A citation-based method for searching scientific literature

Michelle R Greene, Bruce C Hansen. PLoS Comput Biol 2018
Times Cited: 15







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



Times Cited
  Times     Co-cited
Similarity


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
199
73

Deep supervised, but not unsupervised, models may explain IT cortical representation.
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
388
53


Representational similarity analysis - connecting the branches of systems neuroscience.
Nikolaus Kriegeskorte, Marieke Mur, Peter Bandettini. Front Syst Neurosci 2008
40

Resolving human object recognition in space and time.
Radoslaw Martin Cichy, Dimitrios Pantazis, Aude Oliva. Nat Neurosci 2014
295
40

A toolbox for representational similarity analysis.
Hamed Nili, Cai Wingfield, Alexander Walther, Li Su, William Marslen-Wilson, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
325
40

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
515
33

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks.
Radoslaw Martin Cichy, Aditya Khosla, Dimitrios Pantazis, Aude Oliva. Neuroimage 2017
66
33

Speed of processing in the human visual system.
S Thorpe, D Fize, C Marlot. Nature 1996
33

Places: A 10 Million Image Database for Scene Recognition.
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, Antonio Torralba. IEEE Trans Pattern Anal Mach Intell 2018
78
26

The Psychophysics Toolbox.
D H Brainard. Spat Vis 1997
26

Reconstructing visual experiences from brain activity evoked by natural movies.
Shinji Nishimoto, An T Vu, Thomas Naselaris, Yuval Benjamini, Bin Yu, Jack L Gallant. Curr Biol 2011
288
26

Using goal-driven deep learning models to understand sensory cortex.
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
379
26

Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
Jonas Kubilius, Stefania Bracci, Hans P Op de Beeck. PLoS Comput Biol 2016
90
26

Visual information representation and rapid-scene categorization are simultaneous across cortex: An MEG study.
Pavan Ramkumar, Bruce C Hansen, Sebastian Pannasch, Lester C Loschky. Neuroimage 2016
12
33


Visual objects in context.
Moshe Bar. Nat Rev Neurosci 2004
741
20

Computational modelling of visual attention.
L Itti, C Koch. Nat Rev Neurosci 2001
20



Do we know what the early visual system does?
Matteo Carandini, Jonathan B Demb, Valerio Mante, David J Tolhurst, Yang Dan, Bruno A Olshausen, Jack L Gallant, Nicole C Rust. J Neurosci 2005
291
20


From image statistics to scene gist: evoked neural activity reveals transition from low-level natural image structure to scene category.
Iris I A Groen, Sennay Ghebreab, Hielke Prins, Victor A F Lamme, H Steven Scholte. J Neurosci 2013
54
20

How does the brain solve visual object recognition?
James J DiCarlo, Davide Zoccolan, Nicole C Rust. Neuron 2012
580
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
199
20

Seeing it all: Convolutional network layers map the function of the human visual system.
Michael Eickenberg, Alexandre Gramfort, Gaël Varoquaux, Bertrand Thirion. Neuroimage 2017
78
20

Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search.
Antonio Torralba, Aude Oliva, Monica S Castelhano, John M Henderson. Psychol Rev 2006
617
20



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
673
20

Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories.
Iris I A Groen, Sennay Ghebreab, Victor A F Lamme, H Steven Scholte. PLoS Comput Biol 2012
36
20



State-of-the-art in visual attention modeling.
Ali Borji, Laurent Itti. IEEE Trans Pattern Anal Mach Intell 2013
249
13





Building machines that learn and think like people.
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, Samuel J Gershman. Behav Brain Sci 2017
188
13

Components of bottom-up gaze allocation in natural images.
Robert J Peters, Asha Iyer, Laurent Itti, Christof Koch. Vision Res 2005
181
13

Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments.
Kamila M Jozwik, Nikolaus Kriegeskorte, Katherine R Storrs, Marieke Mur. Front Psychol 2017
40
13

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

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
88
13




Large-scale two-photon imaging revealed super-sparse population codes in the V1 superficial layer of awake monkeys.
Shiming Tang, Yimeng Zhang, Zhihao Li, Ming Li, Fang Liu, Hongfei Jiang, Tai Sing Lee. Elife 2018
22
13


Identifying natural images from human brain activity.
Kendrick N Kay, Thomas Naselaris, Ryan J Prenger, Jack L Gallant. Nature 2008
543
13

Spatial frequency selectivity of cells in macaque visual cortex.
R L De Valois, D G Albrecht, L G Thorell. Vision Res 1982
932
13


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.