Three-stage processing of category and variation information by entangled interactive mechanisms of peri-occipital and peri-frontal cortices
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[1] J. Maunsell,et al. Attention improves performance primarily by reducing interneuronal correlations , 2009, Nature Neuroscience.
[2] Arnaud Delorme,et al. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.
[3] A. Norcia,et al. A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification , 2015, PloS one.
[4] Nicole C. Rust,et al. Selectivity and Tolerance (“Invariance”) Both Increase as Visual Information Propagates from Cortical Area V4 to IT , 2010, The Journal of Neuroscience.
[5] Martin N Hebart,et al. What Visual Information Is Processed in the Human Dorsal Stream? , 2012, The Journal of Neuroscience.
[6] Jessica K. Alexander,et al. Stress Increases Peripheral Axon Growth and Regeneration through Glucocorticoid Receptor-Dependent Transcriptional Programs , 2017, eNeuro.
[7] Xian Zhang,et al. Frontal-Occipital Connectivity During Visual Search , 2012, Brain Connect..
[8] Li Zhaoping,et al. Understanding Vision: Theory, Models, and Data , 2014 .
[9] Reza Ebrahimpour,et al. Feedforward object-vision models only tolerate small image variations compared to human , 2014, Front. Comput. Neurosci..
[10] M. Bar,et al. Magnocellular Projections as the Trigger of Top-Down Facilitation in Recognition , 2007, The Journal of Neuroscience.
[11] Majid Nili Ahmadabadi,et al. Temporal dynamics of visual category representation in the macaque inferior temporal cortex. , 2016, Journal of neurophysiology.
[12] Ankoor S. Shah,et al. Functional anatomy and interaction of fast and slow visual pathways in macaque monkeys. , 2007, Cerebral cortex.
[13] Dwight J. Kravitz,et al. The ventral visual pathway: an expanded neural framework for the processing of object quality , 2013, Trends in Cognitive Sciences.
[14] Dwight J. Kravitz,et al. High-level visual object representations are constrained by position. , 2010, Cerebral cortex.
[15] T. Poggio,et al. What and where: A Bayesian inference theory of attention , 2010, Vision Research.
[16] Thomas Serre,et al. Explaining the Timing of Natural Scene Understanding with a Computational Model of Perceptual Categorization , 2015, PLoS Comput. Biol..
[17] V. Lamme,et al. The distinct modes of vision offered by feedforward and recurrent processing , 2000, Trends in Neurosciences.
[18] Mohammad Bagher Menhaj,et al. Spatiotemporal analysis of category and target-related information processing in the brain during object detection , 2018, Behavioural Brain Research.
[19] Nancy Kanwisher,et al. The distribution of category and location information across object-selective regions in human visual cortex , 2008, Proceedings of the National Academy of Sciences.
[20] Jonathan D. Cohen,et al. Noise correlations in the human brain and their impact on pattern classification , 2017, PLoS Comput. Biol..
[21] John Duncan,et al. Dynamic Construction of a Coherent Attentional State in a Prefrontal Cell Population , 2013, Neuron.
[22] Christopher P. Said,et al. Top-down attention switches coupling between low-level and high-level areas of human visual cortex , 2012, Proceedings of the National Academy of Sciences.
[23] Ehud Zohary,et al. Position and Identity Information Available in fMRI Patterns of Activity in Human Visual Cortex , 2015, The Journal of Neuroscience.
[24] Marieke Mur,et al. Extracting Object Identity: Ventral or Dorsal Visual Stream? , 2016, The Journal of Neuroscience.
[25] Marlene Behrmann,et al. Retinotopic information interacts with category selectivity in human ventral cortex , 2016, Neuropsychologia.
[26] Yaoda Xu,et al. Goal-Directed Visual Processing Differentially Impacts Human Ventral and Dorsal Visual Representations , 2017, The Journal of Neuroscience.
[27] K. Zilles,et al. Differentiated parietal connectivity of frontal regions for “what” and “where” memory , 2012, Brain Structure and Function.
[28] E. Halgren,et al. Top-down facilitation of visual recognition. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[29] Victor A. F. Lamme,et al. Feedforward, horizontal, and feedback processing in the visual cortex , 1998, Current Opinion in Neurobiology.
[30] Antonio Torralba,et al. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence , 2016, Scientific Reports.
[31] T. Carlson,et al. High temporal resolution decoding of object position and category. , 2011, Journal of vision.
[32] Susan G. Wardle,et al. Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data , 2016, Journal of Cognitive Neuroscience.
[33] S. Thorpe,et al. The orbitofrontal cortex: Neuronal activity in the behaving monkey , 2004, Experimental Brain Research.
[34] Daeyeol Lee,et al. Effects of noise correlations on information encoding and decoding. , 2006, Journal of neurophysiology.
[35] Nasour Bagheri,et al. Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models , 2017, Scientific Reports.
[36] J. M. Hupé,et al. Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons , 1998, Nature.
