Computational Neuroscience: Realistic Modeling for Experimentalists

✍ Scribed by Erik De Schutter (Editor)

• Publisher: CRC Press
• Year: 2000
• Leaves: 370
• Edition: 1
• Category: Library

✦ Synopsis

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

✦ Table of Contents

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.

✦ Subjects

Bioscience;Neuroscience;Engineering & Technology;Biomedical Engineering;Mathematics & Statistics;Applied Mathematics;Mathematical Biology