Spiking Neuron Models: Single Neurons, Populations, Plasticity

By Werner M Kistler, Wulfram Gerstner, Werner M. Kistler

Book Overview

Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information... This description may be from another edition of this product.

• Format: Hardcover
• Language: English
• ISBN: 0521813840
• ISBN13: 9780521813846
• Release Date: August 2002
• Publisher: Cambridge University Press
• Length: 494 Pages
• Weight: 2.60 lbs.
• Dimensions: 9.8" x 1.0" x 7.0"

Customer Reviews

excellent book

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.

Impressive book

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.

All you ever wanted to know about spiking neuron models

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.