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Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Computational Neuroscience Series) Revised ed. Edition

4.1 out of 5 stars 20 customer reviews
ISBN-13: 978-0262541855
ISBN-10: 9780262541855
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Editorial Reviews

Review

Peter Dayan and L.F. Abbott have crafted an excellent introduction to the various methods of modeling nervous system function. The chapters dealing with neural coding and information theory are particularly welcome because these are new areas that are not well represented in existing texts.

(Phillip S. Ulinski)

Dayan and Abbott inspire us with a work of tremendous breadth, and each chapter is more exciting than the next. Everyone with an interest in neuroscience will want to read this book. A truly remarkable effort by two of the leaders in the field.

(P. Read Montague, Professor, Division of Neuroscience, and Director, Center for Theoretical Neuroscience, Baylor College of Medicine)

It will not be surprising if this book becomes the standard text for students and researchers entering theoretical neuroscience for years to come.

(M. Brandon Westover Philosophical Psychology)

Not only does the book set a high standard for theoretical neuroscience, it defines the field.

(Dmitri Chklovskii Neuron)

An excellent book. There are a few volumes already available in theoretical neuroscience but none have the scope that this one does.

(Bard Ermentrout, Department of Mathematics, University of Pittsburgh)

Theoretical Neuroscience provides a rigorous introduction to how neurons code, compute, and adapt. It is a remarkable synthesis of advances from many areas of neuroscience into a coherent computational framework. This book sets the standards for a new generation of modelers.

(Terrence J. Sejnowski, Howard Hughes Medical Institute, Salk Institute for Biological Studies, and University of California, San Diego)

The first comprehensive textbook on computational neuroscience. The topics covered span the gamut from biophysical faithful single cell models to neural networks, from the way nervous systems encode information in spike trains to how this information might be decoded, and from synaptic plasticity to supervised and unsupervised learning. And all of this is presented in a sophisticated yet accessible manner. A must buy for anybody who cares about the way brains compute.

(Christof Koch, Lois and Victor Troendle Professor of Cognitive and Behavioral Biology, California Institute of Technology)

Theoretical Neuroscience marks a milestone in the scientific maturation of integrative neuroscience. In the last decade, computational and mathematical modelling have developed into an integral part of the field, and now we finally have a textbook that reflects the changes in the way our science is being done. It will be a standard source of knowledge for the coming generation of students, both theoretical and experimental. I urge anyone who wants to be part of the development of this science in the next decades to get this book. Read it, and let your students read it.

(John Hertz, Nordita (Nordic Institute for Theoretical Physics), Denmark)

About the Author

L.F. Abbott is the Nancy Lurie Marks Professor of Neuroscience and Director of the Volen Center for Complex Systems at Brandeis University. He is the coeditor of Neural Codes and Distributed Representations (MIT Press, 1999).

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Product Details

  • Series: Computational Neuroscience Series
  • Paperback: 480 pages
  • Publisher: The MIT Press; Revised ed. edition (September 1, 2005)
  • Language: English
  • ISBN-10: 9780262541855
  • ISBN-13: 978-0262541855
  • ASIN: 0262541858
  • Product Dimensions: 8 x 0.8 x 10 inches
  • Shipping Weight: 2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (20 customer reviews)
  • Amazon Best Sellers Rank: #39,554 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

By Dr. Lee D. Carlson HALL OF FAMEVINE VOICE on May 24, 2003
Format: Hardcover Verified Purchase
This book is a detailed overview of the computational modeling of nervous systems from the molecular and cellular level and from the standpoint of human psychophysics and psychology. They divide their conception of modeling into descriptive, mechanistic, and interpretive models. My sole interest was in Part 3, which covers the mathematical modeling of adaptation and learning, so my review will be confined to these chapters. The virtue of this book, and others like it, is the insistence on empirical validation of the models, and not their justification by "thought experiments" and arm-chair reasoning, as is typically done in philosophy.
Part 3 begins with a discussion of synaptic plasticity and to what degree it explains learning and memory. The goal here is to develop mathematical models to understand how experience and training modify the neuronal synapses and how these changes effect the neuronal patterns and the eventual behavior. The Hebb model of neuronal firing is ubiquitous in this area of research, and the authors discuss it as a rule that synapses change in proportion to the correlation of the activities of pre- and postsynaptic neurons. Experimental data is immediately given that illustrates long-term potentiation (LTP) and long-term depression (LTD). The authors concentrate mostly on models based on unsupervised learning in this chapter. The rules for synaptic modification are given as differential equations and describe the rate of change of the synaptic weights with respect to the pre- and postsynaptic activity. The covariance and BCM rules are discussed, the first separately requiring postsynaptic and presynaptic activity, the second requiring both simultaneously.
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Format: Paperback
I am a mathematician and economist interested in how human brain works. To me, (so far) this is the best book using equations to describe the overall picture of brain functions. Even though it might not touch in-depth research topics, I am sure it gives anyone interested in neuroscience very solid foundations on which more advance topics are built. (It actually invites me to more in-depth research topics, such as reinforcement learning, reward-punishment system, etc.)

If math is your familiar language (says, system of differential equations and Bayesian probability), and you are interested to know, in technical details, how the brain functions, this book is for you. Then, I think, you can go into research topics of your interests after finishing reading this book.
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Format: Hardcover
This text will become a standard course book for Graduate Schools in Computational Neurosciences. You need to know advanced engineering mathematics & probability theory to be able to understand this book. Dayan & Abbott model primary visual cortical, MT, LIP, and Motor cortical neurons as single units, but also as populations (clusters) of firing cells. They discuss Bayes Theorem, probability theory as it applies to the brain, and parietal lobe function as well. They derive all the equations associated with these models for the student so that more advanced parts of the book are comprehensible. The book is not meant to be a general Neuroscience book, but rather a course book about neuronal modeling, computational neurobiology, and neural engineering. It serves these three purposes well. In my opinion, this is the best written account of neuron modeling out there for the graduate student and researcher. Methods in Neuronal Modeling by Christof Koch is the other great book on this subject. If you own these two books you should be able to advance in high level neural modelling. There are numerous equations and formulae of interest throughout each chapter in these two volumes. The price of 39.00 USD for the hardcover is really quite a bargain.
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Format: Paperback Verified Purchase
This book was an eye opener for me. Scientists still fully don't understand how neurons "think" and "learn" but I was shocked to learn how much we _do know_. After reading significant chunks for this book I feel inspired and want to recommend this book to others who have an interest in this subject. This book is a great overview of the field of Computational Neuroscience. The authors convincingly explain the most fundamental theoretical concepts in Computation Neuroscience and back them up by describing some of the major experiments in this field. It does not oversimplify nor does it over-complicate, for a first introduction.

Coming to the specific merits of the book, what stands out is the quality of the prose and explanations. The book is tightly written, so, it gets to the point fast and explains what it needs to without much ado. Because this book is quite succinct (and does not "over explain") you might need do multiple readings of the chapters to understand the content. Actually, it was only on the repeated readings that I came to appreciate the overall coherence of this book.

In this book you will find that complex math and derivations are often either relegated to the chapter appendix or left to the reader to cover independently. This approach actually makes the book less daunting because you don't need to wade through dozens of pages of topics that are not really computational neuroscience but Math!

Lest someone get the impression that the book is too mathematical I want to point out that you need to have a standard science/engineering background in Calculus and differential equations and basic knowledge of Physics/Chemistry and you should be fine.
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