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Our perception of the world is driven by input from the sensory nerves. This input arrives encoded as sequences of identical spikes. Much of neural computation involves processing these spike trains. 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.The authors invite the reader to play the role of a hypothetical observer inside the brain who makes decisions based on the incoming spike trains. Rather than asking how a neuron responds to a given stimulus, the authors ask how the brain could make inferences about an unknown stimulus from a given neural response. The flavor of some problems faced by the organism is captured by analyzing the way in which the observer can make a running reconstruction of the sensory stimulus as it evolves in time. These ideas are illustrated by examples from experiments on several biological systems.
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. A quantitative framework is used to pose precise questions about the structure of the neural code. These questions in turn influence both the design and analysis of experiments on sensory neurons.
- Sales Rank: #249975 in Books
- Published on: 1999-06-25
- Original language: English
- Number of items: 1
- Dimensions: 8.90" h x .90" w x 7.00" l, 1.46 pounds
- Binding: Paperback
- 416 pages
Review
" Spikes is a masterful account of a methodology that has become essential for anyone studying how neuronal spiking patterns encode information. New results and techniques are clearly and insightfully presented and placed within the historical context of earlier work. Both this book and the techniques described in it are likely to become classics in the field."
(L. F. abbott, Volan Center for Complex Systems, Brandeis University)" Spikes is an ode to the single spike, where it points to, what information it carries and how reliable it is. It is quite pleasing to see innovative concepts from the sixties and seventies mature and being integrated into a powerful method to quantify nervous system action. The various digressions and inside physicist's puns makes this book a joy to read. Mandatory for neuroscientists not afraid of a quantitative approach and wanting to replace hand waving with numbers."
(Jos J. Eggermont, Alberta Heritage Medical Scientist; Professor, Department of Psychology, The University of Calgary)" Spikes is a joy to read and flows smoothly even in technical sections. The field of computational neuroscience desperately needs a book as well-written and thought out as this one."
(Joseph Atick, rockefeller University; Editor in Chief, Network: Computation In Neural Systems)"Every now and again there is a book that is written with equal respect for the English language, the reader, and its subject matter. Spikes: Exploring the Neural Code is a pleasure. It deals with a fundamental issue in neuroscience -- how information about the world is represented in sensory spike trains -- how information about the world is represented in sensory spike trains -- and does so with clarity for the neuroscientist and rigor for the computational community. This book should become a classic in computational neuroscience."
(Eve Marder, Victor and Gwendolyn Beinfield Professor of Neuroscience, Volen Center and Department of Biology, Brandeis University)" Spikes opens a valuable dialog about how neural codes are used as well as formed. It is wonderful to see such careful attention to the implications of individual spikes, both as elements of neural codes and as signals in their own right that have to be 'read out' to create perceptions. This is an excellent book that covers an important aspect of neuroscience and biophysics -- how the spike responses of neurons might be used to reconstruct information about the environment as well as organized to represent the stimulus. This is an exciting and rigorous book that explains with clarity and detail how widely-accepted neurophysiological results from several different sensory systems indicate that single spikes must count, and that response-rates and neuronal maps do not exhaust the properties needed to explain perception."
(James A. Simmons, Professor, Department of Neuroscience, Division of Biology and Medicine, Brown University)"This monograph by Bialek and his group is a milestone in the analysis of the quantitative relationship between the firing of nerve cells (in the brain) and the amount of information they transmit. Drawing upon a wealth of physiological experiments, it makes the point that individual action potentials can convey one or more bits of information about the stimulus, permanently changing our attitude about the brain as a fuzzy computational soup that can only act via activity in an incountable number of neurons. This book will become a classic."
(Christof Koch, Professor of Computation and Neural Systems, California Institute of Technology)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)Spikes is a really wonderful book. The particular theory about how the brain works that informs the presentation, and thus determines how neural coding is to be described, is clearly thought through and the arguments are attractively and intelligently presented.
(Charles F. Stevens, The Salk Institute) 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.
Most helpful customer reviews
7 of 8 people found the following review helpful.
The Neural Code (Variability & Meaning)
By Joseph J Grenier
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.
7 of 7 people found the following review helpful.
Spikes - quantifying the neural code
By Will Wagstaff
The point of this review is to evaluate "Spikes: Exploring the Neural Code" from the perspective of an graduate student in computational neurobiology. Overall, this book provides informative and mathematical methods for making sense of spike trains in the brain. While this book may seem appealing to those familiar with the biology of the brain, it is more geared towards the engineer with a strong calculus and statistics background. The concepts should be graspable by the senior undergraduate or graduate student after some time spent computationally evaluating the neuronal models. I would highly recommend this book to any person with an interest in mathematically modeling spike train data of individual neurons.
