A citation-based method for searching scientific literature

Chou P Hung, Gabriel Kreiman, Tomaso Poggio, James J DiCarlo. Science 2005
Times Cited: 432







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



Times Cited
  Times     Co-cited
Similarity


How does the brain solve visual object recognition?
James J DiCarlo, Davide Zoccolan, Nicole C Rust. Neuron 2012
512
27

Untangling invariant object recognition.
James J DiCarlo, David D Cox. Trends Cogn Sci 2007
335
23

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
414
23

Explicit information for category-orthogonal object properties increases along the ventral stream.
Ha Hong, Daniel L K Yamins, Najib J Majaj, James J DiCarlo. Nat Neurosci 2016
103
16

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

Speed of processing in the human visual system.
S Thorpe, D Fize, C Marlot. Nature 1996
16

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
14

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
614
14

Deep supervised, but not unsupervised, models may explain IT cortical representation.
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
309
14

The Psychophysics Toolbox.
D H Brainard. Spat Vis 1997
13



Resolving human object recognition in space and time.
Radoslaw Martin Cichy, Dimitrios Pantazis, Aude Oliva. Nat Neurosci 2014
253
12

The dynamics of invariant object recognition in the human visual system.
Leyla Isik, Ethan M Meyers, Joel Z Leibo, Tomaso Poggio. J Neurophysiol 2014
107
12

A cortical region consisting entirely of face-selective cells.
Doris Y Tsao, Winrich A Freiwald, Roger B H Tootell, Margaret S Livingstone. Science 2006
606
12

Inferotemporal cortex and object vision.
K Tanaka. Annu Rev Neurosci 1996
821
12

Using goal-driven deep learning models to understand sensory cortex.
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
296
12

Representational similarity analysis - connecting the branches of systems neuroscience.
Nikolaus Kriegeskorte, Marieke Mur, Peter Bandettini. Front Syst Neurosci 2008
11

Decoding the visual and subjective contents of the human brain.
Yukiyasu Kamitani, Frank Tong. Nat Neurosci 2005
985
11

A feedforward architecture accounts for rapid categorization.
Thomas Serre, Aude Oliva, Tomaso Poggio. Proc Natl Acad Sci U S A 2007
405
11

Hierarchical models of object recognition in cortex.
M Riesenhuber, T Poggio. Nat Neurosci 1999
11


Representational dynamics of object vision: the first 1000 ms.
Thomas Carlson, David A Tovar, Arjen Alink, Nikolaus Kriegeskorte. J Vis 2013
138
10

Beyond mind-reading: multi-voxel pattern analysis of fMRI data.
Kenneth A Norman, Sean M Polyn, Greg J Detre, James V Haxby. Trends Cogn Sci 2006
10

Visual object recognition.
N K Logothetis, D L Sheinberg. Annu Rev Neurosci 1996
613
10



Object category structure in response patterns of neuronal population in monkey inferior temporal cortex.
Roozbeh Kiani, Hossein Esteky, Koorosh Mirpour, Keiji Tanaka. J Neurophysiol 2007
259
10

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
173
10





The importance of mixed selectivity in complex cognitive tasks.
Mattia Rigotti, Omri Barak, Melissa R Warden, Xiao-Jing Wang, Nathaniel D Daw, Earl K Miller, Stefano Fusi. Nature 2013
503
9


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
66
13


Representational geometry: integrating cognition, computation, and the brain.
Nikolaus Kriegeskorte, Rogier A Kievit. Trends Cogn Sci 2013
268
8

Encoding and decoding in fMRI.
Thomas Naselaris, Kendrick N Kay, Shinji Nishimoto, Jack L Gallant. Neuroimage 2011
290
8

Stimulus-selective properties of inferior temporal neurons in the macaque.
R Desimone, T D Albright, C G Gross, C Bruce. J Neurosci 1984
918
8

Signals in inferotemporal and perirhinal cortex suggest an untangling of visual target information.
Marino Pagan, Luke S Urban, Margot P Wohl, Nicole C Rust. Nat Neurosci 2013
62
12

Identifying natural images from human brain activity.
Kendrick N Kay, Thomas Naselaris, Ryan J Prenger, Jack L Gallant. Nature 2008
514
8



Object decoding with attention in inferior temporal cortex.
Ying Zhang, Ethan M Meyers, Narcisse P Bichot, Thomas Serre, Tomaso A Poggio, Robert Desimone. Proc Natl Acad Sci U S A 2011
82
8

Controlling low-level image properties: the SHINE toolbox.
Verena Willenbockel, Javid Sadr, Daniel Fiset, Greg O Horne, Frédéric Gosselin, James W Tanaka. Behav Res Methods 2010
438
7

Shape representation in the inferior temporal cortex of monkeys.
N K Logothetis, J Pauls, T Poggio. Curr Biol 1995
534
7


The distribution of category and location information across object-selective regions in human visual cortex.
Rebecca F Schwarzlose, Jascha D Swisher, Sabin Dang, Nancy Kanwisher. Proc Natl Acad Sci U S A 2008
169
7

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
155
7


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.