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Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Computational Neuroscience) [Paperback]

by Peter Dayan, Laurence F. Abbott
4.0 out of 5 stars  See all reviews (13 customer reviews)

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Book Description

September 1, 2005 0262541858 978-0262541855 1

Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.

The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.

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Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Computational Neuroscience) + Tutorial on Neural Systems Modeling + Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (Computational Neuroscience)
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Editorial Reviews


Eugene Izhikevich has written an excellent introduction to the application of nonlinear dynamics to the spiking patterns of neurons. There are dozens of clear illustrations and hundreds of exercises ranging from the very easy to Ph.D.-level questions. The book will be suitable for mathematicians and physicists who want to jump into this exciting field as well as for neuroscientists who desire a deeper understanding of the utility of nonlinear dynamics applied to biology.

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

Upon the ruins of Freud's failed attempt to construct a universal theory of mind, Hobson builds a catholic, brain-based edifice to account for the phenomenology of awake consciousness, sleep, and dreams in sickness and health. Its cornerstone -- that dreaming, psychosis, and psychedelic experiences are closely related phenomena caused by specific alterations in the brain's neuromodulatory systems -- allows him to explain a dizzying variety of altered states -- from hypnosis to lucid dreaming, from out-of-body to religious experiences, mind-altering drugs and so on -- within a single framework.

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

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)

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)

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)

The more we learn about the brain, the more we are coming to realize that understanding its development will be a key tounlocking its functions, especially its ability to adapt to new environments. The wide range of levels of development that can be studied, from the molecular to the cognitive, are described in this book by some of the leading researchers in this growing field of computational neural development.

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

About the Author

Peter Dayan is Professor and Director of the Gatsby Computational Neuroscience Unit at University College London.

Larry Abbott is Professor of Neuroscience and Co-Director of the Center for Theoretical Neuroscience at Columbia University.

Product Details

  • Series: Computational Neuroscience
  • Paperback: 480 pages
  • Publisher: The MIT Press; 1 edition (September 1, 2005)
  • Language: English
  • ISBN-10: 0262541858
  • ISBN-13: 978-0262541855
  • Product Dimensions: 10 x 7.8 x 0.9 inches
  • Shipping Weight: 2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (13 customer reviews)
  • Amazon Best Sellers Rank: #197,273 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews
50 of 57 people found the following review helpful
4.0 out of 5 stars Good overview 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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17 of 21 people found the following review helpful
5.0 out of 5 stars Great textbook and reference August 15, 2003
This book is certainly the most thorough textbook currently available
on many aspects of computational neuroscience. It works very carefully
through the fundamental assumptions and equations underlying large
tracts of contemporary quantitative analysis in neuroscience. It is
an ideal introductory book for those with a quantitative background,
and is destined to become a standard course book in the field.
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5 of 5 people found the following review helpful
5.0 out of 5 stars Good book for computational neuroscience January 27, 2007
By Ed Tan
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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20 of 26 people found the following review helpful
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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3 of 3 people found the following review helpful
4.0 out of 5 stars New Title: Theoretical Neuroscience - Firing Rate Models February 27, 2013
Format:Paperback|Verified Purchase
While I would like to say that this book is all encompassing, it only briefly touches upon one of the very important camps of computational neuroscience - the spiking models. Be warned that you will be viewing theoretical neuroscience through one lens targeted mainly at firing rates. A brief distinction: spiking models include the dynamic changes of the individual spikes of neurons into neural models, and tend to focus on the contribution of the temporal and electrical components of the neuronal action potentials as they move down the axons and interact with other neurons. Firing rate models condense this spiking behavior into a probability distribution governing the rate at which the neuron fires (think Hertz). This is a fantastically written book, but I would suggest izhikevich's book as a companion.
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2 of 2 people found the following review helpful
5.0 out of 5 stars Very Interesting Material, Well Written March 21, 2009
Format:Paperback|Verified Purchase
This is a very good book and I recommend it. As only slight criticism, the book should really start at Part II because beginning with neurons is more logical than starting with a high-level view. Nonetheless, a reader can do this himself so it is a nonissue.
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Most Recent Customer Reviews
5.0 out of 5 stars Perfect computational neuroscience book
Its a perfect summary of computational neuroscience. However, it will be a tall order to tackle if this is your first foray into neuroscience - it might be a better idea to start... Read more
Published 2 months ago by NM
5.0 out of 5 stars Great Textbook
I bought this book while taking a course on Coursera. The professors there followed the book pretty closely, so it was a must. Read more
Published 10 months ago by Boris Kogan
2.0 out of 5 stars Too Mathematical
This is the type of book where you get a line of Math for every two lines of text. While I am sure it is informative to math experts there really isn't enough text to even explain... Read more
Published 11 months ago by Graeme E. Smith
5.0 out of 5 stars Awesome condition
The book came in awesome condition and everything seems great. : ) I have not had a chance to really read the book yet but it seems to be in order.
Published 18 months ago by Tiffany
2.0 out of 5 stars Decent book, exceedingly technical, mathematical rigorous
I'm a neuroscience major and a medical student currently.

I bought this book to get a strong grasp of the theoretical underpinnings of computational neuroscience. Read more
Published on May 6, 2011 by Godchild From Godville
4.0 out of 5 stars "Theoretical Neuroscience" Dry but Informative
"Theoretical Neuroscience" is an in-depth introduction to modeling of neural systems from the chemical/electrical processes within neurons, up through small networks of neurons. Read more
Published on March 22, 2006 by John
2.0 out of 5 stars Good starting point for undergraduate students
This book covers a wide range of different and important subjects of this field and provides by this a good overview to students new in neuroscience. Read more
Published on July 4, 2005 by Zac
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