Bayesian inference with probabilistic population codes

Cited by (1,001)

Items 101–200 of 1,001

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5. Grid cells generate an analog error-correcting code for singularly precise neural computation
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6. Theory of cortical function
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7. Sensory uncertainty decoded from visual cortex predicts behavior
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9. Bayesian models: the structure of the world, uncertainty, behavior, and the brain
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11. Computational Grounded Cognition: a new alliance between grounded cognition and computational modeling
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12. There is no Such Thing as Attention
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13. Orbitofrontal Cortex Is Required for Optimal Waiting Based on Decision Confidence
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14. Multisensory Integration in Macaque Visual Cortex Depends on Cue Reliability
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15. Perceptual Decision-Making as Probabilistic Inference by Neural Sampling
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16. Uncertainty of feedback and state estimation determines the speed of motor adaptation
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17. Neural-like computing with populations of superparamagnetic basis functions
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18. Causal Inference in the Multisensory Brain
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19. Efficient Sensory Encoding and Bayesian Inference with Heterogeneous Neural Populations
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20. Reconstructions of Information in Visual Spatial Working Memory Degrade with Memory Load
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21. Brain mechanisms for perceptual and reward-related decision-making
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22. Optimal reward harvesting in complex perceptual environments
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23. How the Bayesians got their beliefs (and what those beliefs actually are): Comment on Bowers and Davis (2012).
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24. The Contribution of Single Synapses to Sensory Representation in Vivo
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25. Spontaneous Brain Oscillations and Perceptual Decision-Making
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26. Correlation detection as a general mechanism for multisensory integration
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27. Twenty-Five Lessons from Computational Neuromodulation
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28. Understanding variability in the BOLD signal and why it matters for aging
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29. Decision Making Under Uncertainty: A Neural Model Based on Partially Observable Markov Decision Processes
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30. Bistability and Non-Gaussian Fluctuations in Spontaneous Cortical Activity
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31. Lip-Reading Aids Word Recognition Most in Moderate Noise: A Bayesian Explanation Using High-Dimensional Feature Space
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32. Adaptive Gain Control during Human Perceptual Choice
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33. Inference in the age of big data: Future perspectives on neuroscience
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34. Still at the Choice‐Point: Action Selection and Initiation in Instrumental Conditioning
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35. Decision Making: The Neuroethological Turn
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36. Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex
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37. Prediction during language comprehension: what is next?
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38. Bayesian sampling in visual perception
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39. Goal-directed decision making as probabilistic inference: A computational framework and potential neural correlates.
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40. Spikes not slots: noise in neural populations limits working memory
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41. Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality
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42. The Future of Educational Neuroscience
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43. Evidence and a computational explanation of cultural differences in facial expression recognition.
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44. High-precision coding in visual cortex
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45. Attention Improves Encoding of Task-Relevant Features in the Human Visual Cortex
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46. Modelling trial-by-trial changes in the mismatch negativity
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47. Exemplar models as a mechanism for performing Bayesian inference
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48. Organizing probabilistic models of perception
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49. Differential Representations of Prior and Likelihood Uncertainty in the Human Brain
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50. Bayes in the Brain—On Bayesian Modelling in Neuroscience
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51. Theoretical Neuroscience Rising
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52. The Mixed Instrumental Controller: Using Value of Information to Combine Habitual Choice and Mental Simulation
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53. Neural processing as causal inference
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54. Origin of information-limiting noise correlations
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55. Decoding of MSTd Population Activity Accounts for Variations in the Precision of Heading Perception
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56. Pharmacological Fingerprints of Contextual Uncertainty
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57. Understanding Vision
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58. A computational perspective on autism
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59. Visual-vestibular cue integration for heading perception: applications of optimal cue integration theory
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60. Perceptual decision making: drift-diffusion model is equivalent to a Bayesian model
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61. Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)
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62. Confidence Leak in Perceptual Decision Making
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63. Choice, difficulty, and confidence in the brain
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64. Artificial Cognitive Systems: From VLSI Networks of Spiking Neurons to Neuromorphic Cognition
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65. Neural representations of confidence emerge from the process of decision formation during perceptual choices
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66. Similarity Effect and Optimal Control of Multiple-Choice Decision Making
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67. Waves of prediction
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68. Ideal-Observer Models of Cue Integration
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69. Owl’s behavior and neural representation predicted by Bayesian inference
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70. Brain networks for confidence weighting and hierarchical inference during probabilistic learning
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71. Probabilistic Representation in Human Visual Cortex Reflects Uncertainty in Serial Decisions
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72. Human Inferences about Sequences: A Minimal Transition Probability Model
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73. Optimal Sensorimotor Integration in Recurrent Cortical Networks: A Neural Implementation of Kalman Filters
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74. A neural basis of probabilistic computation in visual cortex
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75. Bayesian causal inference: A unifying neuroscience theory
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76. On the Origins of Suboptimality in Human Probabilistic Inference
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77. Perceptual Inference, Learning, and Attention in a Multisensory World
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78. Optimal multisensory decision-making in a reaction-time task
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79. Towards a neuronal gauge theory
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80. Marginalization in Neural Circuits with Divisive Normalization
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81. The global workspace needs metacognition
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82. How Can Single Sensory Neurons Predict Behavior?
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83. Cortical gamma oscillations: the functional key is activation, not cognition
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84. Heuristic use of perceptual evidence leads to dissociation between performance and metacognitive sensitivity
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85. Sensory reliability shapes perceptual inference via two mechanisms
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86. Cell Groups Reveal Structure of Stimulus Space
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87. Causal Links between Dorsal Medial Superior Temporal Area Neurons and Multisensory Heading Perception
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88. Neurobiology of Decision Making
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89. Metacognition in Multisensory Perception
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90. Tactile remapping: from coordinate transformation to integration in sensorimotor processing
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91. Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
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92. How Many Arms Make a Pair? Perceptual Illusion of Having an Additional Limb
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93. Tool use and the distalization of the end-effector
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94. Estimating the influence of attention on population codes in human visual cortex using voxel-based tuning functions
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95. Population-Level Neural Codes Are Robust to Single-Neuron Variability from a Multidimensional Coding Perspective
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96. Brain-wide representations of prior information in mouse decision-making
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97. Cue combination in human spatial navigation
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98. Rapidly learned stimulus expectations alter perception of motion
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99. There are things that we know that we know, and there are things that we do not know we do not know: Confidence in decision-making
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100. Perceptual learning as improved probabilistic inference in early sensory areas
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