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.

Product Details

• Amazon Sales Rank: #341791 in Books
• Published on: 1999-06-25
• Original language: English
• Number of items: 1
• Binding: Paperback
• 416 pages

Review
"A joy to read. . . . This book will undoubtedly become a classic. The ideas presented in it have already begun (in no small part through the work of the authors) to reshape our views of the neural code. This book will make them accessible to a much wider audience."
—Anthony Zador, Science

About the Author
Fred Rieke is Assistant Professor in the Department of Physiology and Biophysics, University of Washington. David Warland is Research Associate in the Department of Molecular and Cellular Biology, Harvard University. Rob de Ruyter van Steveninck is Research Scientist, William Bialek a Senior Research Scientist, both at the NEC Research Institute.

Customer Reviews

excellent book, very clearly written
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.

Was provocative, but may not point the way forward.
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.

Taking the organism's point of view
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.