A citation-based method for searching scientific literature

Laurenz Wiskott, Terrence J Sejnowski. Neural Comput 2002
Times Cited: 208







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



Times Cited
  Times     Co-cited
Similarity


Learning Invariance from Transformation Sequences.
Peter Földiák. Neural Comput 1991
241
38


How does the brain solve visual object recognition?
James J DiCarlo, Davide Zoccolan, Nicole C Rust. Neuron 2012
531
23

Slowness and sparseness lead to place, head-direction, and spatial-view cells.
Mathias Franzius, Henning Sprekeler, Laurenz Wiskott. PLoS Comput Biol 2007
68
32



A model of the ventral visual system based on temporal stability and local memory.
Reto Wyss, Peter König, Paul F M J Verschure. PLoS Biol 2006
61
29


Hierarchical models of object recognition in cortex.
M Riesenhuber, T Poggio. Nat Neurosci 1999
17




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


Invariant object recognition and pose estimation with slow feature analysis.
Mathias Franzius, Niko Wilbert, Laurenz Wiskott. Neural Comput 2011
16
75

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
444
12

Effects of temporal association on recognition memory.
G Wallis, H H Bülthoff. Proc Natl Acad Sci U S A 2001
121
12

'Breaking' position-invariant object recognition.
David D Cox, Philip Meier, Nadja Oertelt, James J DiCarlo. Nat Neurosci 2005
92
13

Robust object recognition with cortex-like mechanisms.
Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, Tomaso Poggio. IEEE Trans Pattern Anal Mach Intell 2007
307
11

A feedforward architecture accounts for rapid categorization.
Thomas Serre, Aude Oliva, Tomaso Poggio. Proc Natl Acad Sci U S A 2007
409
11


Spatiotemporal energy models for the perception of motion.
E H Adelson, J R Bergen. J Opt Soc Am A 1985
10


Learning invariant object recognition in the visual system with continuous transformations.
S M Stringer, G Perry, E T Rolls, J H Proske. Biol Cybern 2006
51
17

Learning and disrupting invariance in visual recognition with a temporal association rule.
Leyla Isik, Joel Z Leibo, Tomaso Poggio. Front Comput Neurosci 2012
20
45

Deep supervised, but not unsupervised, models may explain IT cortical representation.
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
335
9


Untangling invariant object recognition.
James J DiCarlo, David D Cox. Trends Cogn Sci 2007
350
9

Hierarchical Bayesian inference in the visual cortex.
Tai Sing Lee, David Mumford. J Opt Soc Am A Opt Image Sci Vis 2003
519
8

How are complex cell properties adapted to the statistics of natural stimuli?
Konrad P Körding, Christoph Kayser, Wolfgang Einhäuser, Peter König. J Neurophysiol 2004
50
16


The neuronal encoding of information in the brain.
Edmund T Rolls, Alessandro Treves. Prog Neurobiol 2011
114
8





Learning the invariance properties of complex cells from their responses to natural stimuli.
Wolfgang Einhäuser, Christoph Kayser, Peter König, Konrad P Körding. Eur J Neurosci 2002
42
16






Models of object recognition.
M Riesenhuber, T Poggio. Nat Neurosci 2000
295
7



Fast readout of object identity from macaque inferior temporal cortex.
Chou P Hung, Gabriel Kreiman, Tomaso Poggio, James J DiCarlo. Science 2005
438
7

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
185
7

A chicken model for studying the emergence of invariant object recognition.
Samantha M W Wood, Justin N Wood. Front Neural Circuits 2015
15
46

Enhanced learning of natural visual sequences in newborn chicks.
Justin N Wood, Aditya Prasad, Jason G Goldman, Samantha M W Wood. Anim Cogn 2016
9
77

The development of newborn object recognition in fast and slow visual worlds.
Justin N Wood, Samantha M W Wood. Proc Biol Sci 2016
10
70


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.