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Title Probabilistic models of the brain : perception and neural function / edited by Rajesh P.N. Rao, Bruno A. Olshausen, Michael S. Lewicki.
Imprint Cambridge, Mass. : MIT Press, ©2002.


 Internet  Electronic Book    AVAILABLE
Description 1 online resource (x, 324 pages) : illustrations.
Series Neural information processing series
Neural information processing series.
Note "A Bradford book."
Bibliog. Includes bibliographical references and index.
Note Available only to authorized UTEP users.
Print version record.
Subject Brain -- Mathematical models.
Neurology -- Statistical methods.
Brain Mapping -- methods -- Congresses.
Models, Neurological -- Congresses.
Models, Statistical -- Congresses.
Neurons -- physiology -- Congresses.
Visual Perception -- physiology -- Congresses.
Genre Electronic books.
Congressen (vorm)
Contents I. Perception -- 1. Bayesian modeling of visula perception / Pascal Marnassian, Michael Landy, and Laurence T. Maloney -- 2. Vision, psychophysics and Bayes / Paul Schrater and Daniel Kersten -- 3. Visual cue integration for depth perception / Rober A. Jacobs -- 4. Velocity likelihoods in biological and machine vision / Yair Weiss and David J. Fleet -- 5. Learning motion analysis / William Freeman, John Haddon, and Egon Pasztor -- 6. Information theoretic approach to neural coding and parameter estimation: a perspective / Jean-Pierre Nadal -- 7. From generic to specific: an informaiton theoretic perspective on the value of high-level information / A.L. Yuille and James M. Coughlan -- 8. Sparse correlation kernal reconstruction and superresolution / Constantine P. Papageorgiou, Federico Girosi, and Tomaso Poggio -- II. Neural function -- 9. Natural image statistics for cortical orientation map development -- 10. Natural image statistics and divisive normalization / Martin J. Wainwright, Odelia Schwartz, and Eero P. Simoncelli -- 11. Probabilistic network model of population responses / Richard S. Zemel and Jonathan Pillow -- 12. Efficient coding of time-varing signals using a spiking population code / Michael S. Lewicki -- 13. Sparse codes and spikes / Bruno A. Olshausen -- 14. Distibuted synchrony: a probabilistic model of neural signaling / Dana H. Ballard, Zuohua Zhang, and Rajesh P.N. Rao -- 15. Learning to use spike timing in a resticted Boltzmann machine / Geoffrey E. Hinton and Andrew D. Brown -- 16. Predictive coding, cortical feedback, and spike-timing dependent plasticity / Rajesh P.N. Rao and Terrence J. Sejnowski.
Summary 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.
Other Author Rao, Rajesh P. N.
Olshausen, Bruno A.
Lewicki, Michael S.
Other Title Print version: Probabilistic models of the brain. Cambridge, Mass. : MIT Press, ©2002 0262182246