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Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Speed of processing in the human visual system
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INVARIANT FACE AND OBJECT RECOGNITION IN THE VISUAL SYSTEM
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The connection between regularization operators and support vector kernels
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How task-related are the responses of inferior temporal neurons?
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Regularization Networks and Support Vector Machines
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Hierarchical models of object recognition in cortex
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Learning representations by back-propagating errors
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Speed of processing in the human visual system
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Visual categorization shapes feature selectivity in the primate temporal cortex
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Neural population dynamics during reaching
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
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IMPAIRMENTS OF VISUAL OBJECT TRANSFORMS IN MONKEYS
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Receptive fields, binocular interaction and functional architecture in the cat's visual cortex
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Fast Readout of Object Identity from Macaque Inferior Temporal Cortex
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ImageNet classification with deep convolutional neural networks
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COMPLETE FUNCTIONAL CHARACTERIZATION OF SENSORY NEURONS BY SYSTEM IDENTIFICATION
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Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
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SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition
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Bayesian binning for maximising information rate of rapid serial presentation for sensory neurons
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Selectivity and Tolerance (“Invariance”) Both Increase as Visual Information Propagates from Cortical Area V4 to IT
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Stimulus-selective properties of inferior temporal neurons in the macaque
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Neuronal Responses to Object Images in the Macaque Inferotemporal Cortex at Different Stimulus Discrimination Levels
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Regularization Networks and Support Vector Machines
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Gaussian Processes for Machine Learning
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Neural network models and deep learning
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Explaining face representation in the primate brain using different computational models
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Shared texture-like representations underlie deep neural network alignment with human visual processing
inferred← citeshttps://doi.org/10.1016/j.ejrad.2019.02.038openalex-snapshot
The present and future of deep learning in radiology
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Modifying the one-hot encoding technique can enhance the adversarial robustness of the visual model for symbol recognition
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Individual tree detection and species classification of Amazonian palms using UAV images and deep learning
inferred← citeshttps://doi.org/10.1016/j.genrep.2020.100882openalex-snapshot
Classification of G protein-coupled receptors using attention mechanism
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Introducing the structural bases of typicality effects in deep learning
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What have we learned about infant visual attention in the first 25 years of the 21st century?
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Multimodal contrastive learning for brain–machine fusion: From brain-in-the-loop modeling to brain-out-of-the-loop application
inferred← citeshttps://doi.org/10.1016/j.isci.2021.102299openalex-snapshot
Collective motion as a distinct behavioral state of the individual
inferred← citeshttps://doi.org/10.1016/j.isci.2021.103013openalex-snapshot
Brain hierarchy score: Which deep neural networks are hierarchically brain-like?
inferred← citeshttps://doi.org/10.1016/j.isci.2022.104913openalex-snapshot
Deep learning models fail to capture the configural nature of human shape perception
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2020.12.010openalex-snapshot
Review on Convolutional Neural Networks (CNN) in vegetation remote sensing
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Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models
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High-level visual processing in the lateral geniculate nucleus revealed using goal-driven deep learning
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Combining inconsistent textures using convolutional neural networks
inferred← citeshttps://doi.org/10.1016/j.neubiorev.2017.01.012openalex-snapshot
Understanding natural scenes: Contributions of image statistics
inferred← citeshttps://doi.org/10.1016/j.neubiorev.2019.04.018openalex-snapshot
Advances in techniques for imposing reciprocity in brain-behavior relations
inferred← citeshttps://doi.org/10.1016/j.neucom.2015.09.116openalex-snapshot
Deep learning for visual understanding: A review
inferred← citeshttps://doi.org/10.1016/j.neucom.2016.02.060openalex-snapshot
A deep feature based framework for breast masses classification
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Topographic property of backpropagation artificial neural network: From human functional connectivity network to artificial neural network
inferred← citeshttps://doi.org/10.1016/j.neucom.2022.11.035openalex-snapshot
Functional network: A novel framework for interpretability of deep neural networks
inferred← citeshttps://doi.org/10.1016/j.neucom.2024.128587openalex-snapshot
The underlying mechanisms of alignment in error backpropagation through arbitrary weights
inferred← citeshttps://doi.org/10.1016/j.neucom.2025.132027openalex-snapshot
Neuro-inspired dynamic dual-processing framework for object detection
inferred← citeshttps://doi.org/10.1016/j.neucom.2025.132094openalex-snapshot
Investigating the effect of attention mechanisms on deep neural network engineering performance and brain-like properties
inferred← citeshttps://doi.org/10.1016/j.neunet.2016.12.001openalex-snapshot
An empirical model of activity in macaque inferior temporal cortex
inferred← citeshttps://doi.org/10.1016/j.neunet.2017.12.005openalex-snapshot
STDP-based spiking deep convolutional neural networks for object recognition
inferred← citeshttps://doi.org/10.1016/j.neunet.2018.09.004openalex-snapshot
A video-driven model of response statistics in the primate middle temporal area
inferred← citeshttps://doi.org/10.1016/j.neunet.2019.08.027openalex-snapshot
How are response properties in the middle temporal area related to inference on visual motion patterns?
