Principles of Neural Information Theory: Computational Neuroscience and Metabolic Efficiency (Tutorial Introductions)

This book, along with Principles of Neural Design, is what you gift your best rival. You gift it with a card reading, "race you to the top", even though there are a thousand avenues to further examine once every phoneme written between the two texts is devoured—that is to say finishing the books are only (the best) beginnings, and everyone finds their own pinnacle. Don’t let the pages-to-price ratio deter you, what James V Stone has done in this succinct account of exactly what the esteemed reviewers claim is worth more than entire University courses to those with an active imagination rendering each formalism, example, and accessible explanation the author elegantly synthesizes from the most prominent neuroscience-related literature of the past few generations. More, the concise argument orients the minds of those who’ve already deeply considered the literature as well as guide the approach to primary text for newcomers. This type of work defines career goals.

Another home run by Mr Stone. Great concepts condensed to an easy to digest format with fun examples showing how one might derive things like bits per second at the neuron level. Great fun!

This is a great introductory text. I highly recommend it for students and others interested in learning how information theory can be applied to understanding neural systems.

A clear, compact, and up-to-date survey of the efficient coding hypothesis and related principles of neural design. An invaluable guide to the background needed to read current primary literature. I particularly appreciated the discussion of color opponency.

The book “Principles of Neural Information Theory” by James V Stone is written in a way that it serves as a great introduction into this topic for students as well as for senior researchers. The author uses many examples of the visual system to describe general principles of information, neural coding, computational neuroscience, and much more. Thus, this book is written for every neuroscientist concerned with these topics - and a must-read for every visual neuroscientist. Peter Sterling wrote in his comment about this book “I only wish I'd had this text as a student!”. In fact, I couldn't agree more with this statement.

Clear and well written text that is accessible for many people.