biophysical simulation
Branching Event
Category=UGK
CRC Press LLC
Dendritic Growth Model
dendritic morphology
Differential Algebraic Equation System
diffusion
Electrotonic Length
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
equations
Finite Difference Method
Growth Model
Hodgkin Huxley Model
hodgkinhuxley
Ion Channel
Ligand Molecules
Local Truncation Error
Low Threshold Ca2
MARKOV Model
MC
model
Model Testing
molecular signaling pathways
monte
neural circuit modeling
Neuronal Reconstructions
Ode Equation
parameter fitting techniques
Passive Cable
plasticity
Purkinje Cell
random
Ray Marching
reaction
realistic neuron simulation tutorial
Stained Neurons
synaptic
synaptic transmission analysis
Terminal Segments
Time Step Iterations
Voltage Clamp
Voltage Clamp Data
walk
Product details
- ISBN 9780849320682
- Weight: 800g
- Dimensions: 156 x 234mm
- Publication Date: 22 Nov 2000
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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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 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.
