J V Stone. Neural Comput 1996
Times Cited: 29
Times Cited: 29
Times Cited
Times Co-cited
Similarity
Slow feature analysis: unsupervised learning of invariances.
Laurenz Wiskott, Terrence J Sejnowski. Neural Comput 2002
Laurenz Wiskott, Terrence J Sejnowski. Neural Comput 2002
58
Invariant face and object recognition in the visual system.
G Wallis, E T Rolls. Prog Neurobiol 1997
G Wallis, E T Rolls. Prog Neurobiol 1997
44
Self-organizing neural network that discovers surfaces in random-dot stereograms.
S Becker, G E Hinton. Nature 1992
S Becker, G E Hinton. Nature 1992
31
Removing Time Variation with the Anti-Hebbian Differential Synapse.
Graeme Mitchison. Neural Comput 1991
Graeme Mitchison. Neural Comput 1991
34
The "independent components" of natural scenes are edge filters.
A J Bell, T J Sejnowski. Vision Res 1997
A J Bell, T J Sejnowski. Vision Res 1997
27
Emergence of simple-cell receptive field properties by learning a sparse code for natural images.
B A Olshausen, D J Field. Nature 1996
B A Olshausen, D J Field. Nature 1996
27
Learning viewpoint-invariant face representations from visual experience in an attractor network.
M S Bartlett, T J Sejnowski. Network 1998
M S Bartlett, T J Sejnowski. Network 1998
25
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
Wolfgang Einhäuser, Christoph Kayser, Peter König, Konrad P Körding. Eur J Neurosci 2002
24
Neuronal correlate of visual associative long-term memory in the primate temporal cortex.
Y Miyashita. Nature 1988
Y Miyashita. Nature 1988
20
Independent component filters of natural images compared with simple cells in primary visual cortex.
J H van Hateren, A van der Schaaf. Proc Biol Sci 1998
J H van Hateren, A van der Schaaf. Proc Biol Sci 1998
20
Simple-cell-like receptive fields maximize temporal coherence in natural video.
Jarmo Hurri, Aapo Hyvärinen. Neural Comput 2003
Jarmo Hurri, Aapo Hyvärinen. Neural Comput 2003
22
How does the brain solve visual object recognition?
James J DiCarlo, Davide Zoccolan, Nicole C Rust. Neuron 2012
James J DiCarlo, Davide Zoccolan, Nicole C Rust. Neuron 2012
20
Emergence of phase- and shift-invariant features by decomposition of natural images into independent feature subspaces.
A Hyvärinen, P Hoyer. Neural Comput 2000
A Hyvärinen, P Hoyer. Neural Comput 2000
17
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.
D H HUBEL, T N WIESEL. J Physiol 1962
D H HUBEL, T N WIESEL. J Physiol 1962
17
Learning the nonlinearity of neurons from natural visual stimuli.
Christoph Kayser, Konrad P Körding, Peter König. Neural Comput 2003
Christoph Kayser, Konrad P Körding, Peter König. Neural Comput 2003
31
How are complex cell properties adapted to the statistics of natural stimuli?
Konrad P Körding, Christoph Kayser, Wolfgang Einhäuser, Peter König. J Neurophysiol 2004
Konrad P Körding, Christoph Kayser, Wolfgang Einhäuser, Peter König. J Neurophysiol 2004
17
Effects of temporal association on recognition memory.
G Wallis, H H Bülthoff. Proc Natl Acad Sci U S A 2001
G Wallis, H H Bülthoff. Proc Natl Acad Sci U S A 2001
17
Newborn chickens generate invariant object representations at the onset of visual object experience.
Justin N Wood. Proc Natl Acad Sci U S A 2013
Justin N Wood. Proc Natl Acad Sci U S A 2013
18
22
13
A self-organizing multiple-view representation of 3D objects.
S Edelman, D Weinshall. Biol Cybern 1991
S Edelman, D Weinshall. Biol Cybern 1991
13
Optimal, unsupervised learning in invariant object recognition.
G Wallis, R Baddeley. Neural Comput 1997
G Wallis, R Baddeley. Neural Comput 1997
20
Neurophysiological mechanisms underlying face processing within and beyond the temporal cortical visual areas.
