Probabilistic models of the brain perception and neural function pdf

Rao , Bruno A. Olshausen and Michael S. A Bradford Book. 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. Olshausen and Michael S. Jordan and Sara A. Solla PDF Perception PDF Maloney PDF Jacobs PDF Fleet PDF Yuille and James M. Coughlan PDF Neural Function PDF Wainwright, Odelia Schwartz, and Eero P. Simoncelli PDF Lewicki PDF Sparse Codes and Spikes Bruno A.

The goal of the workshop was to bring together researchers interested in exploring the use of well-defined statistical principles in understanding cortical structure and function. This book contains chapters written by many of the speakers from the NIPS workshop, as well as invited contributions from other leading researchers in the field. The topics include probabilistic and information theoretic models of perception, 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.

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