"Probabilistic Models of the Brain: Perception and Neural Function" ed. by R.P.N. Rao, B.A. Olshausen, M.S. Lewicki

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"Probabilistic Models of the Brain: Perception and Neural Function" ed. by R.P.N. Rao, B.A. Olshausen, M.S. Lewicki

"Probabilistic Models of the Brain: Perception and Neural Function" ed. by Rajesh P.N. Rao, Bruno A. Olshausen, Michael S. Lewicki
Neural Information Processing Series
BB / MIT Press | 2002 | ISBN: 0585437122 9780585437125 | 335 pages | PDF | 3 MB

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.

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.

Part I: Perception
1 Bayesian Modelling of Visual Perception - Pascal Mamassian,Michael Landy, and Laurence T. Maloney
2 Vision, Psychophysics and Bayes - Paul Schrater and Daniel Kersten
3 Visual Cue Integration for Depth Perception - Robert 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 Information Theoretic Perspective on the Value of High-Level Information - A.L. Yuille and James M. Coughlan
8 Sparse Correlation Kernel Reconstruction and Superresolution - Constantine P. Papageorgiou, Federico Girosi, and Tomaso Poggio
Part II: Neural Function
9 Natural Image Statistics for Cortical OrientationMap Development - Christian Piepenbrock
10 Natural Image Statistics and Divisive Normalization - Martin J.Wainwright, Odelia Schwartz, and Eero P. Simoncelli
11 A Probabilistic NetworkModel of Population Responses - Richard S. Zemel and Jonathan Pillow
12 Efficient Coding of Time-Varying Signals Using a Spiking Population Code - Michael S. Lewicki
13 Sparse Codes and Spikes - Bruno A. Olshausen
14 Distributed 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 Restricted 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

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