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Feb 2002
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ISBN
0262182246
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| 334 pp.
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| Probabilistic Models of the Brain |
|
Rajesh P. N. Rao
,
Bruno A. Olshausen
and
Michael S. Lewicki
|
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.
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| Table of Contents |
| | Series Foreword
by Michael I. Jordan and Sara A. Solla |
| | Preface |
| | Introduction |
| I | | Perception |
| 1 | | Bayesian Modeling of Visula Perception
by Pascal Marnassian, Michael Landy, and Laurence T. Maloney |
| 2 | | Vision, Psychophysics and Bayes
by Paul Schrater and Daniel Kersten |
| 3 | | Visual Cue Integration for Depth Perception
by Rober A. Jacobs |
| 4 | | Velocity Likelihoods in Bilogical and Machine Vision
by Yair Weiss and David J. Fleet |
| 5 | | Learning Motion Analysis
by William Freeman, John Haddon, and Egon Pasztor |
| 6 | | Information Theoretic Approach to Neural Coding and
Parameter Estimation: A Perspective
by Jean-Pierre Nadal |
| 7 | | From Generic to Specific: An Informaiton Theoretic
Perspective on the Value of High-Level Information
by A. L. Yuille and James M. Coughlan |
| 8 | | Sparse Correlation Kernal Reconstruction and Superresolution
by 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
by Martin J. Wainwright, Odelia Schwartz, and Eero
P. Simoncelli |
| 11 | | A Probabilistic Network Model of Populatin Responses
by Richard S. Zemel and Jonathan Pillow |
| 12 | | Efficient Coding of Time-Varing Signals Using a
Spiking Population Code
by Michael S. Lewicki |
| 13 | | Sparse Codes and Spikes
by Bruno A. Olshausen |
| 14 | | Distibuted Synchrony: A Probabilistic Model of
Neural Signaling
by Dana H. Ballard, Zuohua Zhang, and Rajesh P. N. Rao |
| 15 | | Learning to Use Spike Timing in a Resticted
Boltzmann Machine
by Geoffrey E. Hinton and Andrew D. Brown |
| 16 | | Predictive Coding, Cortical Feedback, and
Spike-Timing Dependent Plasticity
by Rajesh P. N. Rao and Terrence J. Sejnowski |
| | Contributors |
| | Index |
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