E P Simoncelli, B A Olshausen. Annu Rev Neurosci 2001
Times Cited: 930
Times Cited: 930
Times Cited
Times Co-cited
Similarity
Emergence of simple-cell receptive field properties by learning a sparse code for natural images.
B A Olshausen, D J Field. Nature 1996
B A Olshausen, D J Field. Nature 1996
27
Relations between the statistics of natural images and the response properties of cortical cells.
D J Field. J Opt Soc Am A 1987
D J Field. J Opt Soc Am A 1987
12
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.
D H HUBEL, T N WIESEL. J Physiol 1962
D H HUBEL, T N WIESEL. J Physiol 1962
11
Visual perception and the statistical properties of natural scenes.
Wilson S Geisler. Annu Rev Psychol 2008
Wilson S Geisler. Annu Rev Psychol 2008
11
Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.
R P Rao, D H Ballard. Nat Neurosci 1999
R P Rao, D H Ballard. Nat Neurosci 1999
10
10
How does the brain solve visual object recognition?
James J DiCarlo, Davide Zoccolan, Nicole C Rust. Neuron 2012
James J DiCarlo, Davide Zoccolan, Nicole C Rust. Neuron 2012
10
The "independent components" of natural scenes are edge filters.
A J Bell, T J Sejnowski. Vision Res 1997
A J Bell, T J Sejnowski. Vision Res 1997
10
Using goal-driven deep learning models to understand sensory cortex.
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
9
Distributed hierarchical processing in the primate cerebral cortex.
D J Felleman, D C Van Essen. Cereb Cortex 1991
D J Felleman, D C Van Essen. Cereb Cortex 1991
8
Sparse coding and decorrelation in primary visual cortex during natural vision.
W E Vinje, J L Gallant. Science 2000
W E Vinje, J L Gallant. Science 2000
8
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream.
Umut Güçlü, Marcel A J van Gerven. J Neurosci 2015
Umut Güçlü, Marcel A J van Gerven. J Neurosci 2015
8
Performance-optimized hierarchical models predict neural responses in higher visual cortex.
Daniel L K Yamins, Ha Hong, Charles F Cadieu, Ethan A Solomon, Darren Seibert, James J DiCarlo. Proc Natl Acad Sci U S A 2014
Daniel L K Yamins, Ha Hong, Charles F Cadieu, Ethan A Solomon, Darren Seibert, James J DiCarlo. Proc Natl Acad Sci U S A 2014
8
Spatiotemporal energy models for the perception of motion.
E H Adelson, J R Bergen. J Opt Soc Am A 1985
E H Adelson, J R Bergen. J Opt Soc Am A 1985
8
7
Normalization as a canonical neural computation.
Matteo Carandini, David J Heeger. Nat Rev Neurosci 2011
Matteo Carandini, David J Heeger. Nat Rev Neurosci 2011
7
Deep supervised, but not unsupervised, models may explain IT cortical representation.
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
7
Independent component filters of natural images compared with simple cells in primary visual cortex.
J H van Hateren, A van der Schaaf. Proc Biol Sci 1998
J H van Hateren, A van der Schaaf. Proc Biol Sci 1998
7
Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex.
Dario L Ringach. J Neurophysiol 2002
Dario L Ringach. J Neurophysiol 2002
6
Receptive fields of single neurones in the cat's striate cortex.
D H HUBEL, T N WIESEL. J Physiol 1959
D H HUBEL, T N WIESEL. J Physiol 1959
6
The VideoToolbox software for visual psychophysics: transforming numbers into movies.
D G Pelli. Spat Vis 1997
D G Pelli. Spat Vis 1997
6
Modelling the power spectra of natural images: statistics and information.
A van der Schaaf, J H van Hateren. Vision Res 1996
A van der Schaaf, J H van Hateren. Vision Res 1996
6
Decorrelation and efficient coding by retinal ganglion cells.
Xaq Pitkow, Markus Meister. Nat Neurosci 2012
Xaq Pitkow, Markus Meister. Nat Neurosci 2012
6
Sparse coding with an overcomplete basis set: a strategy employed by V1?
B A Olshausen, D J Field. Vision Res 1997
B A Olshausen, D J Field. Vision Res 1997
6
Neuroscience Needs Behavior: Correcting a Reductionist Bias.