inferred← citeshttps://doi.org/10.1016/j.neunet.2020.02.009openalex-snapshot
Causal importance of low-level feature selectivity for generalization in image recognition
inferred← citeshttps://doi.org/10.1016/j.neunet.2020.03.016openalex-snapshot
Learning in the machine: To share or not to share?
inferred← citeshttps://doi.org/10.1016/j.neunet.2020.03.021openalex-snapshot
Crowding in humans is unlike that in convolutional neural networks
inferred← citeshttps://doi.org/10.1016/j.neunet.2021.07.011openalex-snapshot
Bio-instantiated recurrent neural networks: Integrating neurobiology-based network topology in artificial networks
inferred← citeshttps://doi.org/10.1016/j.neunet.2021.09.018openalex-snapshot
Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research
inferred← citeshttps://doi.org/10.1016/j.neunet.2023.04.032openalex-snapshot
Contrast sensitivity function in deep networks
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A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations
inferred← citeshttps://doi.org/10.1016/j.neunet.2025.107395openalex-snapshot
Enabling scale and rotation invariance in convolutional neural networks with retina like transformation
inferred← citeshttps://doi.org/10.1016/j.neunet.2025.107834openalex-snapshot
Energy efficiency and sensitivity benefits in a motion processing adaptive recurrent neural network
inferred← citeshttps://doi.org/10.1016/j.neunet.2025.108222openalex-snapshot
Investigating fine- and coarse-grained structural correspondences between deep neural networks and human object image similarity judgments using unsupervised alignment
inferred← citeshttps://doi.org/10.1016/j.neures.2015.10.012openalex-snapshot
Modeling the motor cortex: Optimality, recurrent neural networks, and spatial dynamics
inferred← citeshttps://doi.org/10.1016/j.neurobiolaging.2021.05.021openalex-snapshot
Age-related dedifferentiation and hyperdifferentiation of perceptual and mnemonic representations
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2015.12.036openalex-snapshot
Increasingly complex representations of natural movies across the dorsal stream are shared between subjects
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2016.04.012openalex-snapshot
Typicality sharpens category representations in object-selective cortex
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2016.10.001openalex-snapshot
Seeing it all: Convolutional network layers map the function of the human visual system
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2017.08.016openalex-snapshot
Principles for models of neural information processing
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2018.05.037openalex-snapshot
The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2019.116118openalex-snapshot
Sensory processing and categorization in cortical and deep neural networks
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2022.119635openalex-snapshot
Understanding transformation tolerant visual object representations in the human brain and convolutional neural networks
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2022.119676openalex-snapshot
Keep the head in the right place: Face-body interactions in inferior temporal cortex
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2022.119754openalex-snapshot
A large and rich EEG dataset for modeling human visual object recognition
inferred← citeshttps://doi.org/10.1016/j.neuron.2019.08.034openalex-snapshot
Engineering a Less Artificial Intelligence
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Artificial Neural Networks for Neuroscientists: A Primer
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How learning unfolds in the brain: toward an optimization view
inferred← citeshttps://doi.org/10.1016/j.neuropsychologia.2015.10.023openalex-snapshot
Visual features as stepping stones toward semantics: Explaining object similarity in IT and perception with non-negative least squares
inferred← citeshttps://doi.org/10.1016/j.neuropsychologia.2017.06.010openalex-snapshot
On the partnership between neural representations of object categories and visual features in the ventral visual pathway
inferred← citeshttps://doi.org/10.1016/j.neuropsychologia.2021.108092openalex-snapshot
Skeletal representations of shape in the human visual cortex
inferred← citeshttps://doi.org/10.1016/j.neuropsychologia.2025.109329openalex-snapshot
