Probabilistic Models of the Brain : Perception and Neural Function

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  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2002-02-01
  • Publisher: MIT PRESS
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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.

Table of Contents

Preface ix
Introduction 1(12)
Part I: Perception
Bayesian Modelling of Visual Perception
Pascal Mamassian
Michael Landy
Laurence T. Maloney
Vision, Pyschophysics and Bayes
Paul Schrater
Daniel Kersten
Visual Cue Integration for Depth Perception
Robert A. Jacobs
Velocity Likelihoods in Biological and Machine Vision
Yair Weiss
David J. Fleet
Learning Motion Analysis
William Freeman
John Haddon
Egon Pasztor
Information Theoretic Approach to Neural Coding and Parameter Estimation: A Perspective
Jean-Pierre Nadal
From Generic to Specific: An Information Theoretic Perspective on the Value of High-Level Information
A.L. Yuille
James M. Coughlan
Sparse Correlation Kernel Reconstruction and Superresolution
Constantine P. Papageorgiou
Federico Girosi
Tomaso Poggio
Part II: Neural Function
Natural Image Statistics for Crtical Orientation Map Development
Christian Piepenbrock
Natural Image Statistics and Divisive Normalization
Martin J. Wainwright
Odelia Schwartz
Eero P. Simoncelli
A Probabilistic Network Model of Population Responses
Richard S. Zemel
Jonathan Pillow
Efficient Coding of Time-Varying Signals Using a Spiking Population Code
Michael S. Lewicki
Sparse Codes and Spikes
Bruno A. Olshausen
Distributed Synchrony: A Probabilistic Model of Neural Signaling
Dana H. Ballard
Zuohua Zhang
Rajesh P. N. Rao
Learning to Use Spike timing in a Restricted Boltzmann Machine
Geoffrey E. Hinton
Andrew D. Brown
Predictive Coding, Cortical Feedback, and Spike-Timing Dependent Plasticity
Rajesh P. N. Rao
Terrence J. Sejnowski
Contributors 317(4)
Index 321

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