E P Simoncelli, B A Olshausen. Annu Rev Neurosci 2001
Times Cited: 850
Times Cited: 850
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
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Relations between the statistics of natural images and the response properties of cortical cells.
D J Field. J Opt Soc Am A 1987
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How does the brain solve visual object recognition?
James J DiCarlo, Davide Zoccolan, Nicole C Rust. Neuron 2012
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Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.
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Distributed hierarchical processing in the primate cerebral cortex.
D J Felleman, D C Van Essen. Cereb Cortex 1991
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10
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
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Using goal-driven deep learning models to understand sensory cortex.
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Visual perception and the statistical properties of natural scenes.
Wilson S Geisler. Annu Rev Psychol 2008
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The "independent components" of natural scenes are edge filters.
A J Bell, T J Sejnowski. Vision Res 1997
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Normalization as a canonical neural computation.
Matteo Carandini, David J Heeger. Nat Rev Neurosci 2011
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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
8
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Independent component filters of natural images compared with simple cells in primary visual cortex.
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8
Spatiotemporal energy models for the perception of motion.
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Performance-optimized hierarchical models predict neural responses in higher visual cortex.
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8
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
7
Decorrelation and efficient coding by retinal ganglion cells.
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Predictive coding: a fresh view of inhibition in the retina.
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Modelling the power spectra of natural images: statistics and information.
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Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream.
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Eye smarter than scientists believed: neural computations in circuits of the retina.
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Efficient coding of natural scenes in the lateral geniculate nucleus: experimental test of a computational theory.
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6
Sparse coding with an overcomplete basis set: a strategy employed by V1?
B A Olshausen, D J Field. Vision Res 1997
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6
Deep supervised, but not unsupervised, models may explain IT cortical representation.
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The VideoToolbox software for visual psychophysics: transforming numbers into movies.
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The distinct modes of vision offered by feedforward and recurrent processing.
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Could information theory provide an ecological theory of sensory processing?
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7
Cortical sensitivity to visual features in natural scenes.
Gidon Felsen, Jon Touryan, Feng Han, Yang Dan. PLoS Biol 2005
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5
A simple coding procedure enhances a neuron's information capacity.
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5
5
Flexible gating of contextual influences in natural vision.
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8
Statistical regularities of art images and natural scenes: spectra, sparseness and nonlinearities.
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Visual discomfort and natural image statistics.
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8
Explicit information for category-orthogonal object properties increases along the ventral stream.
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5
Complete functional characterization of sensory neurons by system identification.
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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.