Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
about
Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach.DeepMap+: Recognizing High-Level Indoor Semantics Using Virtual Features and Samples Based on a Multi-Length Window Framework.Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons.A data driven approach to understanding the organization of high-level visual cortex.Computational approaches to fMRI analysisRepresentation of Semantic Similarity in the Left Intraparietal Sulcus: Functional Magnetic Resonance Imaging Evidence.Making Sense of Real-World Scenes.Magnetoencephalography for brain electrophysiology and imaging.Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.Modeling the Dynamics of Human Brain Activity with Recurrent Neural Networks.Perception Science in the Age of Deep Neural Networks.Adaptation in the visual cortex: a case for probing neuronal populations with natural stimuli.ECG data compression using a neural network model based on multi-objective optimization.Stereo viewing modulates three-dimensional shape processing during object recognition: A high-density ERP study.Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments.Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models.Method for enhancing single-trial P300 detection by introducing the complexity degree of image information in rapid serial visual presentation tasks.Visual perception affected by motivation and alertness controlled by a noninvasive brain-computer interface.Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision.Visual properties and memorising scenes: Effects of image-space sparseness and uniformity.Macroscopic and microscopic spectral properties of brain networks during local and global synchronization.Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.Visual Features in the Perception of Liquids.Using human brain activity to guide machine learning.Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior.Cortical Statistical Correlation Tomography of EEG Resting State Networks.Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction.Information spreading by a combination of MEG source estimation and multivariate pattern classification.Computational mechanisms underlying cortical responses to the affordance properties of visual scenes.Ultra-Rapid serial visual presentation reveals dynamics of feedforward and feedback processes in the ventral visual pathway.Constraint-Free Natural Image Reconstruction From fMRI Signals Based on Convolutional Neural Network.Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway.Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortexA Comprehensive Review of Magnetoencephalography (MEG) Studies for Brain Functionality in Healthy Aging and Alzheimer's Disease (AD)Three-stage processing of category and variation information by entangled interactive mechanisms of peri-occipital and peri-frontal corticesConnecting Deep Neural Networks to Physical, Perceptual, and Electrophysiological Auditory SignalsSpatial frequency supports the emergence of categorical representations in visual cortex during natural scene perceptionDeep convolutional networks do not classify based on global object shape
P2860
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P2860
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
description
2016 nî lūn-bûn
@nan
2016 թուականին հրատարակուած գիտական յօդուած
@hyw
2016 թվականին հրատարակված գիտական հոդված
@hy
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
name
Comparison of deep neural netw ...... ls hierarchical correspondence
@ast
Comparison of deep neural netw ...... ls hierarchical correspondence
@en
Comparison of deep neural netw ...... ls hierarchical correspondence
@nl
type
label
Comparison of deep neural netw ...... ls hierarchical correspondence
@ast
Comparison of deep neural netw ...... ls hierarchical correspondence
@en
Comparison of deep neural netw ...... ls hierarchical correspondence
@nl
prefLabel
Comparison of deep neural netw ...... ls hierarchical correspondence
@ast
Comparison of deep neural netw ...... ls hierarchical correspondence
@en
Comparison of deep neural netw ...... ls hierarchical correspondence
@nl
P2093
P2860
P921
P3181
P356
P1433
P1476
Comparison of deep neural netw ...... ls hierarchical correspondence
@en
P2093
Aditya Khosla
Antonio Torralba
Aude Oliva
Dimitrios Pantazis
P2860
P2888
P3181
P356
10.1038/SREP27755
P407
P577
2016-06-10T00:00:00Z
P6179
1036702724