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lgli/B_Biology/Rao, Olshausen, Lewicki. (eds.) Probabilistic models of the brain.. Perception and neural function (MIT, 2002)(ISBN 0262182246)(335s)_B_.pdf
Probabilistic Models of the Brain: Perception and Neural Function (Neural Information Processing) 🔍
Rajesh P. N. Rao, Bruno A. Olshausen, Michael S. Lewicki The MIT Press, Neural Information Processing, illustrated edition, 2002
英语 [en] · PDF · 3.4MB · 2002 · 📘 非小说类图书 · 🚀/lgli/lgrs/nexusstc/zlib · Save
描述
Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light on how the brain transforms raw sensory information into a form that is useful for goal-directed behavior. A fundamental question that is seldom addressed by these studies, however, is why the brain uses the types of representations it does and what evolutionary advantage, if any, these representations confer. It is difficult to address such questions directly via animal experiments. A promising alternative is to use probabilistic principles such as maximum likelihood and Bayesian inference to derive models of brain function. This book surveys some of the current probabilistic approaches to modeling and understanding brain function. Although most of the examples focus on vision, many of the models and techniques are applicable to other modalities as well. The book presents top-down computational models as well as bottom-up neurally motivated models of brain function. The topics covered include Bayesian and information-theoretic models of perception, probabilistic theories of neural coding and spike timing, computational models of lateral and cortico-cortical feedback connections, and the development of receptive field properties from natural signals.
备用文件名
lgrsnf/B_Biology/Rao, Olshausen, Lewicki. (eds.) Probabilistic models of the brain.. Perception and neural function (MIT, 2002)(ISBN 0262182246)(335s)_B_.pdf
备用文件名
nexusstc/Probabilistic models of the brain: Perception and neural function/0b2478a0f7bec169e30a8b3d23db7283.pdf
备用文件名
zlib/Medicine/Rajesh P. N. Rao, Bruno A. Olshausen, Michael S. Lewicki/Probabilistic models of the brain: Perception and neural function_502752.pdf
备选作者
Rajesh P.N. Rao; Bruno A. Olshausen; Michael S. Lewicki; Paul Schrater
备用出版商
Ebsco Publishing
备用出版商
A Bradford Book
备用出版商
Bradford Books
备用出版商
AAAI Press
备用版本
Neural information processing series, Cambridge, Massachusetts, 2002
备用版本
United States, United States of America
备用版本
MIT Press, Cambridge, Mass, 2002
备用版本
New Edition, 2002
备用版本
February 15, 2002
元数据中的注释
Kolxo3 -- 25
元数据中的注释
lg70887
元数据中的注释
{"isbns":["0262182246","0585437122","9780262182249","9780585437125"],"last_page":335,"publisher":"The MIT Press","series":"Neural Information Processing"}
备用描述
A survey of probabilistic approaches to modeling and understanding brain function.Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light on how the brain transforms raw sensory information into a form that is useful for goal-directed behavior. A fundamental question that is seldom addressed by these studies, however, is why the brain uses the types of representations it does and what evolutionary advantage, if any, these representations confer. It is difficult to address such questions directly via animal experiments. A promising alternative is to use probabilistic principles such as maximum likelihood and Bayesian inference to derive models of brain function.This book surveys some of the current probabilistic approaches to modeling and understanding brain function. Although most of the examples focus on vision, many of the models and techniques are applicable to other modalities as well. The book presents top-down computational models as well as bottom-up neurally motivated models of brain function. The topics covered include Bayesian and information-theoretic models of perception, probabilistic theories of neural coding and spike timing, computational models of lateral and cortico-cortical feedback connections, and the development of receptive field properties from natural signals.
备用描述
<p>Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light on how the brain transforms raw sensory information into a form that is useful for goal-directed behavior. A fundamental question that is seldom addressed by these studies, however,is why the brain uses the types of representations it does and what evolutionary advantage, if any,these representations confer. It is difficult to address such questions directly via animal experiments. A promising alternative is to use probabilistic principles such as maximum likelihood and Bayesian inference to derive models of brain function.This book surveys some of the current probabilistic approaches to modeling and understanding brain function. Although most of the examples focus on vision, many of the models and techniques are applicable to other modalities as well. The book presents top-down computational models as well as bottom-up neurally motivated models of brain function. The topics covered include Bayesian and information-theoretic models of perception,probabilistic theories of neural coding and spike timing, computational models of lateral and cortico-cortical feedback connections, and the development of receptive field properties from natural signals.</p>
备用描述
Each waking moment, our body's sensory receptors convey a vast amount of information about the surrounding environment to the brain.
开源日期
2009-07-20
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