A citation-based method for searching scientific literature


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



Times Cited
  Times     Co-cited
Similarity


Natural image statistics and neural representation.
E P Simoncelli, B A Olshausen. Annu Rev Neurosci 2001
918
23



Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Nature 2015
14



Sparse coding of sensory inputs.
Bruno A Olshausen, David J Field. Curr Opin Neurobiol 2004
544
14


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
414
13


Using goal-driven deep learning models to understand sensory cortex.
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
296
10


Efficient coding of natural sounds.
Michael S Lewicki. Nat Neurosci 2002
248
9


The cost of cortical computation.
Peter Lennie. Curr Biol 2003
442
9

Deep supervised, but not unsupervised, models may explain IT cortical representation.
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
309
8

Energy efficient neural codes.
W B Levy, R A Baxter. Neural Comput 1996
220
7

Image denoising via sparse and redundant representations over learned dictionaries.
Michael Elad, Michal Aharon. IEEE Trans Image Process 2006
504
7


Redundancy reduction revisited.
H Barlow. Network 2001
251
7


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
73
9


Information-limiting correlations.
Rubén Moreno-Bote, Jeffrey Beck, Ingmar Kanitscheider, Xaq Pitkow, Peter Latham, Alexandre Pouget. Nat Neurosci 2014
219
6


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
173
6



Normalization as a canonical neural computation.
Matteo Carandini, David J Heeger. Nat Rev Neurosci 2011
751
6

How does the brain solve visual object recognition?
James J DiCarlo, Davide Zoccolan, Nicole C Rust. Neuron 2012
512
6



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
585
6

An energy budget for signaling in the grey matter of the brain.
D Attwell, S B Laughlin. J Cereb Blood Flow Metab 2001
6



Decorrelation and efficient coding by retinal ganglion cells.
Xaq Pitkow, Markus Meister. Nat Neurosci 2012
88
5

Functional specificity of local synaptic connections in neocortical networks.
Ho Ko, Sonja B Hofer, Bruno Pichler, Katherine A Buchanan, P Jesper Sjöström, Thomas D Mrsic-Flogel. Nature 2011
432
5

Efficient auditory coding.
Evan C Smith, Michael S Lewicki. Nature 2006
199
5

How close are we to understanding v1?
Bruno A Olshausen, David J Field. Neural Comput 2005
184
5



The hippocampus as a predictive map.
Kimberly L Stachenfeld, Matthew M Botvinick, Samuel J Gershman. Nat Neurosci 2017
143
5


Toward an Integration of Deep Learning and Neuroscience.
Adam H Marblestone, Greg Wayne, Konrad P Kording. Front Comput Neurosci 2016
140
5



Canonical microcircuits for predictive coding.
Andre M Bastos, W Martin Usrey, Rick A Adams, George R Mangun, Pascal Fries, Karl J Friston. Neuron 2012
830
5

Responses of neurons in primary and inferior temporal visual cortices to natural scenes.
R Baddeley, L F Abbott, M C Booth, F Sengpiel, T Freeman, E A Wakeman, E T Rolls. Proc Biol Sci 1997
238
5

Predictive coding: a fresh view of inhibition in the retina.
M V Srinivasan, S B Laughlin, A Dubs. Proc R Soc Lond B Biol Sci 1982
494
5


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.