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Principles of Computational Modelling in Neuroscience 1st Edition

5.0 out of 5 stars 1 customer review
ISBN-13: 978-0521877954
ISBN-10: 0521877954
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Editorial Reviews

Review

"Here at last is a book that is aware of my problem, as an experimental neuroscientist, in understanding the maths, the book helps me deal with it with the patience that the team always showed to students and professors alike. I expect it to be as mind expanding as my involvement with its authors was over the years. I only wish I had had the whole book sooner - then my students and post-docs would have been able to understand what I was trying to say and been able to derive the critical tests of the ideas that only the rigor of the mathematical formulation of them could have generated."
Gordon W. Arbuthnott, Okinawa Institute of Science and Technology

"This is a wonderful, clear and compelling text on mathematically-minded computational modelling in neuroscience. It is beautifully aimed at those engaged in capturing quantitatively, and thus simulating, complex neural phenomena at multiple spatial and temporal scales, from intracellular calcium dynamics and stochastic ion channels, through compartmental modelling, all the way to aspects of development. It takes particular care to define the processes, potential outputs and even some pitfalls of modelling; and can be recommended for containing the key lessons and pointers for people seeking to build their own computational models. By eschewing issues of coding and information processing, it largely hews to concrete biological data, and it nicely avoids sacrificing depth for breadth. It is very suitably pitched as a Master's level text, and its two appendices, on mathematical methods and software resources, will rapidly become dog-eared."
Peter Dayan, University College London

"This book has done a nice job of laying out their strategy or covering major topics in the field of computational neuroscience while maintaining a well-organized structure. It is prepared for both expert and non-expert readers with an elementary background in neuroscience and some high school mathematics. This is a timely, well-written book that provides a comprehensive, in-depth and state-of-the-art coverage of computational modeling in neuroscience. It can serve as an excellent text for a graduate level course in computational neuroscience, as well as a valuable reference for experimental neuroscientists, computational neuroscientists and people working in relevant areas such as neuroinformatics and systems biology."
Li Shen, Briefings in Bioinformatics

Book Description

For neuroscientists at all levels and for people from the informational and physical sciences who want to develop computational models of the neuron and neural circuits. It presents the principles of computational neuroscience in a clear and coherent manner, and addresses practical issues that arise in modelling projects.
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Product Details

  • Hardcover: 404 pages
  • Publisher: Cambridge University Press; 1 edition (August 15, 2011)
  • Language: English
  • ISBN-10: 0521877954
  • ISBN-13: 978-0521877954
  • Product Dimensions: 7.4 x 0.9 x 9.7 inches
  • Shipping Weight: 2.3 pounds (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #1,118,418 in Books (See Top 100 in Books)

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In "Principles of Computational Modeling in Neuroscience" the authors present a comprehensive treatment describing the principles involved in (a) designing computational models, (b) analyzing the models, and (c) developing simulation techniques for validating abstract and mathematical models of the nervous system and its components. A good balance is maintained between the basic mathematical foundations needed and the qualitative description of the basics underlying the model. More advanced mathematical treatments are inserted in boxes associated with the basic presentation. The placement of figures and boxes is visually pleasant. Examples of some simple models are given with extensive references to higher order theoretical models and simulations.
The book is intended for postgraduates and researchers in the field of experimental and computational neuroscience. Readers are assumed to have basic knowledge of the multidisciplinary fields of physical sciences, computer science and engineering, and neuroscience. Organization of chapters is structured well evolving from the lowest component level to the highest system and network level. A detailed chapter-by-chapter summary of the book follows.

Summary
The first and introductory chapter defines computational modeling and presents a synopsis of the succeeding chapters of the book. The authors define the term computational model as "Hodgkin and Huxley's simulation of the propagation of a nerve impulse (action potential) along an axon." Remaining chapters address the questions regarding the following: "deciding what type of model to use; at what level to model; what aspects of the system to model; and how to deal with parameters that have not or cannot be measured experimentally.
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