Performance-optimized hierarchical models predict neural responses in higher visual cortex. - Test2 - elhamkhani - TriplyDB

Performance-optimized hierarchical models predict neural responses in higher visual cortex.

Performance-optimized hierarchical models predict neural responses in higher visual cortex.

http://www.wikidata.org/entity/Q33767681

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Invariant visual object recognition and shape processing in rats Deep supervised, but not unsupervised, models may explain IT cortical representation Semantic Wavelet-Induced Frequency-Tagging (SWIFT) Periodically Activates Category Selective Areas While Steadily Activating Early Visual Areas Category-Selectivity in Human Visual Cortex Follows Cortical Topology: A Grouped icEEG Study Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence How machine learning is shaping cognitive neuroimaging.Building Machines That Learn and Think Like People How Invariant Feature Selectivity Is Achieved in Cortex Neural representations of emotion are organized around abstract event features Image statistics underlying natural texture selectivity of neurons in macaque V4.A chicken model for studying the emergence of invariant object recognition.Effects of generalized pooling on binocular disparity selectivity of neurons in the early visual cortex Representation of Naturalistic Image Structure in the Primate Visual Cortex Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach.A distributed, hierarchical and recurrent framework for reward-based choice.Generic decoding of seen and imagined objects using hierarchical visual features.Function determines structure in complex neural networks.Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons.Deep neural networks rival the representation of primate IT cortex for core visual object recognition.Correlated activity supports efficient cortical processing Visual dictionaries as intermediate features in the human brain.Applying artificial vision models to human scene understanding.Unsupervised feature learning improves prediction of human brain activity in response to natural images.Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework.Deep Neural Networks as a Computational Model for Human Shape Sensitivity.Comparison of Object Recognition Behavior in Human and Monkey Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation.A top-down manner-based DCNN architecture for semantic image segmentation.Inferring learning rules from distributions of firing rates in cortical neurons.Head to toe, in the head Visual features as stepping stones toward semantics: Explaining object similarity in IT and perception with non-negative least squares Atoms of recognition in human and computer vision.Stimulus features coded by single neurons of a macaque body category selective patch.Selectivity and tolerance for visual texture in macaque V2.Typicality sharpens category representations in object-selective cortex Neurophysiological Organization of the Middle Face Patch in Macaque Inferior Temporal Cortex What the success of brain imaging implies about the neural code.Recurrent Network Dynamics; a Link between Form and Motion.Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models.

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Performance-optimized hierarchical models predict neural responses in higher visual cortex.

http://www.wikidata.org/entity/Q33767681

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2014 nî lūn-bûn

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2014 թուականի Մայիսին հրատարակուած գիտական յօդուած

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2014年の論文

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2014年論文

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2014年論文

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Performance-optimized hierarch ...... onses in higher visual cortex.

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Performance-optimized hierarch ...... onses in higher visual cortex.

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Proceedings of the National Academy of Sciences of the United States of America

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Performance-optimized hierarch ...... onses in higher visual cortex.

@en

P2093

Charles F Cadieu

http://www.w3.org/2001/XMLSchema#string

Daniel L K Yamins

http://www.w3.org/2001/XMLSchema#string

Darren Seibert

http://www.w3.org/2001/XMLSchema#string

Ethan A Solomon

http://www.w3.org/2001/XMLSchema#string

Ha Hong

http://www.w3.org/2001/XMLSchema#string

James J DiCarlo

http://www.w3.org/2001/XMLSchema#string

P2860

Why is real-world visual object recognition hard?

A high-throughput screening approach to discovering good forms of biologically inspired visual representation

Distributed hierarchical processing in the primate cerebral cortex

Do we know what the early visual system does?

Preface: Cerebral Cortex Has Come of Age

Visual Object Recognition

The lateral occipital complex and its role in object recognition.

Models of object recognition.

Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position.

Selectivity and tolerance ("invariance") both increase as visual information propagates from cortical area V4 to IT.

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262193403

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24812127

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