A citation-based method for searching scientific literature

Ha Hong, Daniel L K Yamins, Najib J Majaj, James J DiCarlo. Nat Neurosci 2016
Times Cited: 103







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



Times Cited
  Times     Co-cited
Similarity


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
47

Deep supervised, but not unsupervised, models may explain IT cortical representation.
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
309
45

Using goal-driven deep learning models to understand sensory cortex.
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
296
32

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
30

How does the brain solve visual object recognition?
James J DiCarlo, Davide Zoccolan, Nicole C Rust. Neuron 2012
512
30

Untangling invariant object recognition.
James J DiCarlo, David D Cox. Trends Cogn Sci 2007
335
29



Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Nature 2015
23

Fast readout of object identity from macaque inferior temporal cortex.
Chou P Hung, Gabriel Kreiman, Tomaso Poggio, James J DiCarlo. Science 2005
432
22

Representational similarity analysis - connecting the branches of systems neuroscience.
Nikolaus Kriegeskorte, Marieke Mur, Peter Bandettini. Front Syst Neurosci 2008
21

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
20

Hierarchical models of object recognition in cortex.
M Riesenhuber, T Poggio. Nat Neurosci 1999
19

Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
Jonas Kubilius, Stefania Bracci, Hans P Op de Beeck. PLoS Comput Biol 2016
73
26

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
17



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
15

Resolving human object recognition in space and time.
Radoslaw Martin Cichy, Dimitrios Pantazis, Aude Oliva. Nat Neurosci 2014
253
13

The Psychophysics Toolbox.
D H Brainard. Spat Vis 1997
12


The ventral visual pathway: an expanded neural framework for the processing of object quality.
Dwight J Kravitz, Kadharbatcha S Saleem, Chris I Baker, Leslie G Ungerleider, Mortimer Mishkin. Trends Cogn Sci 2013
436
12

A toolbox for representational similarity analysis.
Hamed Nili, Cai Wingfield, Alexander Walther, Li Su, William Marslen-Wilson, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
275
12

Decoding the visual and subjective contents of the human brain.
Yukiyasu Kamitani, Frank Tong. Nat Neurosci 2005
985
12


Seeing it all: Convolutional network layers map the function of the human visual system.
Michael Eickenberg, Alexandre Gramfort, Gaël Varoquaux, Bertrand Thirion. Neuroimage 2017
56
21

Speed of processing in the human visual system.
S Thorpe, D Fize, C Marlot. Nature 1996
11

Representational geometry: integrating cognition, computation, and the brain.
Nikolaus Kriegeskorte, Rogier A Kievit. Trends Cogn Sci 2013
268
11

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
16

Center-periphery organization of human object areas.
I Levy, U Hasson, G Avidan, T Hendler, R Malach. Nat Neurosci 2001
422
10

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
10

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


A feedforward architecture accounts for rapid categorization.
Thomas Serre, Aude Oliva, Tomaso Poggio. Proc Natl Acad Sci U S A 2007
405
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
10

Toward an Integration of Deep Learning and Neuroscience.
Adam H Marblestone, Greg Wayne, Konrad P Kording. Front Comput Neurosci 2016
140
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





Eccentricity bias as an organizing principle for human high-order object areas.
Uri Hasson, Ifat Levy, Marlene Behrmann, Talma Hendler, Rafael Malach. Neuron 2002
313
9

Inferotemporal cortex and object vision.
K Tanaka. Annu Rev Neurosci 1996
821
9


Population receptive field estimates in human visual cortex.
Serge O Dumoulin, Brian A Wandell. Neuroimage 2008
533
9

Information-based functional brain mapping.
Nikolaus Kriegeskorte, Rainer Goebel, Peter Bandettini. Proc Natl Acad Sci U S A 2006
9


Only some spatial patterns of fMRI response are read out in task performance.
Mark A Williams, Sabin Dang, Nancy G Kanwisher. Nat Neurosci 2007
124
9

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
8

High-level visual object representations are constrained by position.
Dwight J Kravitz, Nikolaus Kriegeskorte, Chris I Baker. Cereb Cortex 2010
105
8


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.