Dynamics of visual motion processing : neuronal, behavioral, and computational approaches
著者
書誌事項
Dynamics of visual motion processing : neuronal, behavioral, and computational approaches
Springer, c2010
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
Motion processing is an essential piece of the complex brain machinery that allows us to reconstruct the 3D layout of objects in the environment, to break camouflage, to perform scene segmentation, to estimate the ego movement, and to control our action. Although motion perception and its neural basis have been a topic of intensive research and modeling the last two decades, recent experimental evidences have stressed the dynamical aspects of motion integration and segmentation. This book presents the most recent approaches that have changed our view of biological motion processing. These new experimental evidences call for new models emphasizing the collective dynamics of large population of neurons rather than the properties of separate individual filters. Chapters will stress how the dynamics of motion processing can be used as a general approach to understand the brain dynamics itself.
目次
Low-Level Cortical Dynamic Motion Processing.- From Moving Contours to Object Motion: Functional Networks for Visual Form/Motion Processing.- Temporal Dynamics of Motion Integration.- Dynamics of Pattern Motion Computation.- Multiscale Functional Imaging in V1 and Cortical Correlates of Apparent Motion.- Stimulus Localization by Neuronal Populations in Early Visual Cortex: Linking Functional Architecture to Perception.- Second-Order Motion Stimuli: A New Handle to Visual Motion Processing.- Active Vision, Pursuit and Motion Perception.- Motion Detection for Reflexive Tracking.- When the Brain Meets the Eye: Tracking Object Motion.- Interactions Between Perception and Smooth Pursuit Eye Movements.- Perception of Intra-saccadic Motion.- Intrasaccadic Motion: Neural Evidence for Saccadic Suppression and Postsaccadic Enhancement.- Modeling Dynamic Processing.- Maximizing Causal Information of Natural Scenes in Motion.- Neural Models of Motion Integration, Segmentation, and Probabilistic Decision-Making.- Features in the Recognition of Emotions from Dynamic Bodily Expression.
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