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| | 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. | |
| | Product Details | | Author: | Wulfram Gerstner | | Paperback: | 496 pages | | Publisher: | Cambridge University Press | | Publication Date: | August 26, 2002 | | Language: | English | | ISBN: | 0521890799 | | Product Length: | 9.62 inches | | Product Width: | 6.96 inches | | Product Height: | 0.91 inches | | Product Weight: | 2.19 pounds | | Package Length: | 9.61 inches | | Package Width: | 6.85 inches | | Package Height: | 0.87 inches | | Package Weight: | 2.2 pounds | | Average Customer Rating: |  based on 4 reviews |
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| | Customer Reviews | Average Customer Review: ( 4 customer reviews )
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Most Helpful Customer Reviews
17 of 17 found the following review helpful:
All you ever wanted to know about spiking neuron models Aug 19, 2004
By Orlando Furioso
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.
9 of 9 found the following review helpful:
Impressive book Aug 30, 2004
By Harel Z. Shouval
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 found the following review helpful:
excellent book Dec 01, 2007
By bob
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.
this is theee book for Spiking neural systems May 09, 2011
By Ghassan Ayesh
spiking neuron models are explained in an intuitive simple way to the reader. (Hodgkin-Huxley, Spike-Response-Model, etc) plasticity and learning of spiking systems is explained along with spiking network systems as the chapters progress along into more deeper topics: spatially structured networks and other. All in a clear manner that does not sacrifice detail without too much dry mathematics. Spiking neural systems have long been considered a not-so-much-easy to understand or grasp compared to other neural models as it focuses on accommodating non-linear dynamic properties of the neural cell recorded in laboratory to arguably provide a more accurate biological model. But this book has it well presented and thought of. I consider this book along with the "Dynamical Systems of Neuroscience" book by Eugene M. Izhikevich to be the best on this topic.
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