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eBook - ePub
About this book
Designed primarily as an introduction to realistic modeling methods, Computational Neuroscience: Realistic Modeling for Experimentalists focuses on methodological approaches, selecting appropriate methods, and identifying potential pitfalls. The author addresses varying levels of complexity, from molecular interactions within single neurons to the processing of information by neural networks. He avoids theoretical mathematics and provides just enough of the basic math used by experimentalists.
What makes this resource unique is the inclusion of downloadable resources that furnish interactive modeling examples. It contains tutorials and demos, movies and images, and the simulation scripts necessary to run the full simulation described in the chapter examples. Each chapter covers: the theoretical foundation; parameters needed; appropriate software descriptions; evaluation of the model; future directions expected; examples in text boxes linked to the downloadable resources; and references.
The first book to bring you cutting-edge developments in neuronal modeling. It provides an introduction to realistic modeling methods at levels of complexity varying from molecular interactions to neural networks. The book and downloadable resources combine to make Computational Neuroscience: Realistic Modeling for Experimentalists the complete package for understanding modeling techniques.
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Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Yes, you can access Computational Neuroscience by Erik De Schutter in PDF and/or ePUB format, as well as other popular books in Mathematics & Biotechnology in Medicine. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Cover
- Half Title
- Methods & New Frontiers in Neuroscience
- Title Page
- Copyright Page
- Series Preface
- Foreword
- Introduction
- Supplementary Resources Disclaimer
- Material Available on the CD-ROM
- About the Editor
- Contributors
- Table of Contents
- Chapter 1 Introduction to Equation Solving and Parameter Fitting
- Chapter 2 Modeling Networks of Signaling Pathways
- Chapter 3 Modeling Local and Global Calcium Signals Using Reaction-Diffusion Equations
- Chapter 4 Monte Carlo Methods for Simulating Realistic Synaptic Microphysiology Using MCell
- Chapter 5 Which Formalism to Use for Modeling Voltage-Dependent Conductances?
- Chapter 6 Accurate Reconstruction of Neuronal Morphology
- Chapter 7 Modeling Dendritic Geometry and the Development of Nerve Connections
- Chapter 8 Passive Cable Modeling — A Practical Introduction
- Chapter 9 Modeling Simple and Complex Active Neurons
- Chapter 10 Realistic Modeling of Small Neuronal Circuits
- Chapter 11 Modeling of Large Networks
- Chapter 12 Modeling of Interactions Between Neural Networks and Musculoskeletal Systems
- Index