Neural correlates of 3D versus 2D perception: An activation likelihood estimation meta-analysis
inferred← citeshttps://doi.org/10.1016/j.neuroscience.2017.02.050openalex-snapshot
Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition
inferred← citeshttps://doi.org/10.1016/j.patrec.2019.09.006openalex-snapshot
Exemplar based regular texture synthesis using LSTM
inferred← citeshttps://doi.org/10.1016/j.patrec.2019.10.013openalex-snapshot
Understanding trained CNNs by indexing neuron selectivity
inferred← citeshttps://doi.org/10.1016/j.patter.2021.100350openalex-snapshot
Unraveling neural coding of dynamic natural visual scenes via convolutional recurrent neural networks
inferred← citeshttps://doi.org/10.1016/j.patter.2024.101149openalex-snapshot
Unraveling the complexity of rat object vision requires a full convolutional network and beyond
inferred← citeshttps://doi.org/10.1016/j.patter.2025.101297openalex-snapshot
cytoGPNet: Enhancing clinical outcome prediction accuracy using longitudinal cytometry data in small cohort studies
inferred← citeshttps://doi.org/10.1016/j.patter.2025.101368openalex-snapshot
Neural mechanisms of visual quality perception and adaptability in the visual pathway
inferred← citeshttps://doi.org/10.1016/j.pediatrneurol.2023.01.004openalex-snapshot
Review of Machine Learning and Artificial Intelligence (ML/AI) for the Pediatric Neurologist
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Charlie Gross: An inspiration
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Dynamic emergence of relational structure network in human brains
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Motion Memory: Invariant representations of sequences in cortical L2/3 by Hierarchical Temporal Memory
inferred← citeshttps://doi.org/10.1016/j.tics.2018.06.006openalex-snapshot
Face Space Representations in Deep Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1016/j.tics.2022.12.006openalex-snapshot
Decoding semantic representations in mind and brain
inferred← citeshttps://doi.org/10.1016/j.tics.2025.11.016openalex-snapshot
Better artificial intelligence does not mean better models of biology
inferred← citeshttps://doi.org/10.1016/j.visres.2018.03.010openalex-snapshot
Color encoding in biologically-inspired convolutional neural networks
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3D shape estimation in a constraint optimization neural network
inferred← citeshttps://doi.org/10.1016/j.visres.2020.10.001openalex-snapshot
Putative ratios of facial attractiveness in a deep neural network
inferred← citeshttps://doi.org/10.1016/j.visres.2022.108058openalex-snapshot
Deep neural networks and image classification in biological vision
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Contextual feedback in object recognition: A biologically inspired computational model and human behavioral study
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Exon-based phylogenomics strengthens the phylogeny of Neotropical cichlids and identifies remaining conflicting clades (Cichliformes: Cichlidae: Cichlinae)
inferred← citeshttps://doi.org/10.1017/9781108339216.003openalex-snapshot
The Prehistory of Cognitive Science
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The Discipline Matures: Three Milestones
inferred← citeshttps://doi.org/10.1017/9781108339216.006openalex-snapshot
Physical Symbol Systems and the Language of Thought
inferred← citeshttps://doi.org/10.1017/9781108339216.007openalex-snapshot
Neural Networks and Distributed Information Processing
inferred← citeshttps://doi.org/10.1017/9781108339216.008openalex-snapshot
Applying Dynamical Systems Theory to Model the Mind
inferred← citeshttps://doi.org/10.1017/9781108339216.009openalex-snapshot
Bayesianism in Cognitive Science
inferred← citeshttps://doi.org/10.1017/9781108339216.011openalex-snapshot
Strategies for Brain Mapping
inferred← citeshttps://doi.org/10.1017/9781108339216.012openalex-snapshot
Models of Language Learning
inferred← citeshttps://doi.org/10.1017/9781108339216.013openalex-snapshot
Object Perception and Folk Physics
inferred← citeshttps://doi.org/10.1017/9781108339216.014openalex-snapshot
Machine Learning: From Expert Systems to Deep Learning
inferred← citeshttps://doi.org/10.1017/9781108339216.016openalex-snapshot
Mindreading: Advanced Topics
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