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Probabilistic Models of the Brain: Perception and Neural Function (Neural Information Processing)
 
 
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Probabilistic Models of the Brain: Perception and Neural Function (Neural Information Processing) [Hardcover]

Rajesh P. N. Rao (Author), Bruno A. Olshausen (Author), Michael S. Lewicki (Author)

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Book Description

0262182246 978-0262182249 February 15, 2002

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.


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About the Author

Bruno A. Olshausen is Associate Professor in the Department of Psychology and the Center for Neuroscience at the University of California, Davis.



Michael S. Lewicki is Assistant Professor in the Department of Computer Science and the Center for the Neural Basis of Cognition at Carnegie Mellon University.


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Inside This Book (learn more)
First Sentence:
Each waking moment, our body's sensory receptors convey a vast amount of information about the surrounding environment to the brain. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
pattern inference theory, visual cue integration, distractor paths, generative model likelihood, deeper cylinder, predictive coding model, cortical competition, temporal kernel, cortical orientation map, informative stimuli, recurrent weights, neural response properties, divisive normalization, ideal observer analysis, recurrent feedback connections, natural image statistics, conditional histogram, simple cell receptive fields, texture cue, correlation kernels, contrast response function, world angle, scene patches, viewing stimuli, receptive field profiles
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Neural Computation, Neural Information Processing Systems, Cambridge University Press, New York, Journal of Neurophysiology, Journal of Neuroscience, Artificial Intelligence Laboratory, International Conference, Nature Neuroscience, Visual Neuroscience, Academic Press, Fort Collins, Morgan Kaufmann, Pacific Grove, Ruyter van Steveninck, Sensory Communication
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