Computational Neuroscience: Realistic Modeling for Experimentalists, 1st Edition (Hardback) book cover

Computational Neuroscience

Realistic Modeling for Experimentalists, 1st Edition

Edited by Erik De Schutter

CRC Press

368 pages

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Hardback: 9780849320682
pub: 2000-11-22
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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 a CD-ROM that furnishes 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 CD-ROM; 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 CD-ROM combine to make Computational Neuroscience: Realistic Modeling for Experimentalists the complete package for understanding modeling techniques.


"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

Table of Contents



Introduction to Equation Solving and Parameter Fitting

Modeling Networks of Signaling Pathways

Modeling Local and Global Calcium Signals Using 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

About the Series

Frontiers in Neuroscience

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MEDICAL / Biotechnology
SCIENCE / Life Sciences / Neuroscience