Probabilistic Models of the Brain

Perception and Neural Function
Overview

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

  1. Series Foreword

    Michael I. Jordan and Sara A. Solla

  2. Preface
  3. Introduction
  4. I. Perception
  5. 1. Bayesian Modeling of Visula Perception

    Pascal Marnassian, Michael Landy, and Laurence T. Maloney

  6. 2. Vision, Psychophysics and Bayes

    Paul Schrater and Daniel Kersten

  7. 3. Visual Cue Integration for Depth Perception

    Rober A. Jacobs

  8. 4. Velocity Likelihoods in Bilogical and Machine Vision

    Yair Weiss and David J. Fleet

  9. 5. Learning Motion Analysis

    William Freeman, John Haddon, and Egon Pasztor

  10. 6. Information Theoretic Approach to Neural Coding and Parameter Estimation: A Perspective

    Jean-Pierre Nadal

  11. 7. From Generic to Specific: An Informaiton Theoretic Perspective on the Value of High-Level Information

    A. L. Yuille and James M. Coughlan

  12. 8. Sparse Correlation Kernal Reconstruction and Superresolution

    Constantine P. Papageorgiou, Federico Girosi, and Tomaso Poggio

  13. II. Neural Function
  14. 9. Natural Image Statistics for Cortical Orientation Map Development

  15. 10. Natural Image Statistics and Divisive Normalization

    Martin J. Wainwright, Odelia Schwartz, and Eero P. Simoncelli

  16. 11. A Probabilistic Network Model of Populatin Responses

    Richard S. Zemel and Jonathan Pillow

  17. 12. Efficient Coding of Time-Varing Signals Using a Spiking Population Code

    Michael S. Lewicki

  18. 13. Sparse Codes and Spikes

    Bruno A. Olshausen

  19. 14. Distibuted Synchrony: A Probabilistic Model of Neural Signaling

    Dana H. Ballard, Zuohua Zhang, and Rajesh P. N. Rao

  20. 15. Learning to Use Spike Timing in a Resticted Boltzmann Machine

    Geoffrey E. Hinton and Andrew D. Brown

  21. 16. Predictive Coding, Cortical Feedback, and Spike-Timing Dependent Plasticity

    Rajesh P. N. Rao and Terrence J. Sejnowski

  22. Contributors
  23. Index