142283

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