Download Ebook Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Computational Neuroscience Series)
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Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Computational Neuroscience Series)
Download Ebook Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Computational Neuroscience Series)
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Review
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 PsychologyNot only does the book set a high standard for theoretical neuroscience, it defines the field.―Dmitri Chklovskii, Neuron
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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. UlinskiDayan 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 MedicineAn 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 PittsburghTheoretical 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 DiegoThe 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 TechnologyTheoretical 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
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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: 0262541858
ISBN-13: 978-0262541855
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
24 customer reviews
Amazon Best Sellers Rank:
#336,711 in Books (See Top 100 in Books)
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. I personally only had a few problems in the area of dynamical systems which make their appearance in a few places in the book.On the negative side (though this book definitely deserves its 5 stars), I feel the book lacks a little sparkle and personality and can be a bit dry in places. Luckily there are a lot interesting MOOCs and videos on the Internet on Neuroscience that will provide the necessary background "excitement" and context you need while reading this book.Another (subjective) thing: I love calculus but I think its slightly overdone here. If you're _really_ doing computational neuroscience, you're probably going to use a lot of summation, simulation, discrete math, data analysis and algorithms but this book loves showing things in terms of Calculus. Yeah, its prettier with integrals but you're going to have to translate that into algorithms eventually. So, ironically, this book on Computational Neuroscience needs to be a bit more "computational."Finally, if you have some prior knowledge of Machine Learning you are likely to enjoy this book more. This was an unexpected bonus as I didn't realize that was so much overlap between Machine Learning and (real) Neural Systems before diving into the subject.
Yes, the book is heavy in mathematics. This is, after all, a book about COMPUTATIONAL neuroscience! Mathematics is a human language, like English or Mandarin. It happens to be a PRECISE languags. Be prepared to embrace the math, and do know you need to understand enough math so that the math itself speaks to you, like English or Mandarin prose. If you are not prepared for that, then think twice of purchasing this book or taking a class based on this book.I know, I know, many people go in to medicine in order to avoid math. I think that is to the eternal shame of the modern practitioner, but just know that computational neuroscience is not for you.I don't give many reviews five stars, or even one star. Those stars are too many standard deviations from the norm for most work. This is a good book, better than merely competent. With my math background, I am finding it very useful and understandable read.
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 off with an elementary neuroscience textbook and graduate into this one..
Slow reading but very thorough
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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