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Spiking Neuron Models by W. Gerstner & W.M. Kistler

Spiking Neuron Models 



  • Wulfram Gerstner
  • Werner M. Kistler


  • Paperback: 400 pages
  • Publisher: Cambridge University Press; 1st edition (August 15, 2002)
  • Language: English
  • ISBN: 0521890799
  • Shipping Weight: 2.19 pounds


Book 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. 

Book Info

An introduction to spiking neurons aimed at those taking courses in computational neuroscience, theoretical biology, biophysics, or neural networks. Useful for biologists who are interested in mathematical modelling as well as for students of physics, mathematics, or computer science. –This text refers to the Hardcover edition.  



All you ever wanted to know about spiking neuron models, August 19, 2004

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.