Free Download From Computer to Brain: Foundations of Computational Neuroscience
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From Computer to Brain: Foundations of Computational Neuroscience
Free Download From Computer to Brain: Foundations of Computational Neuroscience
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Review
From the Foreword: "In From Computer to Brain: Foundations of Computational Neuroscience, William W. Lytton provides a gentle but rigorous introduction to the art of modeling neurons and neural systems. It is an accessible entry to the methods and approaches used to model the brain at many different levels, ranging from synapses and dendrites to neurons and neural circuits. This book gives a balanced view of the wide range of modeling techniques that are available in a way that is accessible to a wide audience. This is a book that will bring a smile to your face as well as inspire your imagination." -- Terrence J. SejnowskiHoward Hughes Medical Institute, Salk Institute, University of California at San Diego
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Product details
Paperback: 364 pages
Publisher: Springer; Softcover reprint of the original 1st ed. 2002 edition (October 1, 2002)
Language: English
ISBN-10: 0387955267
ISBN-13: 978-0387955261
Product Dimensions:
6.1 x 0.9 x 9.2 inches
Shipping Weight: 1.5 pounds (View shipping rates and policies)
Average Customer Review:
4.7 out of 5 stars
5 customer reviews
Amazon Best Sellers Rank:
#1,309,869 in Books (See Top 100 in Books)
I really enjoyed reading this book - it reads easily, and it is often actually fun to read. That being said, this book was not entirely what I expected. I learned a lot about how computers operate, artificial neural networks, and how the abstract concepts of neural networks approximate (or fail to approximate) the biological reality. Other than simple linear algebra-based artificial neural network simulations, this book will not teach you how to do computational modeling of neurons or networks, but I don't think that a how-to-model manual was the author's intent. This book is a very accessible introduction to concepts in computational neuroscience that are difficult for novices to learn from other texts because computational neuroscience is typically formulated in the language of mathematics. In order to actually do some modeling of your own, you are still going to need to develop the required skills in math and programming. Neuroscience is overwhelmingly full of facts, which you can very easily appreciate if you read a general neuroscience text like From Neuron to Brain. The great thing about this book is that it is primarily principle-based - it doesn't get lost in a myriad of facts. Each chapter seems to contain one solid theoretical principle or concept that can be used to understand the brain.
A bit dated, but still a good introduction. Might want to supplement it with The Troyner Notes.
Very good book for beginners. Simple and direct to the point. Gives good explanation for people coming from the biologics or computer sciences. Has an reference area on the end of the book for topics like calculus and other import concepts.I really recommend it.
A clear and comprehensive introduction to computational neuroscience. The author, as an active researcher in the field, is highly qualified to be the guide for a beginner, as well as for an expert who would like to refresh and reorganize what they know. This book covers the biological and mathematical aspects of the body of knowledge that we have about the brain today. It covers the basic biology of neurons and their networks, the modeling of their biophysics, how they process information, how they learn and remember, and how neural activity relates to the wider field of computing. The reader will be accompanied throughout the book by the author's erudition as well as a good sense of humor.
I have used this book as the basis for a junior/senior seminar on Models of Brain Function every every since it came out. The students really loved it and they developed a genuine interest in the filed. I wish that there was a second edition coming out. Compared to other texts aimed at the same audience I found this to be eminently accessible.
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