by Fred Rieke, David Warland, Rob de Ruyter van Steveninck, William Bialek
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Product Description
What does it mean to say that a certain set of spikes is the right answer to a computational problem? In what sense does a spike train convey information about the sensory world? Spikes begins by providing precise formulations of these and related questions about the representation of sensory signals in neural spike trains. The answers to these questions are then pursued in experiments on sensory neurons.
Intended for neurobiologists with an interest in mathematical analysis of neural data as well as the growing number of physicists and mathematicians interested in information processing by "real" nervous systems, Spikes provides a self-contained review of relevant concepts in information theory and statistical decision theory.
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Average Customer Review:
0 of 0 people found the following review helpful:
 excellent book, very clearly written, 2008-03-23
excellent book, lots of very good examples and figures, everything very clearly explained, clarifies a lot of things in a very logical step by step way.
5 of 5 people found the following review helpful:
 Was provocative, but may not point the way forward., 2007-03-06
A decade ago, computational neuroscientists and some neurophysiologists were twittering with excitement about information theory. Finally, a tool that could decode the "noise" observed when we record neuronal spike signals!
These days...information theory has become part of the standard toolkit in a few types of experiments. But we're not much closer to understanding the neural code(s) than when this book was written. Nevertheless, Bialek's group of mostly physicists turned neuroscientists continue to develop information theoretic tools. Perhaps they'll come up with one that's not just another hammer.
The authors of Spikes may still turn out to have been ahead of their time (just like Barlow, MacKay and McCulloch, who originally applied information theory to neurons). Or their research program may turn out to have been a detour, a misguided attempt to find a particular physical universal in evolutionarily contingent biological systems.
If you're interested in theoretical neuroscience, I would definitely recommend Dayan and Abbott's textbook. van Hemmen and Sejnowski's "23 Problems in Systems Neuroscience" also has good bits. If you really want to read about information theory, David MacKay's new book is available on the web.
4 of 4 people found the following review helpful:
Taking the organism's point of view, 2006-01-09
What would it mean to understand how a neuron works? Traditionally this questions has been addressed by attempting to solve the encoding problem-that is, given a sample stimulus input, construct a model neuron that predicts the temporal pattern of spikes resulting from observing that stimulus. While much progress has been made on this front (for example, using Weiner-Volterra expansion methods), the remarkable contribution of this book is to turn the question on its head. Instead of asking how a neuron encodes information about the world into discrete spikes, this book instead takes the organism's point of view. Namely, animals do not "observe" the world, but only the spike trains that encode sensory stimuli, and they must be capable of producing successful behavior on the basis of these discrete spikes.
The question for the researcher becomes, given a sample spike train, what do we know about the environmental situation that resulted in this spike train? This question, the decoding problem, is the problem that biological organisms must solve. Perhaps even more remarkably, when posed as a decoding problem, many of the nonlinearities of the neural response disappear, and we are left with a simple linear filtering problem.
`Spikes: Exploring the Neural Code' presents numerous recent results on this front, drawing on behavioral and neurological data as diverse as bat echo location, moth evasion tactics, vertebrate and invertebrate vision, and the incredible French cave beetle capable of reliably detecting temperature changes as small as 1/1000 of a degree. To interpret these results, the authors rely on a variety of mathematical techniques, from probability theory and information theory, to optimal filtering and kernel approaches. This book is very rigorous, and not for math-phobic readers. Understanding all of the ideas presented in this book will take work: about one-third of the book is devoted to a series of appendixes or "Mathematical asides". Finally, one of the most valuable contributions of this book is its extensive list of references for the ideas and results presented in each chapter.
6 of 6 people found the following review helpful:
The Neural Code (Variability & Meaning), 2004-06-11
Rieke et al. have written a great book exploring how single neurons and populations of cells code information sensitive spikes and patterns of spikes, i.e. single action potentials, clusters, repetitive bursts, or single bursts. There are quite a few equations in the book, but the authors have written the text so well, that an advanced undergraduate or graduate student in the Neurosciences can understand it. One of my favorate sections discusses the Entropy of information, and the entropy of neural code patterns. This concept will likely shape the future of many neurophysiological investigations.
1 of 30 people found the following review helpful:
Good book study for neural code, 2002-12-27
i looked this book, some difficults. but study neural code...
this book help you study neural code, and good friends...
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