(MIT Press, 2002)
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ISBN 0-262-18224-6
Review by Josh McDermott (Nature Neurosci. 5(9), 829, 2002)
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, by C. 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, by D. 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