The Bayesian brain: the role of uncertainty in neural coding and computation

[1]  James M. Bower,et al.  Computation and Neural Systems , 2014, Springer US.

[2]  James M. Hillis,et al.  Slant from texture and disparity cues: optimal cue combination. , 2004, Journal of vision.

[3]  David C Knill,et al.  Visual Feedback Control of Hand Movements , 2004, The Journal of Neuroscience.

[4]  D. Burr,et al.  The Ventriloquist Effect Results from Near-Optimal Bimodal Integration , 2004, Current Biology.

[5]  Konrad Paul Kording,et al.  Bayesian integration in sensorimotor learning , 2004, Nature.

[6]  Miguel P Eckstein,et al.  An ideal observer with channels versus feature-independent processing of spatial frequency and orientation in visual search performance. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[7]  R. Zemel,et al.  Inference and computation with population codes. , 2003, Annual review of neuroscience.

[8]  J. Saunders,et al.  Do humans optimally integrate stereo and texture information for judgments of surface slant? , 2003, Vision Research.

[9]  J. Saunders,et al.  Humans use continuous visual feedback from the hand to control fast reaching movements , 2003, Experimental Brain Research.

[10]  Richard F Murray,et al.  Saccadic and perceptual performance in visual search tasks. II. Letter discrimination. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[11]  Michael S Landy,et al.  Statistical decision theory and the selection of rapid, goal-directed movements. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[12]  Wendy J Adams,et al.  Bayesian modeling of cue interaction: bistability in stereoscopic slant perception. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[13]  Robert A Jacobs,et al.  Bayesian integration of visual and auditory signals for spatial localization. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[14]  Peter E. Latham,et al.  Optimal computation with attractor networks , 2003, Journal of Physiology-Paris.

[15]  Miguel P Eckstein,et al.  Saccadic and perceptual performance in visual search tasks. I. Contrast detection and discrimination. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[16]  Tai Sing Lee,et al.  Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[17]  Wilson S. Geisler,et al.  A Bayesian approach to the evolution of perceptual and cognitive systems , 2003, Cogn. Sci..

[18]  David C Knill,et al.  Mixture models and the probabilistic structure of depth cues , 2003, Vision Research.

[19]  Richard N Aslin,et al.  Statistical learning of new visual feature combinations by infants , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Michael I. Jordan,et al.  Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.

[21]  Daniel M Wolpert,et al.  Role of uncertainty in sensorimotor control. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[22]  Edward H. Adelson,et al.  Motion illusions as optimal percepts , 2002, Nature Neuroscience.

[23]  R. Aslin,et al.  Statistical learning of higher-order temporal structure from visual shape sequences. , 2002, Journal of experimental psychology. Learning, memory, and cognition.

[24]  M. Ernst,et al.  Humans integrate visual and haptic information in a statistically optimal fashion , 2002, Nature.

[25]  J. Saunders,et al.  Perception of 3D surface orientation from skew symmetry , 2001, Vision Research.

[26]  William T. Freeman,et al.  Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology , 1999, Neural Computation.

[27]  Pascal Mamassian,et al.  Interaction of visual prior constraints , 2001, Vision Research.

[28]  John W. Clark,et al.  Neural Representation of Probabilistic Information , 2001, Neural Computation.

[29]  A. Pouget,et al.  Efficient computation and cue integration with noisy population codes , 2001, Nature Neuroscience.

[30]  J. Gold,et al.  Neural computations that underlie decisions about sensory stimuli , 2001, Trends in Cognitive Sciences.

[31]  Thomas J. Anastasio,et al.  Using Bayes' Rule to Model Multisensory Enhancement in the Superior Colliculus , 2000, Neural Computation.

[32]  R. Jacobs,et al.  Optimal integration of texture and motion cues to depth , 1999, Vision Research.

[33]  William T. Freeman,et al.  Learning low-level vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[34]  Michael L. Platt,et al.  Neural correlates of decision variables in parietal cortex , 1999, Nature.

[35]  R. J. van Beers,et al.  Integration of proprioceptive and visual position-information: An experimentally supported model. , 1999, Journal of neurophysiology.

[36]  D. Wolpert,et al.  Signal-dependent noise determines motor planning , 1998, Nature.

[37]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[38]  D. Knill,et al.  Discrimination of planar surface slant from texture: human and ideal observers compared , 1998, Vision Research.

[39]  Michael Isard,et al.  Statistical models of visual shape and motion , 1998, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[40]  Peter Dayan,et al.  Combining Probabilistic Population Codes , 1997, IJCAI.

[41]  T. Sanger,et al.  Probability density estimation for the interpretation of neural population codes. , 1996, Journal of neurophysiology.

[42]  James A. Crowell,et al.  Ideal observer for heading judgments , 1996, Vision Research.

[43]  Andrew Blake,et al.  Two-dimensional constraints on three-dimensional structure from motion tasks , 1995, Vision Research.

[44]  Michael I. Jordan,et al.  An internal model for sensorimotor integration. , 1995, Science.

[45]  David C. Knill,et al.  Object classification for human and ideal observers , 1995, Vision Research.

[46]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[47]  J. Movshon,et al.  The statistical reliability of signals in single neurons in cat and monkey visual cortex , 1983, Vision Research.

[48]  Chris Eliasmith,et al.  Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems , 2004, IEEE Transactions on Neural Networks.

[49]  Rajesh P. N. Rao Bayesian Computation in Recurrent Neural Circuits , 2004, Neural Computation.

[50]  M. Landy,et al.  Statistical decision theory and trade-offs in the control of motor response. , 2003, Spatial vision.

[51]  R. Zemel,et al.  Probabilistic Interpretation of Population Codes , 1996, NIPS.

[52]  P. Földiák,et al.  The ‘Ideal Homunculus’: Statistical Inference from Neural Population Responses , 1993 .

[53]  W. Geisler Sequential ideal-observer analysis of visual discriminations. , 1989, Psychological review.

[54]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[55]  R. Aslin,et al.  PSYCHOLOGICAL SCIENCE Research Article UNSUPERVISED STATISTICAL LEARNING OF HIGHER-ORDER SPATIAL STRUCTURES FROM VISUAL SCENES , 2022 .