A citation-based method for searching scientific literature

D H HUBEL, T N WIESEL. J Physiol 1959
Times Cited: 1915







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



Times Cited
  Times     Co-cited
Similarity





Ultrasensitive fluorescent proteins for imaging neuronal activity.
Tsai-Wen Chen, Trevor J Wardill, Yi Sun, Stefan R Pulver, Sabine L Renninger, Amy Baohan, Eric R Schreiter, Rex A Kerr, Michael B Orger, Vivek Jayaraman,[...]. Nature 2013
7

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
300
7

Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Nature 2015
7

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

DeepLabCut: markerless pose estimation of user-defined body parts with deep learning.
Alexander Mathis, Pranav Mamidanna, Kevin M Cury, Taiga Abe, Venkatesh N Murthy, Mackenzie Weygandt Mathis, Matthias Bethge. Nat Neurosci 2018
885
6


Fully integrated silicon probes for high-density recording of neural activity.
James J Jun, Nicholas A Steinmetz, Joshua H Siegle, Daniel J Denman, Marius Bauza, Brian Barbarits, Albert K Lee, Costas A Anastassiou, Alexandru Andrei, Çağatay Aydın,[...]. Nature 2017
665
5





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
99
5

Using goal-driven deep learning models to understand sensory cortex.
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
430
5

Dimensionality reduction for large-scale neural recordings.
John P Cunningham, Byron M Yu. Nat Neurosci 2014
428
4

Prefrontal cortex as a meta-reinforcement learning system.
Jane X Wang, Zeb Kurth-Nelson, Dharshan Kumaran, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Demis Hassabis, Matthew Botvinick. Nat Neurosci 2018
147
4

High-dimensional geometry of population responses in visual cortex.
Carsen Stringer, Marius Pachitariu, Nicholas Steinmetz, Matteo Carandini, Kenneth D Harris. Nature 2019
137
4

Neuroscience Needs Behavior: Correcting a Reductionist Bias.
John W Krakauer, Asif A Ghazanfar, Alex Gomez-Marin, Malcolm A MacIver, David Poeppel. Neuron 2017
479
4

Single-trial neural dynamics are dominated by richly varied movements.
Simon Musall, Matthew T Kaufman, Ashley L Juavinett, Steven Gluf, Anne K Churchland. Nat Neurosci 2019
263
4

A map of object space in primate inferotemporal cortex.
Pinglei Bao, Liang She, Mason McGill, Doris Y Tsao. Nature 2020
69
5

Long short-term memory.
S Hochreiter, J Schmidhuber. Neural Comput 1997
4


Spontaneous behaviors drive multidimensional, brainwide activity.
Carsen Stringer, Marius Pachitariu, Nicholas Steinmetz, Charu Bai Reddy, Matteo Carandini, Kenneth D Harris. Science 2019
379
4

Local diversity and fine-scale organization of receptive fields in mouse visual cortex.
Vincent Bonin, Mark H Histed, Sergey Yurgenson, R Clay Reid. J Neurosci 2011
155
4


Mastering the game of Go with deep neural networks and tree search.
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot,[...]. Nature 2016
4


Dendritic organization of sensory input to cortical neurons in vivo.
Hongbo Jia, Nathalie L Rochefort, Xiaowei Chen, Arthur Konnerth. Nature 2010
329
4




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
69
5

Speed of processing in the human visual system.
S Thorpe, D Fize, C Marlot. Nature 1996
4




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
587
4

Human-level control through deep reinforcement learning.
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski,[...]. Nature 2015
4

Towards the neural population doctrine.
Shreya Saxena, John P Cunningham. Curr Opin Neurobiol 2019
69
5

Deep supervised, but not unsupervised, models may explain IT cortical representation.
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
424
4

Context-dependent computation by recurrent dynamics in prefrontal cortex.
Valerio Mante, David Sussillo, Krishna V Shenoy, William T Newsome. Nature 2013
654
4

Microstructure of a spatial map in the entorhinal cortex.
Torkel Hafting, Marianne Fyhn, Sturla Molden, May-Britt Moser, Edvard I Moser. Nature 2005
4

Neural population dynamics during reaching.
Mark M Churchland, John P Cunningham, Matthew T Kaufman, Justin D Foster, Paul Nuyujukian, Stephen I Ryu, Krishna V Shenoy. Nature 2012
623
4

Spatio-temporal correlations and visual signalling in a complete neuronal population.
Jonathan W Pillow, Jonathon Shlens, Liam Paninski, Alexander Sher, Alan M Litke, E J Chichilnisky, Eero P Simoncelli. Nature 2008
655
4


Neuroscience-Inspired Artificial Intelligence.
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, Matthew Botvinick. Neuron 2017
263
3

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
154
3

Fast animal pose estimation using deep neural networks.
Talmo D Pereira, Diego E Aldarondo, Lindsay Willmore, Mikhail Kislin, Samuel S-H Wang, Mala Murthy, Joshua W Shaevitz. Nat Methods 2019
178
3


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.