Computational Neuroscience: Realistic Modeling for Experimentalists

by Erik De Schutter (Editor)

Overview

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.

Product Details

ISBN-13:	9781040201336
Publisher:	CRC Press
Publication date:	11/22/2000
Series:	Frontiers in Neuroscience
Sold by:	Barnes & Noble
Format:	eBook
Pages:	368
File size:	8 MB

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.

Editorial Reviews

"This book is a refreshing exception to [the dour expectations of multiauthored books]. Instead of merely repackaging their own scientific findings, the authors focused on the methodology of creating computational implementations of conceptual models of biological neurons and networks."

- N.T. Carnevale, Psychology, Yale University, in The Quarterly Review of Biology, Vol. 77, No. 2

From the Publisher

This introduction to realistic modeling methods for neuroscientists focuses on methodological approaches, selecting appropriate methods, and identifying potential pitfalls. The 12 contributions describe techniques for modeling networks of signaling pathways, local and global calcium signals using reaction-diffusion equations, dendritic geometry and the development of nerve connections, simple and complex active neurons, and interactions between neural networks and musculoskeletal systems. Other topics include equation solving and parameter fitting, Monte Carlo methods, accurate reconstruction of neuronal morphology, and passive cable modeling. The CD-ROM contains images, data files, simulation scripts, and software for the models described in the text. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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