9780262182249

Probabilistic Models of the Brain : Perception and Neural Function

by
  • ISBN13:

    9780262182249

  • ISBN10:

    0262182246

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2002-02-01
  • Publisher: MIT PRESS
  • Purchase Benefits
  • Free Shipping On Orders Over $35!
    Your order must be $35 or more to qualify for free economy shipping. Bulk sales, PO's, Marketplace items, eBooks and apparel do not qualify for this offer.
  • Get Rewarded for Ordering Your Textbooks! Enroll Now
List Price: $58.00 Save up to $1.74
  • Buy New
    $56.26
    Add to Cart Free Shipping

    SPECIAL ORDER: 1-2 WEEKS

Supplemental Materials

What is included with this book?

  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

Summary

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
13(24)
Pascal Mamassian
Michael Landy
Laurence T. Maloney
Vision, Pyschophysics and Bayes
37(24)
Paul Schrater
Daniel Kersten
Visual Cue Integration for Depth Perception
61(16)
Robert A. Jacobs
Velocity Likelihoods in Biological and Machine Vision
77(20)
Yair Weiss
David J. Fleet
Learning Motion Analysis
97(20)
William Freeman
John Haddon
Egon Pasztor
Information Theoretic Approach to Neural Coding and Parameter Estimation: A Perspective
117(18)
Jean-Pierre Nadal
From Generic to Specific: An Information Theoretic Perspective on the Value of High-Level Information
135(20)
A.L. Yuille
James M. Coughlan
Sparse Correlation Kernel Reconstruction and Superresolution
155(26)
Constantine P. Papageorgiou
Federico Girosi
Tomaso Poggio
Part II: Neural Function
Natural Image Statistics for Crtical Orientation Map Development
181(22)
Christian Piepenbrock
Natural Image Statistics and Divisive Normalization
203(20)
Martin J. Wainwright
Odelia Schwartz
Eero P. Simoncelli
A Probabilistic Network Model of Population Responses
223(20)
Richard S. Zemel
Jonathan Pillow
Efficient Coding of Time-Varying Signals Using a Spiking Population Code
243(14)
Michael S. Lewicki
Sparse Codes and Spikes
257(16)
Bruno A. Olshausen
Distributed Synchrony: A Probabilistic Model of Neural Signaling
273(12)
Dana H. Ballard
Zuohua Zhang
Rajesh P. N. Rao
Learning to Use Spike timing in a Restricted Boltzmann Machine
285(12)
Geoffrey E. Hinton
Andrew D. Brown
Predictive Coding, Cortical Feedback, and Spike-Timing Dependent Plasticity
297(20)
Rajesh P. N. Rao
Terrence J. Sejnowski
Contributors 317(4)
Index 321

Rewards Program

Write a Review