A citation-based method for searching scientific literature

James J DiCarlo, Davide Zoccolan, Nicole C Rust. Neuron 2012
Times Cited: 516







List of co-cited articles
683 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
425
23

Deep supervised, but not unsupervised, models may explain IT cortical representation.
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
319
20

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

Untangling invariant object recognition.
James J DiCarlo, David D Cox. Trends Cogn Sci 2007
339
16

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

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
24

Using goal-driven deep learning models to understand sensory cortex.
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
306
13


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
12



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
11

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
11


Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Nature 2015
10


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
42
23

Speed of processing in the human visual system.
S Thorpe, D Fize, C Marlot. Nature 1996
9



Deep convolutional models improve predictions of macaque V1 responses to natural images.
Santiago A Cadena, George H Denfield, Edgar Y Walker, Leon A Gatys, Andreas S Tolias, Matthias Bethge, Alexander S Ecker. PLoS Comput Biol 2019
34
26



Natural image statistics and neural representation.
E P Simoncelli, B A Olshausen. Annu Rev Neurosci 2001
924
8

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
11


The Psychophysics Toolbox.
D H Brainard. Spat Vis 1997
7


Normalization as a canonical neural computation.
Matteo Carandini, David J Heeger. Nat Rev Neurosci 2011
756
7

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
7

Top-down influences on visual processing.
Charles D Gilbert, Wu Li. Nat Rev Neurosci 2013
402
7

Resolving human object recognition in space and time.
Radoslaw Martin Cichy, Dimitrios Pantazis, Aude Oliva. Nat Neurosci 2014
257
7

Neural population control via deep image synthesis.
Pouya Bashivan, Kohitij Kar, James J DiCarlo. Science 2019
54
12

Inferotemporal cortex and object vision.
K Tanaka. Annu Rev Neurosci 1996
822
7

A toolbox for representational similarity analysis.
Hamed Nili, Cai Wingfield, Alexander Walther, Li Su, William Marslen-Wilson, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
280
7

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
9

Deep Learning: The Good, the Bad, and the Ugly.
Thomas Serre. Annu Rev Vis Sci 2019
26
26

Separate visual pathways for perception and action.
M A Goodale, A D Milner. Trends Neurosci 1992
6

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
6

Slow feature analysis: unsupervised learning of invariances.
Laurenz Wiskott, Terrence J Sejnowski. Neural Comput 2002
200
6

Neural correlations, population coding and computation.
Bruno B Averbeck, Peter E Latham, Alexandre Pouget. Nat Rev Neurosci 2006
816
6


Highly selective receptive fields in mouse visual cortex.
Cristopher M Niell, Michael P Stryker. J Neurosci 2008
575
6

Visual object recognition.
N K Logothetis, D L Sheinberg. Annu Rev Neurosci 1996
615
6

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
510
6

Representational geometry: integrating cognition, computation, and the brain.
Nikolaus Kriegeskorte, Rogier A Kievit. Trends Cogn Sci 2013
275
6

The parahippocampal place area: recognition, navigation, or encoding?
R Epstein, A Harris, D Stanley, N Kanwisher. Neuron 1999
513
6



Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.
Courtney J Spoerer, Patrick McClure, Nikolaus Kriegeskorte. Front Psychol 2017
35
17


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.