Synopsis and Opinion:
The goal of this book is to "understand how the nervous system represents signals with realistic time dependencies" (12). Although a lofty and seemingly unattainable goal for a single textbook, Rieke et al. limit most of their evaluation to the spike train data of a single neuron. A single chapter is devoted to the issues associated with a small ensemble of neurons. In order to guide this discussion and properly frame the problem, Rieke appeals to the Bayes' mathematical formalism in the majority of the descriptions of spike train data. If any equations appear within the text without justification, supplementary material is provided in an appendix with formal proofs and discussion.
Chapter 1 is an informative introduction to the problem of neural coding: the ability of an ensemble of neurons to represent any stimulus. The authors set out to frame the problem in a way that is manageable, quantifiable, and justifiable under the limitations of a 300 page book. They appeal to the idea of a homunculus looking at the neuronal spikes, when referring to the task of deciphering the neural code. The question to answer is: how is this homunculus making sense of the data?
Chapter 2 is an extended and detailed look at both the mathematical fundaments of the probabilistic approach to decoding neural spike trains, and the early methods used in quantifying this data. The authors introduce probability theory and use this framework to explain how stimuli might be predicted given a time series of spike data, and also the reverse, how spike data might be predicted from a stimuli. They introduce and quantify the basic neural coding language: spike rates, interspike intervals, and neuronal correlations. Following this introduction, they detail and describe how neurons should perform under natural conditions and seek techniques that can be used to measure the parameters of these models. Many of the issues discussed deal with managing noise that arises in the data and extracting the principle components of the neural code.
Chapter 3 uses Shannon's information theory to try to quantify the amount of information that a neuron can represent. Information theory is presented to the reader and justified as a reasonable approach to solve this problem. This framework is then applied to real world experiments on synaptic vesicles and mammalian ganglion cells.
Chapter 4 seeks to quantify the reliability of the nervous system. This task consists of "comparing the reliability of perception to the reliability of individual neurons" (191). This essentially means predicting the behavior expected from a particular stimulus, and assessing whether the neurons actually fire a response that encodes for this behavior. The remainder of the chapter consists of several case studies that provide quantifiable measures of reliability, and qualify the difficulty of this task.
Chapter 5 provides a brief overview of some of the issues of neural coding in a population of neurons. Initially, the methods that can sample many neurons are presented (micro-electrode arrays), followed by a discussion of the statistics of natural scenes. The author later reflects of the models presented: "most progress to date has been made by studying a model world that is a simpler and less structured place than the real world, hoping that the optimal strategies for deal with this simple world will at least give us hints about optimal strategies for the real world" (268).
Style and Structure:
This book is educational and well written, and suitable for the mathematical neurobiologist. Rieke et al. formalize the question they are seeking to ask, develop a model to explore this question, provide the relevant mathematical background for the model, evaluate the model against several scenarios, and provide case studies describing other attempts at answering the question. The logical flow of each chapter is appropriate and thoughtful.
At certain points throughout chapters 3 and 4, the authors digress too far into the case studies without providing enough background for the reader to fully understand the point of the section.
Overall, "Spikes" effectively communicates complex topics for readers with a sufficient mathematical background. They structure of each chapter makes following the authors' arguments very straightforward and relevant to the questions posed in the introduction.
Discussion
The authors' decide to approach neural spike data from a purely mathematical approach. Although this brings a strong theoretical background to problem, much of the biology is lost in the process; the biological justification behind the mathematical simplifications are missing, which may cause the reader to question the relevance of the techniques presented. Dendrites, axons, neurotransmitters, etc. are left out of the models used within the book.
One major complaint is that, for a majority of the book, "Spikes" only looks at an ensemble of spike rates from a single neuron. This process suffers from "grandmother cell-ism" and thus cannot possibly capture the intricacies and extensive dynamics associated with a group of neuron firings. The brain uses populations of thousands of neurons to code for even the simplest of stimuli.
For Potential Readers:
This book will supplement any computational neuroscience course very well, although, be warned that the following prerequisite knowledge is necessary: calculus, physics, some linear algebra, rudimentary neuroscience, signal processing, probability and statistics, time/frequency domain analysis, linear systems. Students will benefit from the author's informative tone, detailed mathematical descriptions, and organized presentation. Professors and researchers could use this book as both a personal reference and teaching tool.
5 of 5 people found the following review helpful.
Taking the organism's point of view
By Coffeecoffeecoffee
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.
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