A citation-based method for searching scientific literature

William F Kindel, Elijah D Christensen, Joel Zylberberg. J Vis 2019
Times Cited: 10







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



Times Cited
  Times     Co-cited
Similarity


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
37
70

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
436
60

Using goal-driven deep learning models to understand sensory cortex.
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
314
60

Deep supervised, but not unsupervised, models may explain IT cortical representation.
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
328
60

Flexible gating of contextual influences in natural vision.
Ruben Coen-Cagli, Adam Kohn, Odelia Schwartz. Nat Neurosci 2015
67
40


Deep Learning Models of the Retinal Response to Natural Scenes.
Lane T McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen A Baccus. Adv Neural Inf Process Syst 2016
41
40

Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes.
Ján Antolík, Sonja B Hofer, James A Bednar, Thomas D Mrsic-Flogel. PLoS Comput Biol 2016
16
40

Inferring single-trial neural population dynamics using sequential auto-encoders.
Chethan Pandarinath, Daniel J O'Shea, Jasmine Collins, Rafal Jozefowicz, Sergey D Stavisky, Jonathan C Kao, Eric M Trautmann, Matthew T Kaufman, Stephen I Ryu, Leigh R Hochberg,[...]. Nat Methods 2018
88
30

Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Nature 2015
30

Convolutional neural network models of V1 responses to complex patterns.
Yimeng Zhang, Tai Sing Lee, Ming Li, Fang Liu, Shiming Tang. J Comput Neurosci 2019
8
37

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
182
30

Spatiotemporal energy models for the perception of motion.
E H Adelson, J R Bergen. J Opt Soc Am A 1985
30

Untangling invariant object recognition.
James J DiCarlo, David D Cox. Trends Cogn Sci 2007
344
30

Natural image statistics and neural representation.
E P Simoncelli, B A Olshausen. Annu Rev Neurosci 2001
930
30


Correlated variability modifies working memory fidelity in primate prefrontal neuronal ensembles.
Matthew L Leavitt, Florian Pieper, Adam J Sachs, Julio C Martinez-Trujillo. Proc Natl Acad Sci U S A 2017
34
20


The Nature of Shared Cortical Variability.
I-Chun Lin, Michael Okun, Matteo Carandini, Kenneth D Harris. Neuron 2015
109
20

Correlations and Neuronal Population Information.
Adam Kohn, Ruben Coen-Cagli, Ingmar Kanitscheider, Alexandre Pouget. Annu Rev Neurosci 2016
121
20

Neural population control via deep image synthesis.
Pouya Bashivan, Kohitij Kar, James J DiCarlo. Science 2019
57
20

A Convolutional Subunit Model for Neuronal Responses in Macaque V1.
Brett Vintch, J Anthony Movshon, Eero P Simoncelli. J Neurosci 2015
35
20

Using human brain activity to guide machine learning.
Ruth C Fong, Walter J Scheirer, David D Cox. Sci Rep 2018
11
20

A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging.
Nicholas James Sofroniew, Daniel Flickinger, Jonathan King, Karel Svoboda. Elife 2016
203
20

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
109
20

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
38
20

Inferring nonlinear neuronal computation based on physiologically plausible inputs.
James M McFarland, Yuwei Cui, Daniel A Butts. PLoS Comput Biol 2013
75
20

Measuring and interpreting neuronal correlations.
Marlene R Cohen, Adam Kohn. Nat Neurosci 2011
505
20


Population code in mouse V1 facilitates readout of natural scenes through increased sparseness.
Emmanouil Froudarakis, Philipp Berens, Alexander S Ecker, R James Cotton, Fabian H Sinz, Dimitri Yatsenko, Peter Saggau, Matthias Bethge, Andreas S Tolias. Nat Neurosci 2014
82
20

Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.
Courtney J Spoerer, Patrick McClure, Nikolaus Kriegeskorte. Front Psychol 2017
35
20

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
73
20


Learning to see stuff.
Roland W Fleming, Katherine R Storrs. Curr Opin Behav Sci 2019
11
20

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
62
20

Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
Jonas Kubilius, Stefania Bracci, Hans P Op de Beeck. PLoS Comput Biol 2016
75
20

Building machines that learn and think like people.
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, Samuel J Gershman. Behav Brain Sci 2017
146
20

A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex.
Saskia E J de Vries, Jerome A Lecoq, Michael A Buice, Peter A Groblewski, Gabriel K Ocker, Michael Oliver, David Feng, Nicholas Cain, Peter Ledochowitsch, Daniel Millman,[...]. Nat Neurosci 2020
47
20

Natural signal statistics and sensory gain control.
O Schwartz, E P Simoncelli. Nat Neurosci 2001
416
20


Normalization as a canonical neural computation.
Matteo Carandini, David J Heeger. Nat Rev Neurosci 2011
767
20

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
164
20


Representational similarity analysis - connecting the branches of systems neuroscience.
Nikolaus Kriegeskorte, Marieke Mur, Peter Bandettini. Front Syst Neurosci 2008
20

Temporal precision in the neural code and the timescales of natural vision.
Daniel A Butts, Chong Weng, Jianzhong Jin, Chun-I Yeh, Nicholas A Lesica, Jose-Manuel Alonso, Garrett B Stanley. Nature 2007
194
10


Motor control by precisely timed spike patterns.
Kyle H Srivastava, Caroline M Holmes, Michiel Vellema, Andrea R Pack, Coen P H Elemans, Ilya Nemenman, Samuel J Sober. Proc Natl Acad Sci U S A 2017
39
10


Neural correlations, population coding and computation.
Bruno B Averbeck, Peter E Latham, Alexandre Pouget. Nat Rev Neurosci 2006
823
10



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.