John W Krakauer, Asif A Ghazanfar, Alex Gomez-Marin, Malcolm A MacIver, David Poeppel. Neuron 2017
John W Krakauer, Asif A Ghazanfar, Alex Gomez-Marin, Malcolm A MacIver, David Poeppel. Neuron 2017
5
The distinct modes of vision offered by feedforward and recurrent processing.
V A Lamme, P R Roelfsema. Trends Neurosci 2000
V A Lamme, P R Roelfsema. Trends Neurosci 2000
5
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.
Radoslaw Martin Cichy, Aditya Khosla, Dimitrios Pantazis, Antonio Torralba, Aude Oliva. Sci Rep 2016
Radoslaw Martin Cichy, Aditya Khosla, Dimitrios Pantazis, Antonio Torralba, Aude Oliva. Sci Rep 2016
5
Image quality assessment: from error visibility to structural similarity.
Zhou Wang, Alan Conrad Bovik, Hamid Rahim Sheikh, Eero P Simoncelli. IEEE Trans Image Process 2004
Zhou Wang, Alan Conrad Bovik, Hamid Rahim Sheikh, Eero P Simoncelli. IEEE Trans Image Process 2004
5
Chromatic mechanisms in lateral geniculate nucleus of macaque.
A M Derrington, J Krauskopf, P Lennie. J Physiol 1984
A M Derrington, J Krauskopf, P Lennie. J Physiol 1984
5
Color in the cortex: single- and double-opponent cells.
Robert Shapley, Michael J Hawken. Vision Res 2011
Robert Shapley, Michael J Hawken. Vision Res 2011
5
Eye smarter than scientists believed: neural computations in circuits of the retina.
Tim Gollisch, Markus Meister. Neuron 2010
Tim Gollisch, Markus Meister. Neuron 2010
5
Trichromacy, opponent colours coding and optimum colour information transmission in the retina.
G Buchsbaum, A Gottschalk. Proc R Soc Lond B Biol Sci 1983
G Buchsbaum, A Gottschalk. Proc R Soc Lond B Biol Sci 1983
5
Stimulus- and goal-oriented frameworks for understanding natural vision.
Maxwell H Turner, Luis Gonzalo Sanchez Giraldo, Odelia Schwartz, Fred Rieke. Nat Neurosci 2019
Maxwell H Turner, Luis Gonzalo Sanchez Giraldo, Odelia Schwartz, Fred Rieke. Nat Neurosci 2019
45
Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks.
Rishi Rajalingham, Elias B Issa, Pouya Bashivan, Kohitij Kar, Kailyn Schmidt, James J DiCarlo. J Neurosci 2018
Rishi Rajalingham, Elias B Issa, Pouya Bashivan, Kohitij Kar, Kailyn Schmidt, James J DiCarlo. J Neurosci 2018
6
A deep learning framework for neuroscience.
Blake A Richards, Timothy P Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath, Rui Ponte Costa, Archy de Berker, Surya Ganguli,[...]. Nat Neurosci 2019
Blake A Richards, Timothy P Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath, Rui Ponte Costa, Archy de Berker, Surya Ganguli,[...]. Nat Neurosci 2019
5
Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior.
Kohitij Kar, Jonas Kubilius, Kailyn Schmidt, Elias B Issa, James J DiCarlo. Nat Neurosci 2019
Kohitij Kar, Jonas Kubilius, Kailyn Schmidt, Elias B Issa, James J DiCarlo. Nat Neurosci 2019
8
Building machines that learn and think like people.
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, Samuel J Gershman. Behav Brain Sci 2017
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, Samuel J Gershman. Behav Brain Sci 2017
5
Could information theory provide an ecological theory of sensory processing?
Joseph J Atick. Network 2011
Joseph J Atick. Network 2011
5
Visual discomfort and natural image statistics.
Igor Juricevic, Leah Land, Arnold Wilkins, Michael A Webster. Perception 2010
Igor Juricevic, Leah Land, Arnold Wilkins, Michael A Webster. Perception 2010
7
Representational similarity analysis - connecting the branches of systems neuroscience.
Nikolaus Kriegeskorte, Marieke Mur, Peter Bandettini. Front Syst Neurosci 2008
Nikolaus Kriegeskorte, Marieke Mur, Peter Bandettini. Front Syst Neurosci 2008
5
Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.