Spiking Neuron Models (Paperback)

by Wulfram Gerstner (Author), Werner M. Kistler (Author) "From a biophysical point of view, action potentials are the result of currents that pass through ion channels in the cell membrane..." (more)
Key Phrases: presynaptic spike arrival, noisy reset, cortical neuron model, Spike Response Model, Locking Theorem (more...)

Editorial Reviews

Review
'The treatment undoubtedly holds pointers to future developments that will allow robots to come closer to their biological prototypes.' Journal of Robotica

Product Description
This introduction to spiking neurons can be used in advanced-level courses in computational neuroscience, theoretical biology, neural modeling, biophysics, or neural networks. It focuses on phenomenological approaches rather than detailed models in order to provide the reader with a conceptual framework. The authors formulate the theoretical concepts clearly without many mathematical details. While the book contains standard material for courses in computational neuroscience, neural modeling, or neural networks, it also provides an entry to current research. No prior knowledge beyond undergraduate mathematics is required.

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Product Details

• Paperback: 400 pages
• Publisher: Cambridge University Press; 1 edition (August 15, 2002)
• Language: English
• ISBN-10: 0521890799
• ISBN-13: 978-0521890793
• Product Dimensions: 9.6 x 6.8 x 0.9 inches
• Shipping Weight: 2.2 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars See all reviews (3 customer reviews)
• Amazon.com Sales Rank: #261,170 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

14 of 14 people found the following review helpful:

5.0 out of 5 stars All you ever wanted to know about spiking neuron models, August 19, 2004

By	Roland Schaette (Berlin, Germany) - See all my reviews

I have used this book as an introduction and reference book for modeling neurons since I started my thesis work in computational neuroscience two years ago. It covers various types of spiking neuron models (e.g. Hodgkin-Huxley, Morris-Lecar, Integrate&Fire, Spike-Response-Model), noise in neuron models, population models, and plasticity/learning.
It is a very useful book, clearly written and comprehensive, providing sufficient detail and background information. Derivations of the equations are clearly presented and understandable to anyone with a decent knowledge of mathematics. A degree in physics is not required in order to read this book ;-) With this book and some programming skills, one has a solid foundation for modeling neurons on various levels.
I also like the literature recommendations at the end of each chapter, they give a good overview over important original papers and further reviews.
I would strongly recommend this book to undergraduate and PhD-students in computational neuroscience, as well as to anyone interested in modeling neurons.

8 of 8 people found the following review helpful:

5.0 out of 5 stars Impressive book, August 30, 2004

By	Harel Z. Shouval (USA) - See all my reviews (REAL NAME)

This is a very impressive book. It covers in a systematic manner a broad portion of the field of theoretical neuroscience. It covers topics from models of single spiking neurons, through networks of interconnected neurons and up to neuronal plasticity. This book is also written very well. The style of this book reflects the background of the authors as Physicists; it therefore strives for simplicity wherever possible.
I used chapters from this book as a basis for some of my lectures in a course I teach: Introduction to Theoretical/Computational Neuroscience, a graduate level course. I especially liked the systematic approach they have adopted for describing various simplifications of the Hodgkin-Huxley equations.

3 of 3 people found the following review helpful:

5.0 out of 5 stars excellent book, November 30, 2007

By	bob (Boston, MA United States) - See all my reviews

very well written, easy to understand, walks you through the logic of each part of each equation. builds up more and more complex models based upon the previous models. You'll learn a lot of practical neurobiology stuff other than just modeling too.