De Schutter Computational Neuroscience

Realistic Modeling for Experimentalists
Erscheinungsjahr 2002
ISBN: 978-1-4200-3929-0
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 368 Seiten

Reihe: Frontiers in Neuroscience

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.

Zielgruppe

Experimental neuroscientists, molecular geneticists, physicists, mathematicians, and engineers

Autoren/Hrsg.

De Schutter, Erik

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften Neurobiologie, Verhaltensbiologie

Inhaltsverzeichnis

Foreword
Introduction
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