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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.
- Sales Rank: #1612790 in Books
- Brand: Brand: Cambridge University Press
- Published on: 2002-08-26
- Original language: English
- Number of items: 1
- Dimensions: 9.72" h x .98" w x 6.85" l, 2.19 pounds
- Binding: Paperback
- 496 pages
Features
- Used Book in Good Condition
Review
'The treatment undoubtedly holds pointers to future developments that will allow robots to come closer to their biological prototypes.' Journal of Robotica
About the Author
Wulfram Gerstner is Director of the Laboratory of Computational Neuroscience and a Professor of Life Sciences and Computer Science at the Ecole Polytechnique Federale de Lausanne (EPFL) in Switzerland. He studied physics in Tubingen and Munich and holds a PhD from the Technical University of Munich. His research in computational neuroscience concentrates on models of spiking neurons and synaptic plasticity. He teaches computational neuroscience to physicists, computer scientists, mathematicians, and life scientists. He is a co-author of Spiking Neuron Models (Cambridge, 2002).
Werner M. Kistler received a Master's and PhD in physics from the Technical University of Munich. He previously worked as Assistant Professor in Rotterdam for computational neuroscience and he is the co-author of Spiking Neuron Models (Cambridge, 2002). He is now working in Munich as a patent attorney. His scientific contributions are related to spiking neuron models, synaptic plasticity, and network models of the cerebellum and the inferior olive.
Most helpful customer reviews
0 of 0 people found the following review helpful.
this is theee book for Spiking neural systems
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.
3 of 3 people found the following review helpful.
excellent book
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.
19 of 19 people found the following review helpful.
All you ever wanted to know about spiking neuron models
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.
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