Редактирование: Whole brain emulation (раздел)

== Computational Neuroscience ==

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{{Publication
| Title = Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
| Cover = Theoretical_Neuroscience_cover.jpg
| Author = Peter Dayanm, L.F. Abbott
| Date = 2001
| Description = A fantastic introduction to computational neuroscience, provides a very detailed overview of computational models of nervous systems and at varying scales. A very thorough book that covers many aspects of the field, working very carefully through every fact and equation.

* [http://www.amazon.com/Theoretical-Neuroscience-Computational-Mathematical-Modeling/dp/0262041995 Buy]
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{{Publication
| Title = The Computational Brain
| Cover = The_Computational_Brain_cover.jpg
| Author = Patricia Churchland, Terrence J. Sejnowski
| Date = 1992
| Description = No, it doesn't actually teach you how to build computational models -- It does, however, show a variety of them, including strengths and weaknesses --, but it's a good introduction to computational neuroscience, with a focus on the visual and sensorimotor systems. A basic introduction to neuroscience is provided before jumping into the computational side. The book can be read by neuroscientists and computer scientists alike, or just people trying to teach themselves comp neurosci, but a moderate neuroscience knowledge (See Neuroscience: Exploring the Brain) is recommended. Still, all technicalities are explained in a massive glossary the text has.

* [http://www.amazon.com/Computational-Brain-Neuroscience/dp/0262531208 Buy]
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{{Publication
| Title = Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting
| Cover = Dynamical Systems in Neuroscience cover.jpg
| Author = [[Eugene Izhikevich]]
| Date = 2007
| Description = A comprehensive computational neuroscience overview of mathemathical model of electrophysiology and spiking. Each chapter presents an increasingly complex models with problems and Matlab code samples, striking a perfect balance between the biological neuroscience and the abstract, mathematical models.

<blockquote>''A unique contribution to the theoretical neuroscience literature that can serve as a useful reference for audiences ranging from quantitatively skilled undergraduates interested in mathematical modeling, to neuroscientists at all levels, to graduate students and even researchers in the field of theoretical neuroscience.''</blockquote> - Jonathan E. Rubin Mathematical Review

* [http://www.amazon.com/Dynamical-Systems-Neuroscience-Excitability-Computational/dp/0262090430 Buy]
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