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We introduce a deep recurrent neural network architecture that approximates visual cortical circuits.
The tilt illusion: repulsion and attraction effects in the oblique meridian
B. O’Toole and P. Wenderoth · 1977
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Distributed hierarchical processing in the primate cerebral cortex
D. J. Felleman and D. C. Van Essen · 1991
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Induction of multiscale temporal structure
M. C. Mozer · 1992
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Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit
R. H. Hahnloser, R. Sarpeshkar, M. a. Mahowald, R. J. Douglas, and H. S. Seung · 2000
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View from the top: Hierarchies and reverse hierarchies in the visual system
S. Hochstein and M. Ahissar · 2002
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Hierarchical bayesian inference in the visual cortex
T. S. Lee and D. Mumford · 2003
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Cortical algorithms for perceptual grouping
P. R. Roelfsema · 2006
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Brain states: top-down influences in sensory processing
C. D. Gilbert and M. Sigman · 2007
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Contour detection and hierarchical image segmentation
P. Arbeláez, M. Maire, C. Fowlkes, and J. Malik · 2011
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Volume electron microscopy for neuronal circuit reconstruction
K. L. Briggman and D. D. Bock · 2012
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Machine learning of hierarchical clustering to segment 2D and 3D images
J. Nunez-Iglesias, R. Kennedy, T. Parag, J. Shi, and D. B. Chklovskii · 2013
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Recurrent processing during object recognition
R. C. O’Reilly, D. Wyatte, S. Herd, B. Mingus, and D. J. Jilk · 2013
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Early recurrent feedback facilitates visual object recognition under challenging conditions
D. Wyatte, D. J. Jilk, and R. C. O’Reilly · 2014
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Oriented edge forests for boundary detection
S. Hallman and C. C. Fowlkes · 2015
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Feedforward and feedback processes in vision
H. Kafaligonul, B. G. Breitmeyer, and H. Öğmen · 2015
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Saturated reconstruction of a volume of neocortex
N. Kasthuri, K. J. Hayworth, D. R. Berger, R. L. Schalek, J. A. Conchello, S. Knowles-Barley, D. Lee, A. Vázquez-Reina, V. Kaynig, T. R. Jones, M. Roberts, J. L. Morgan, J. C. Tapia, H. S. Seung, W. G. Roncal, J. T. Vogelstein, R. Burns, D. L. Sussman, C. E. Priebe, H. Pfister, and J. W. Lichtman · 2015
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Pushing the boundaries of boundary detection using deep learning
I. Kokkinos · 2015
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Deep neural networks: A new framework for modeling biological vision and brain information processing
N. Kriegeskorte · 2015
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Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 2015
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U-Net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Cited alongside, same era.
Cortical information flow during flexible sensorimotor decisions
M. Siegel, T. J. Buschman, and E. K. Miller · 2015
Cited alongside, same era.
Species-specific wiring for direction selectivity in the mammalian retina
H. Ding, R. G. Smith, A. Poleg-Polsky, J. S. Diamond, and K. L. Briggman · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
A unified account of tilt illusions, association fields, and contour detection based on elastica
S. W. Keemink and M. C. W. van Rossum · 2016
Cited alongside, same era.
Bridging the gaps between residual learning, recurrent neural networks and visual cortex
Q. Liao and T. Poggio · 2016
Generalisation in humans and deep neural networks
R. Geirhos, C. R. M. Temme, J. Rauber, H. H. Schütt, M. Bethge, and F. A. Wichmann · 2018
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Not-So-CLEVR: learning same–different relations strains feedforward neural networks
J. K. Kim, M. Ricci, and T. Serre · 2018
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Complementary surrounds explain diverse contextual phenomena across visual modalities
D. A. Mély, D. Linsley, and T. Serre · 2018
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Task-Driven convolutional recurrent models of the visual system
A. Nayebi, D. Bear, J. Kubilius, K. Kar, S. Ganguli, D. Sussillo, J. J. DiCarlo, and D. L. Yamins · 2018
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Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms
P. J. Phillips, A. N. Yates, Y. Hu, C. A. Hahn, E. Noyes, K. Jackson, J. G. Cavazos, G. Jeckeln, R. Ranjan, S. Sankaranarayanan, J.-C. Chen, C. D. Castillo, R. Chellappa, D. White, and A. J. O’Toole · 2018
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Cited alongside, same era.
Deep predictive coding networks for video prediction and unsupervised learning
W. Lotter, G. Kreiman, and D. Cox · 2016
Cited alongside, same era.
Instance normalization: The missing ingredient for fast stylization
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2016
Cited alongside, same era.
Using goal-driven deep learning models to understand sensory cortex
D. L. K. Yamins and J. J. DiCarlo · 2016
Cited alongside, same era.
Recurrent batch normalization
T. Cooijmans, N. Ballas, C. Laurent, Ç. Gülçehre, and A. Courville · 2017
Cited alongside, same era.
Dynamic representation of partially occluded objects in primate prefrontal and visual cortex
A. M. Fyall, Y. El-Shamayleh, H. Choi, E. Shea-Brown, and A. Pasupathy · 2017
Cited alongside, same era.
A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs
D. George, W. Lehrach, K. Kansky, M. Lázaro-Gredilla, C. Laan, B. Marthi, X. Lou, Z. Meng, Y. Liu, H. Wang, A. Lavin, and D. S. Phoenix · 2017
Cited alongside, same era.
Do CIFAR-10 classifiers generalize to CIFAR-10?
B. Recht, R. Roelofs, L. Schmidt, and V. Shankar · 2018
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The elephant in the room
A. Rosenfeld, R. Zemel, and J. K. Tsotsos · 2018
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Can recurrent neural networks warp time?
C. Tallec and Y. Ollivier · 2018
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Recurrent computations for visual pattern completion
H. Tang, M. Schrimpf, W. Lotter, C. Moerman, A. Paredes, J. Ortega Caro, W. Hardesty, D. Cox, and G. Kreiman · 2018
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Deep predictive coding network for object recognition
H. Wen, K. Han, J. Shi, Y. Zhang, E. Culurciello, and Z. Liu · 2018
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Single-neuron perturbations reveal feature-specific competition in V1
S. N. Chettih and C. D. Harvey · 2019
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Bi-Directional cascade network for perceptual edge detection
J. He, S. Zhang, M. Yang, Y. Shan, and T. Huang · 2019
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Segmentation-Enhanced CycleGAN
M. Januszewski and V. Jain · 2019
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Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior
K. Kar, J. Kubilius, K. Schmidt, E. B. Issa, and J. J. DiCarlo · 2019
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Recurrence required to capture the dynamic computations of the human ventral visual stream
T. C. Kietzmann, C. J. Spoerer, L. Sörensen, and others · 2019
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Learning what and where to attend with humans in the loop
D. Linsley, D. Shiebler, S. Eberhardt, and T. Serre · 2019
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Richer convolutional features for edge detection
Y. Liu, M.-M. Cheng, X. Hu, J.-W. Bian, L. Zhang, X. Bai, and J. Tang · 2019
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Deep learning: The good, the bad, and the ugly
T. Serre · 2019
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Deep crisp boundaries: From boundaries to Higher-Level tasks
Y. Wang, X. Zhao, Y. Li, and K. Huang · 2019
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