A citation-based method for searching scientific literature

Thomas Serre, Aude Oliva, Tomaso Poggio. Proc Natl Acad Sci U S A 2007
Times Cited: 431







List of co-cited articles
777 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


Hierarchical models of object recognition in cortex.
M Riesenhuber, T Poggio. Nat Neurosci 1999
36

How does the brain solve visual object recognition?
James J DiCarlo, Davide Zoccolan, Nicole C Rust. Neuron 2012
593
29

Deep supervised, but not unsupervised, models may explain IT cortical representation.
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
396
21

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
530
21

Speed of processing in the human visual system.
S Thorpe, D Fize, C Marlot. Nature 1996
20

Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Nature 2015
16

Representational similarity analysis - connecting the branches of systems neuroscience.
Nikolaus Kriegeskorte, Marieke Mur, Peter Bandettini. Front Syst Neurosci 2008
15


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
206
14

Resolving human object recognition in space and time.
Radoslaw Martin Cichy, Dimitrios Pantazis, Aude Oliva. Nat Neurosci 2014
299
13

Using goal-driven deep learning models to understand sensory cortex.
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
392
12



Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition.
Saeed Reza Kheradpisheh, Masoud Ghodrati, Mohammad Ganjtabesh, Timothée Masquelier. Sci Rep 2016
50
22

A toolbox for representational similarity analysis.
Hamed Nili, Cai Wingfield, Alexander Walther, Li Su, William Marslen-Wilson, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
333
11

Top-down facilitation of visual recognition.
M Bar, K S Kassam, A S Ghuman, J Boshyan, A M Schmid, A M Dale, M S Hämäläinen, K Marinkovic, D L Schacter, B R Rosen,[...]. Proc Natl Acad Sci U S A 2006
900
10

Visual objects in context.
Moshe Bar. Nat Rev Neurosci 2004
749
10



The dynamics of invariant object recognition in the human visual system.
Leyla Isik, Ethan M Meyers, Joel Z Leibo, Tomaso Poggio. J Neurophysiol 2014
119
9

Fast readout of object identity from macaque inferior temporal cortex.
Chou P Hung, Gabriel Kreiman, Tomaso Poggio, James J DiCarlo. Science 2005
456
9




Matching categorical object representations in inferior temporal cortex of man and monkey.
Nikolaus Kriegeskorte, Marieke Mur, Douglas A Ruff, Roozbeh Kiani, Jerzy Bodurka, Hossein Esteky, Keiji Tanaka, Peter A Bandettini. Neuron 2008
680
9

Untangling invariant object recognition.
James J DiCarlo, David D Cox. Trends Cogn Sci 2007
380
9

Deep neural networks rival the representation of primate IT cortex for core visual object recognition.
Charles F Cadieu, Ha Hong, Daniel L K Yamins, Nicolas Pinto, Diego Ardila, Ethan A Solomon, Najib J Majaj, James J DiCarlo. PLoS Comput Biol 2014
203
9

Recurrent computations for visual pattern completion.
Hanlin Tang, Martin Schrimpf, William Lotter, Charlotte Moerman, Ana Paredes, Josue Ortega Caro, Walter Hardesty, David Cox, Gabriel Kreiman. Proc Natl Acad Sci U S A 2018
50
18

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
90
10

The Psychophysics Toolbox.
D H Brainard. Spat Vis 1997
8


Neural mechanisms of object recognition.
Maximilian Riesenhuber, Tomaso Poggio. Curr Opin Neurobiol 2002
196
8

The lateral occipital complex and its role in object recognition.
K Grill-Spector, Z Kourtzi, N Kanwisher. Vision Res 2001
844
8


Deep convolutional networks do not classify based on global object shape.
Nicholas Baker, Hongjing Lu, Gennady Erlikhman, Philip J Kellman. PLoS Comput Biol 2018
48
16

A cortical area selective for visual processing of the human body.
P E Downing, Y Jiang, M Shuman, N Kanwisher. Science 2001
8


Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
Jonas Kubilius, Stefania Bracci, Hans P Op de Beeck. PLoS Comput Biol 2016
93
7

Robust object recognition with cortex-like mechanisms.
Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, Tomaso Poggio. IEEE Trans Pattern Anal Mach Intell 2007
322
7

Representational dynamics of object vision: the first 1000 ms.
Thomas Carlson, David A Tovar, Arjen Alink, Nikolaus Kriegeskorte. J Vis 2013
156
7


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
90
7

Top-down influences on visual processing.
Charles D Gilbert, Wu Li. Nat Rev Neurosci 2013
451
7

Hierarchical Bayesian inference in the visual cortex.
Tai Sing Lee, David Mumford. J Opt Soc Am A Opt Image Sci Vis 2003
547
7

A theory of cortical responses.
Karl Friston. Philos Trans R Soc Lond B Biol Sci 2005
7


Recurrence is required to capture the representational dynamics of the human visual system.
Tim C Kietzmann, Courtney J Spoerer, Lynn K A Sörensen, Radoslaw M Cichy, Olaf Hauk, Nikolaus Kriegeskorte. Proc Natl Acad Sci U S A 2019
81
8


Distributed and overlapping representations of faces and objects in ventral temporal cortex.
J V Haxby, M I Gobbini, M L Furey, A Ishai, J L Schouten, P Pietrini. Science 2001
6

Top-down facilitation of visual object recognition: object-based and context-based contributions.
Mark J Fenske, Elissa Aminoff, Nurit Gronau, Moshe Bar. Prog Brain Res 2006
110
6


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.