A citation-based method for searching scientific literature

Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
Times Cited: 339







List of co-cited articles
730 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
468
37


Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Nature 2015
32

Deep supervised, but not unsupervised, models may explain IT cortical representation.
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
352
27

A deep learning framework for neuroscience.
Blake A Richards, Timothy P Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath, Rui Ponte Costa, Archy de Berker, Surya Ganguli,[...]. Nat Neurosci 2019
112
24


Neural population control via deep image synthesis.
Pouya Bashivan, Kohitij Kar, James J DiCarlo. Science 2019
68
25

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
71
22

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
178
16


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
77
16

Deep Neural Networks as Scientific Models.
Radoslaw M Cichy, Daniel Kaiser. Trends Cogn Sci 2019
54
24

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
43
27

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
193
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
67
16

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
60
18

A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy.
Alexander J E Kell, Daniel L K Yamins, Erica N Shook, Sam V Norman-Haignere, Josh H McDermott. Neuron 2018
79
13

Long short-term memory.
S Hochreiter, J Schmidhuber. Neural Comput 1997
10


Context-dependent computation by recurrent dynamics in prefrontal cortex.
Valerio Mante, David Sussillo, Krishna V Shenoy, William T Newsome. Nature 2013
554
10

Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.
Courtney J Spoerer, Patrick McClure, Nikolaus Kriegeskorte. Front Psychol 2017
41
24

Untangling invariant object recognition.
James J DiCarlo, David D Cox. Trends Cogn Sci 2007
357
10

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
117
10

Natural image statistics and neural representation.
E P Simoncelli, B A Olshausen. Annu Rev Neurosci 2001
947
10


Normalization as a canonical neural computation.
Matteo Carandini, David J Heeger. Nat Rev Neurosci 2011
792
9


Representational similarity analysis - connecting the branches of systems neuroscience.
Nikolaus Kriegeskorte, Marieke Mur, Peter Bandettini. Front Syst Neurosci 2008
9

Neuroscience-Inspired Artificial Intelligence.
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, Matthew Botvinick. Neuron 2017
177
8

Encoding and decoding in fMRI.
Thomas Naselaris, Kendrick N Kay, Shinji Nishimoto, Jack L Gallant. Neuroimage 2011
307
8

Hierarchical models of object recognition in cortex.
M Riesenhuber, T Poggio. Nat Neurosci 1999
8



Deep Learning: The Good, the Bad, and the Ugly.
Thomas Serre. Annu Rev Vis Sci 2019
31
25

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
276
8

How does the brain solve visual object recognition?
James J DiCarlo, Davide Zoccolan, Nicole C Rust. Neuron 2012
538
8

Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision.
Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu. Cereb Cortex 2018
48
14


Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences.
Carlos R Ponce, Will Xiao, Peter F Schade, Till S Hartmann, Gabriel Kreiman, Margaret S Livingstone. Cell 2019
46
15

Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
Jonas Kubilius, Stefania Bracci, Hans P Op de Beeck. PLoS Comput Biol 2016
80
8



A neural network that finds a naturalistic solution for the production of muscle activity.
David Sussillo, Mark M Churchland, Matthew T Kaufman, Krishna V Shenoy. Nat Neurosci 2015
149
7



Task representations in neural networks trained to perform many cognitive tasks.
Guangyu Robert Yang, Madhura R Joglekar, H Francis Song, William T Newsome, Xiao-Jing Wang. Nat Neurosci 2019
67
8


Theories of Error Back-Propagation in the Brain.
James C R Whittington, Rafal Bogacz. Trends Cogn Sci 2019
48
12

Deep image reconstruction from human brain activity.
Guohua Shen, Tomoyasu Horikawa, Kei Majima, Yukiyasu Kamitani. PLoS Comput Biol 2019
20
30



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.