A citation-based method for searching scientific literature

James J DiCarlo, David D Cox. Trends Cogn Sci 2007
Times Cited: 339







List of co-cited articles
765 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
516
41

Using goal-driven deep learning models to understand sensory cortex.
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
306
24

Deep supervised, but not unsupervised, models may explain IT cortical representation.
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
319
22

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
425
21

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
20

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
176
18

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
619
18

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
106
18

Hierarchical models of object recognition in cortex.
M Riesenhuber, T Poggio. Nat Neurosci 1999
17

Resolving human object recognition in space and time.
Radoslaw Martin Cichy, Dimitrios Pantazis, Aude Oliva. Nat Neurosci 2014
257
16

Fast readout of object identity from macaque inferior temporal cortex.
Chou P Hung, Gabriel Kreiman, Tomaso Poggio, James J DiCarlo. Science 2005
434
16

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
159
15


Representational similarity analysis - connecting the branches of systems neuroscience.
Nikolaus Kriegeskorte, Marieke Mur, Peter Bandettini. Front Syst Neurosci 2008
14

Speed of processing in the human visual system.
S Thorpe, D Fize, C Marlot. Nature 1996
13

Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Nature 2015
13



Encoding and decoding in fMRI.
Thomas Naselaris, Kendrick N Kay, Shinji Nishimoto, Jack L Gallant. Neuroimage 2011
293
11

Representational geometry: integrating cognition, computation, and the brain.
Nikolaus Kriegeskorte, Rogier A Kievit. Trends Cogn Sci 2013
275
11

Decoding neural representational spaces using multivariate pattern analysis.
James V Haxby, Andrew C Connolly, J Swaroop Guntupalli. Annu Rev Neurosci 2014
258
11

A toolbox for representational similarity analysis.
Hamed Nili, Cai Wingfield, Alexander Walther, Li Su, William Marslen-Wilson, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
280
11


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
64
17

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
68
16


Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
Jonas Kubilius, Stefania Bracci, Hans P Op de Beeck. PLoS Comput Biol 2016
74
13

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


Representational dynamics of object vision: the first 1000 ms.
Thomas Carlson, David A Tovar, Arjen Alink, Nikolaus Kriegeskorte. J Vis 2013
139
10

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

Neural correlations, population coding and computation.
Bruno B Averbeck, Peter E Latham, Alexandre Pouget. Nat Rev Neurosci 2006
816
10

Population receptive field estimates in human visual cortex.
Serge O Dumoulin, Brian A Wandell. Neuroimage 2008
542
9



Seeing it all: Convolutional network layers map the function of the human visual system.
Michael Eickenberg, Alexandre Gramfort, Gaël Varoquaux, Bertrand Thirion. Neuroimage 2017
59
15

Identifying natural images from human brain activity.
Kendrick N Kay, Thomas Naselaris, Ryan J Prenger, Jack L Gallant. Nature 2008
518
9

Decoding the visual and subjective contents of the human brain.
Yukiyasu Kamitani, Frank Tong. Nat Neurosci 2005
988
9


Natural image statistics and neural representation.
E P Simoncelli, B A Olshausen. Annu Rev Neurosci 2001
924
9

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
58
15

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
262
8

The dynamics of invariant object recognition in the human visual system.
Leyla Isik, Ethan M Meyers, Joel Z Leibo, Tomaso Poggio. J Neurophysiol 2014
108
8

Information-based functional brain mapping.
Nikolaus Kriegeskorte, Rainer Goebel, Peter Bandettini. Proc Natl Acad Sci U S A 2006
8


Inferotemporal cortex and object vision.
K Tanaka. Annu Rev Neurosci 1996
822
8



Normalization as a canonical neural computation.
Matteo Carandini, David J Heeger. Nat Rev Neurosci 2011
756
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.