Computational modeling methods for neuroscientists

✍ Scribed by Erik De Schutter

• Publisher: MIT Press
• Year: 2010
• Tongue: English
• Leaves: 433
• Series: Computational neuroscience
• Edition: 1
• Category: Library

✦ Synopsis

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 Computational Neuroscience series

✦ Table of Contents

Contents......Page 6
Series Foreword......Page 8
Introduction......Page 10
1 Differential Equations......Page 14
2 Parameter Searching......Page 44
3 Reaction-Diffusion Modeling......Page 74
4 Modeling Intracellular Calcium Dynamics......Page 106
5 Modeling Voltage-Dependent Channels......Page 120
6 Modeling Synapses......Page 152
7 Modeling Point Neurons......Page 174
8 Reconstruction of Neuronal Morphology......Page 200
9 An Approach to Capturing Neuron Morphological Diversity......Page 224
10 Passive Cable Modeling......Page 246
11 Modeling Complex Neurons......Page 272
12 Realistic Modeling of Small Neuronal Networks......Page 298
13 Large-Scale Network Simulations in Systems Neuroscience......Page 330
Software Appendix......Page 368
References......Page 380
Contributors......Page 418
Index......Page 422

✦ Subjects

Биологические дисциплины;Физиология животных;Нейрофизиология животных;