[37] T. Poggio,et al. Neural mechanisms of object recognition , 2002, Current Opinion in Neurobiology.
[38] Nicolas Pinto,et al. Why is Real-World Visual Object Recognition Hard? , 2008, PLoS Comput. Biol..
[39] James J. DiCarlo,et al. How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.
[40] Manfred Fahle,et al. Ultra Rapid Object Categorization: Effects of Level, Animacy and Context , 2013, PloS one.
[41] Tim Curran,et al. The Limits of Feedforward Vision: Recurrent Processing Promotes Robust Object Recognition when Objects Are Degraded , 2012, Journal of Cognitive Neuroscience.
[42] M. Bar,et al. Cortical Mechanisms Specific to Explicit Visual Object Recognition , 2001, Neuron.
[43] Christoph Braun,et al. Feeling before knowing why: The role of the orbitofrontal cortex in intuitive judgments—an MEG study , 2014, Cognitive, affective & behavioral neuroscience.
[44] Margot J. Taylor,et al. N170 or N1? Spatiotemporal differences between object and face processing using ERPs. , 2004, Cerebral cortex.
[45] P. Milner. A model for visual shape recognition. , 1974, Psychological review.
[46] David C. Plaut,et al. ‘What’ Is Happening in the Dorsal Visual Pathway , 2016, Trends in Cognitive Sciences.
[47] Tomaso Poggio,et al. Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.
[48] Joel Z. Leibo,et al. The dynamics of invariant object recognition in the human visual system. , 2014, Journal of neurophysiology.
[49] Blake W. Johnson,et al. A high density ERP comparison of mental rotation and mental size transformation , 2003, Brain and Cognition.
[50] David D. Cox,et al. What response properties do individual neurons need to underlie position and clutter "invariant" object recognition? , 2009, Journal of neurophysiology.
[51] S. Thorpe,et al. Speed of processing in the human visual system , 1996, Nature.
[52] Daniel L. K. Yamins,et al. Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition , 2014, PLoS Comput. Biol..
[53] Andrea Vedaldi,et al. MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.
[54] Nikolaus Kriegeskorte,et al. Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation , 2014, PLoS Comput. Biol..
[55] Daniel L K Yamins,et al. Neural Mechanisms Underlying Visual Object Recognition. , 2014, Cold Spring Harbor symposia on quantitative biology.
[56] Ha Hong,et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.
[57] Thomas A. Carlson,et al. Representational dynamics of object recognition: Feedforward and feedback information flows , 2016, NeuroImage.
[58] A. Mognon,et al. ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. , 2011, Psychophysiology.
[59] D. J. Felleman,et al. Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.
[60] Christoph M. Michel,et al. The Neural Substrates and Timing of Top–Down Processes during Coarse-to-Fine Categorization of Visual Scenes: A Combined fMRI and ERP Study , 2010, Journal of Cognitive Neuroscience.
[61] Keiji Tanaka,et al. Inferotemporal cortex and object vision. , 1996, Annual review of neuroscience.
[62] D H Brainard,et al. The Psychophysics Toolbox. , 1997, Spatial vision.
[63] Sidney R. Lehky,et al. Frontiers in Computational Neuroscience Computational Neuroscience , 2022 .
[64] David D. Cox,et al. Untangling invariant object recognition , 2007, Trends in Cognitive Sciences.
[65] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[66] G. Ganis,et al. Top-down modulation of visual processing and knowledge after 250 ms supports object constancy of category decisions , 2015, Front. Psychol..
[67] Nasour Bagheri,et al. Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition , 2017, Neuroscience.
[68] Ha Hong,et al. Explicit information for category-orthogonal object properties increases along the ventral stream , 2016, Nature Neuroscience.
[69] J. Bullier. Integrated model of visual processing , 2001, Brain Research Reviews.
[70] T. Moore,et al. CONTROL OF VISUAL CORTICAL SIGNALS BY PREFRONTAL DOPAMINE , 2011, Nature.
[71] David A. Tovar,et al. Representational dynamics of object vision: the first 1000 ms. , 2013, Journal of vision.
[72] Su Keun Jeong,et al. Behaviorally Relevant Abstract Object Identity Representation in the Human Parietal Cortex , 2016, The Journal of Neuroscience.
[73] Nikolaus Kriegeskorte,et al. Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .
[74] G. Kreiman,et al. Timing, Timing, Timing: Fast Decoding of Object Information from Intracranial Field Potentials in Human Visual Cortex , 2009, Neuron.
[75] Nasour Bagheri,et al. Average activity, but not variability, is the dominant factor in the representation of object categories in the brain , 2017, Neuroscience.
[76] Kalanit Grill-Spector,et al. Task alters category representations in prefrontal but not high-level visual cortex , 2017, NeuroImage.
[77] N. Sigala,et al. Dynamic Coding for Cognitive Control in Prefrontal Cortex , 2013, Neuron.
[78] N Apurva Ratan Murty,et al. A Balanced Comparison of Object Invariances in Monkey IT Neurons , 2017, eNeuro.