Buch, Englisch, 368 Seiten, Format (B × H): 162 mm x 241 mm, Gewicht: 712 g
Reihe: Frontiers in Neuroscience
Realistic Modeling for Experimentalists
Buch, Englisch, 368 Seiten, Format (B × H): 162 mm x 241 mm, Gewicht: 712 g
Reihe: Frontiers in Neuroscience
ISBN: 978-0-8493-2068-2
Verlag: Taylor & Francis Inc
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.
Zielgruppe
Professional
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Foreword. Introduction. Introduction to Equation Solving and Parameter Fitting. Modeling Networks of Signaling Pathways. Reaction-Diffusion Systems. Monte Carlo Methods for Simulating Realistic Synaptic Microphysiology Using Mcell. Which Formalism to Use for Modeling Voltage-Dependent Conductances. Accurate Reconstruction of Neuronal Morphology. Modeling Dendritic Geometry and the Development of Nerve Connections. Passive Cable Modeling - A Practical Introduction. Modeling Simple and Complex Active Neurons. Realistic Modeling of Small Neuronal Circuits. Modeling of Large Networks. Modeling of Interactions Between Neural Networks and Musculoskeletal Systems. Demo's and Other Material Available on the CD-Rom. Index.
