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Computational Neuroscience Ser.: Spikes : Exploring the Neural Code by William Bialek, David Warland, Fred Rieke and Rob De Ruyter Van Steveninck (1999, Trade Paperback)
Orion Tech LLC (248593)
99.5% positive feedback
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About this product
Product Identifiers
PublisherMIT Press
ISBN-100262681080
ISBN-139780262681087
eBay Product ID (ePID)69423
Product Key Features
Number of Pages414 Pages
Publication NameSpikes : Exploring the Neural Code
LanguageEnglish
Publication Year1999
SubjectNeurology, Neuroscience
FeaturesReprint
TypeTextbook
AuthorWilliam Bialek, David Warland, Fred Rieke, Rob De Ruyter Van Steveninck
Subject AreaMedical
SeriesComputational Neuroscience Ser.
FormatTrade Paperback
Dimensions
Item Height0.8 in
Item Weight22.8 Oz
Item Length9 in
Item Width7 in
Additional Product Features
Intended AudienceTrade
LCCN95-046161
Reviews"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, 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.
Dewey Edition21
Grade FromCollege Graduate Student
IllustratedYes
Dewey Decimal612.8
Edition DescriptionReprint
SynopsisWhat 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., 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.
LC Classification NumberQP364.5.S66 1999
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