Spikes: Exploring the Neural Code (Computational Neuroscience) by Fred Rieke (PDF)

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.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.

User’s Reviews

Editorial Reviews: 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— Review 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. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐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.DiscussionThe 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.

⭐This book is well written. You won’t always find a book that is mostly math, where the writing is stellar and keeps you interested. But this definitely fits the bill. The math is at the level where a physics or advanced science or math level will suffice to read it. I would start with something less advanced if just starting out in higher level math so you get your chops up before delving in. Great book – highly recommend for the subject.

⭐This book appears to be oriented towards neurobiologists with an interest in the mathematical analysis of neural data, and also towards physicists and mathemeticians interested in the information processing by so-called “real” nervous systems. The authors have done a great job in quantifying various neural responses – these include representation of time-dependent signals, calculation of information rate and coding efficiency – and in understanding the reliability of the nervous system to represent answers to its computational problems. The study of neural coding is thus tied to the much broader issue of neural computation. The section on “Mathematical Asides” in the Appendix is particularly helpful in understanding the response of the nervous system. “Spikes” is well-written though somewhat non-inspiring. As a physicist specializing in non-linear processes, I expect the book to be helpful both for neuroscientists and physicists. — D.K.Bhadra, Advanced Spectral Research.

⭐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.

⭐It is good condition and help me to understand the its spikes mechanism .

⭐Extremely well written: informative and entertaining.

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