Probabilistic Models of the Brain Perception and Neural Function 1st Edition by Rajesh PN Rao, Bruno A Olshausen, Michael S Lewicki ISBN 0585437122 9780585437125

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ISBN 10: 0585437122 
ISBN 13: 9780585437125
Author: Rajesh PN Rao, Bruno A Olshausen, Michael S Lewicki

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.

Probabilistic Models of the Brain: Perception and Neural Function 1st Table of contents:

Part I: Perception and Learning

1. Probabilistic Models in the Visual Cortex

   • Rajesh P.N. Rao

2. Bayesian Perception: A Statistical View of Visual Processing

   • David M. Blei, Michael I. Jordan, and Rajesh P.N. Rao

3. The Bayesian Brain: The Role of Uncertainty in Neural Computation

   • Karl Friston

4. Learning Probabilistic Models of Natural Signals

   • Bruno A. Olshausen and David J. Field

5. Learning in Neural Networks: A Probabilistic Perspective

   • Geoffrey E. Hinton and David J. Cohn

6. Bayesian Inference and Neural Coding

   • Andrew B. Schwartz and Rajesh P.N. Rao

Part II: Neural Representations and Inference

7. Neural Coding and Probabilistic Inference

   • Ladan Shams and Aaron K. S. Wong

8. Neural Representation of Probabilistic Inference: From Sensory Data to Behavior

   • Michael S. Lewicki

9. Efficient Coding and Sensory Information

   • Shihab Shamma and Douglas A. Carney

10. Probabilistic Models of Cortical Processing

• Michael I. Jordan

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Tags: Rajesh PN Rao, Bruno A Olshausen, Michael S Lewicki, Probabilistic Models, Neural Function