Research graph
References from Category-specific perceptual learning of robust object recognition modelled using deep neural networks. Local targets link to admitted publications; unresolved targets remain external evidence.
Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting
10.1073/pnas.95.23.13988 · 1998 · External reference
Unresolved reference
2018 · External reference
Signal but not noise changes with perceptual learning
10.1038/46027 · 1999 · External reference
Noise-trained deep neural networks effectively predict human vision and its neural responses to challenging images
10.1371/journal.pbio.3001418 · 2021 · External reference
Convolutional neural networks trained with a developmental sequence of blurry to clear images reveal core differences between face and object processing
10.1167/jov.21.12.6 · 2021 · External reference
Spatial-frequency cutoff requirements for pattern recognition in central and peripheral vision
10.1016/j.visres.2011.06.020 · 2011 · External reference
How does the brain solve visual object recognition?
10.1016/j.neuron.2012.01.010 · 2012 · External reference
Foundations of vision.
2018 · External reference
Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
10.1007/bf00344251 · 1980 · External reference
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
10.1113/jphysiol.1962.sp006837 · 1962 · External reference
Unresolved reference
1990 · External reference
A Convolutional Subunit Model for Neuronal Responses in Macaque V1
10.1523/jneurosci.2815-13.2015 · 2015 · External reference
Unresolved reference
2016 · External reference
ImageNet classification with deep convolutional neural networks
2012 · External reference
Very deep convolutional networks for large-scale image recognition
2015 · External reference
Going deeper with convolutions
10.1109/cvpr.2015.7298594 · 2015 · External reference
Rethinking the inception architecture for computer vision
10.1109/cvpr.2016.308 · 2016 · External reference
Deep convolutional models improve predictions of macaque V1 responses to natural images
10.1371/journal.pcbi.1006897 · 2019 · External reference
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
10.1038/srep27755 · 2016 · External reference
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream
10.1523/jneurosci.5023-14.2015 · 2015 · External reference
Deep supervised, but not unsupervised, models may explain IT cortical representation
2014 · External reference
Mid-level visual features underlie the high-level categorical organization of the ventral stream
10.1073/pnas.1719616115 · 2018 · External reference
Convolutional neural network models applied to neuronal responses in macaque V1 reveal limited nonlinear processing
10.1167/jov.24.6.1 · 2024 · External reference
Brain-Score: Which Artificial Neural Network for Object Recognition is Most Brain-Like?
2020 · External reference
Performance-optimized hierarchical models predict neural responses in higher visual cortex
10.1073/pnas.1403112111 · 2014 · External reference
Unresolved reference
2019 · External reference
Evaluating the robustness of object recognition to visual noise in humans and convolutional neural networks
10.1167/17.10.805 · 2017 · External reference
Unresolved reference
2018 · External reference
Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks
10.1038/s41467-024-45679-0 · 2024 · External reference
Towards a theory of early visual processing
10.1162/neco.1990.2.3.308 · 1990 · External reference
Characterizing observers using external noise and observer models: assessing internal representations with external noise
10.1037/0033-295x.115.1.44 · 2008 · External reference
Image denoising review: From classical to state-of-the-art approaches
10.1016/j.inffus.2019.09.003 · 2020 · External reference
Characterizing perceptual learning with external noise
10.1207/s15516709cog2802_3 · 2004 · External reference
Current directions in visual perceptual learning
10.1038/s44159-022-00107-2 · 2022 · External reference
ImageNet Large Scale Visual Recognition Challenge
10.1007/s11263-015-0816-y · 2015 · External reference
Dissociations and Associations between Shape and Category Representations in the Two Visual Pathways
10.1523/jneurosci.2314-15.2016 · 2016 · External reference
Spikiness and animacy as potential organizing principles of human ventral visual cortex
10.1093/cercor/bhad108 · 2023 · External reference
Distributed representations of behavior-derived object dimensions in the human visual system
2024 · External reference
Disentangling five dimensions of animacy in human brain and behaviour
10.1038/s42003-022-04194-y · 2022 · External reference
Matching categorical object representations in inferior temporal cortex of man and monkey
10.1016/j.neuron.2008.10.043 · 2008 · External reference
The animacy continuum in the human ventral vision pathway
10.1162/jocn_a_00733 · 2015 · External reference
QUEST: a Bayesian adaptive psychometric method
10.3758/bf03202828 · 1983 · External reference
How attention extracts objects from noise
10.1152/jn.00127.2013 · 2013 · External reference
Basic objects in natural categories
10.1016/0010-0285(76)90013-x · 1976 · External reference
Perceptual learning specific for orientation and spatial frequency
10.1038/287043a0 · 1980 · External reference
Perceptual learning: toward a comprehensive theory
10.1146/annurev-psych-010814-015214 · 2015 · External reference
Variability in training unlocks generalization in visual perceptual learning through invariant representations
10.1016/j.cub.2023.01.011 · 2023 · External reference
Profiles of visual perceptual learning in feature space
10.1016/j.isci.2024.109128 · 2024 · External reference
Deep Neural Networks for Modeling Visual Perceptual Learning
10.1523/jneurosci.1620-17.2018 · 2018 · External reference
Unresolved reference
2019 · External reference
Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior
10.1038/s41593-019-0392-5 · 2019 · External reference
Recurrent processing during object recognition
10.3389/fpsyg.2013.00124 · 2013 · External reference
Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition
10.3389/fpsyg.2017.01551 · 2017 · External reference
The limits of feedforward vision: recurrent processing promotes robust object recognition when objects are degraded
10.1162/jocn_a_00282 · 2012 · External reference
Perceptual learning selectively refines orientation representations in early visual cortex
10.1523/jneurosci.6112-11.2012 · 2012 · External reference
Perceptual learning and top-down influences in primary visual cortex
10.1038/nn1255 · 2004 · External reference
Perceptual learning rules based on reinforcers and attention
10.1016/j.tics.2009.11.005 · 2010 · External reference
The attentive reconstruction of objects facilitates robust object recognition
2024 · External reference
Unresolved reference
External reference
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
10.1371/journal.pone.0130140 · 2015 · External reference
