Probabilistic Models of the Brain: Perception and Neural Function

(MIT Press, 2002)

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ISBN 0-262-18224-6

Review by Josh McDermott (

## Chapter 1:

Bayesian Modelling of Visual Perception, by P. Mamassian, M. Landy and L. Maloney## Chapter 2:

Vision, Psychophysics, and Bayes, by P. Schrater and D. Kersten## Chapter 3:

Visual Cue Integration for Depth Perception, by R. Jacobs## Chapter 4:

Velocity Likelihoods in Biological and Machine Vision, by Y. Weiss and D. Fleet## Chapter 5:

Learning Motion Analysis, by W. Freeman, J. Haddon and E. Pasztor## Chapter 6:

Information Theoretic Approach to Neural Coding and Parameter Estimation: A Perspective,by J.-P. Nadal## Chapter 7:

From Generic to Specific: An Information Theoretic Perspective on the Value of High-Level Information, by A. Yuille and J. Coughlan## Chapter 8:

Sparse Correlation Kernel Reconstruction and Superresolution, byC. Papageorgiou, F. Girosi and T. Poggio

## Chapter 9:

Natural Image Statistics for Cortical Orientation Map Development, by C. Piepenbrock## Chapter 10:

Natural Image Statistics and Divisive Normalization: Modeling Nonlinearities and Adaptation in Cortical Neurons, by M. J. Wainwright, O. Schwartz, and E. P. Simoncelli## Chapter 11:

A Probabilistic Network Model of Population Responses, by R. S. Zemel and J. Pillow## Chapter 12:

Efficient Coding of Time-Varying Signals Using a Spiking Population Code, by M. Lewicki## Chapter 13:

Sparse Codes and Spikes, by B. Olshausen## Chapter 14:

Distributed Synchrony: A Probabilistic Model of Neural Signaling, byD. Ballard, Z. Zhang, and R. Rao## Chapter 15:

Learning to use Spike Timing in a Restricted Boltzmann Machine, by G. E. Hinton and A. D. Brown## Chapter 16:

Predictive Coding, Cortical Feedback, and Spike-Timing Dependent Plasticity, by R. Rao and T. Sejnowski

Rajesh P. N. Rao

Dept. of Computer Science and Engineering

University of Washington

Seattle, WA 98195-2350

FAX: 206-543-2969

WWW: http://cs.washington.edu/homes/rao/

e-mail: rao[at]cs[dot]washington[dot]edu