Normalization as a canonical neural computation

Cited by (1,001)

Items 801–900 of 1,001

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7. Contrast gain control and retinogeniculate communication
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10. Mach bands explained by response normalization
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12. Efficient Coding and Risky Choice
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18. Divisive normalization is an efficient code for multivariate Pareto-distributed environments
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19. Input and Output Gain Modulation by the Lateral Interhemispheric Network in Early Visual Cortex
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20. The Temporal Dynamics of Cortical Normalization Models of Decision-making
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21. Nonlinear dynamics of cortical responses to color in the human cVEP
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22. Development of Cross-Orientation Suppression and Size Tuning and the Role of Experience
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23. Cell Type Specific Representation of Vibro-tactile Stimuli in the Mouse Primary Somatosensory Cortex
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24. Normalization and pooling in hierarchical models of natural images
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25. The Poisson shot noise model of visual short-term memory and choice response time: Normalized coding by neural population size
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26. Feedback from retinal ganglion cells to the inner retina
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27. A performance-optimized model of neural responses across the ventral visual stream
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34. Divisively normalized neuronal processing of uncertain visual feedback for visuomotor learning
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35. Overcoming Catastrophic Interference in Connectionist Networks Using Gram-Schmidt Orthogonalization
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36. Broadband noise masks suppress neural responses to narrowband stimuli
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37. Syntax and the Cognitive Neuroscience of Syntactic Structure Building
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51. Division of Labor for Division: Inhibitory Interneurons with Different Spatial Landscapes in the Olfactory System
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52. Central auditory neurons display flexible feature recombination functions
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53. Optimized Tone Curve for In-Camera Image Processing
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54. Sparse identification of contrast gain control in the fruit fly photoreceptor and amacrine cell layer
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55. Pooling strategies in V1 can account for the functional and structural diversity across species
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56. A computational neuroscience framework for quantifying warning signals
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57. Responses to second-order texture modulations undergo surround suppression
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58. Predictive Ensemble Decoding of Acoustical Features Explains Context-Dependent Receptive Fields
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59. The search of “canonical” explanations for the cerebral cortex
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60. A reevaluation of Whittle (1986, 1992) reveals the link between detection thresholds, discrimination thresholds, and brightness perception
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61. Spatial Suppression and Sensitivity for Motion in Schizophrenia
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62. Mixture Coding and Segmentation in the Anterior Piriform Cortex
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63. Adaptive rewiring of random neural networks generates convergent–divergent units
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64. A dynamic neural field model of continuous input integration
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65. Range-Adaptive Value Representation in Different Stages of Schizophrenia: A Proof of Concept Study
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67. Learning Contextual Reward Expectations for Value Adaptation
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68. Context-dependent outcome encoding in human reinforcement learning
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69. Electrophysiological population dynamics reveal context dependencies during decision making in human frontal cortex
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72. Revisiting the high-dimensional geometry of population responses in the visual cortex
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73. Simulating Cortical Feedback Modulation as Changes in Excitation and Inhibition in a Cortical Circuit Model
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74. When the weaker conquer: A contrast-dependent illusion of visual numerosity
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75. A Model for Structured Information Representation in Neural Networks of the Brain
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76. Illusions, Delusions, and Your Backwards Bayesian Brain: A Biased Visual Perspective
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77. Cortical Divisive Normalization from Wilson–Cowan Neural Dynamics
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80. MULTIMODAL INTEGRATION OF MICRO-DOPPLER SONAR AND AUDITORY SIGNALS FOR BEHAVIOR CLASSIFICATION WITH CONVOLUTIONAL NETWORKS
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82. Effect of overlaid luminance contrast on perceived color contrast: Shadows enhance, borders suppress
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87. Recurrent competitive networks can learn locally excitatory topologies
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94. Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity
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95. Erratum: Normalization as a canonical neural computation
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96. Interneuron diversity and normalization specificity in a visual system
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