E T Rolls. Philos Trans R Soc Lond B Biol Sci 1992
E T Rolls. Philos Trans R Soc Lond B Biol Sci 1992
13
A model of invariant object recognition in the visual system: learning rules, activation functions, lateral inhibition, and information-based performance measures.
E T Rolls, T Milward. Neural Comput 2000
E T Rolls, T Milward. Neural Comput 2000
13
Unsupervised natural experience rapidly alters invariant object representation in visual cortex.
Nuo Li, James J DiCarlo. Science 2008
Nuo Li, James J DiCarlo. Science 2008
13
Characterizing the information content of a newly hatched chick's first visual object representation.
Justin N Wood. Dev Sci 2015
Justin N Wood. Dev Sci 2015
26
Neocortical evolution: neuronal circuits arise independently of lamination.
Harvey J Karten. Curr Biol 2013
Harvey J Karten. Curr Biol 2013
13
A chicken model for studying the emergence of invariant object recognition.
Samantha M W Wood, Justin N Wood. Front Neural Circuits 2015
Samantha M W Wood, Justin N Wood. Front Neural Circuits 2015
26
Avian brains and a new understanding of vertebrate brain evolution.
Erich D Jarvis, Onur Güntürkün, Laura Bruce, András Csillag, Harvey Karten, Wayne Kuenzel, Loreta Medina, George Paxinos, David J Perkel, Toru Shimizu,[...]. Nat Rev Neurosci 2005
Erich D Jarvis, Onur Güntürkün, Laura Bruce, András Csillag, Harvey Karten, Wayne Kuenzel, Loreta Medina, George Paxinos, David J Perkel, Toru Shimizu,[...]. Nat Rev Neurosci 2005
13
Visual statistical learning in the newborn infant.
Hermann Bulf, Scott P Johnson, Eloisa Valenza. Cognition 2011
Hermann Bulf, Scott P Johnson, Eloisa Valenza. Cognition 2011
13
Enhanced learning of natural visual sequences in newborn chicks.
Justin N Wood, Aditya Prasad, Jason G Goldman, Samantha M W Wood. Anim Cogn 2016
Justin N Wood, Aditya Prasad, Jason G Goldman, Samantha M W Wood. Anim Cogn 2016
44
Development of three-dimensional form perception.
P J Kellman, K R Short. J Exp Psychol Hum Percept Perform 1987
P J Kellman, K R Short. J Exp Psychol Hum Percept Perform 1987
10
An information-maximization approach to blind separation and blind deconvolution.
A J Bell, T J Sejnowski. Neural Comput 1995
A J Bell, T J Sejnowski. Neural Comput 1995
10
Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex.
J H van Hateren, D L Ruderman. Proc Biol Sci 1998
J H van Hateren, D L Ruderman. Proc Biol Sci 1998
10
A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images.
A Hyvärinen, P O Hoyer. Vision Res 2001
A Hyvärinen, P O Hoyer. Vision Res 2001
10
Sparse coding with an overcomplete basis set: a strategy employed by V1?
B A Olshausen, D J Field. Vision Res 1997
B A Olshausen, D J Field. Vision Res 1997
10
Natural image statistics and neural representation.
E P Simoncelli, B A Olshausen. Annu Rev Neurosci 2001
E P Simoncelli, B A Olshausen. Annu Rev Neurosci 2001
10
Size and position invariance of neuronal responses in monkey inferotemporal cortex.
M Ito, H Tamura, I Fujita, K Tanaka. J Neurophysiol 1995
M Ito, H Tamura, I Fujita, K Tanaka. J Neurophysiol 1995
10
Implicit learning in 3D object recognition: the importance of temporal context.
S Becker. Neural Comput 1999
S Becker. Neural Comput 1999
18
Recognition-by-components: a theory of human image understanding.
Irving Biederman. Psychol Rev 1987
Irving Biederman. Psychol Rev 1987
10
10
Slow feature analysis: a theoretical analysis of optimal free responses.
Laurenz Wiskott. Neural Comput 2003
Laurenz Wiskott. Neural Comput 2003
13
Invariant object recognition in the visual system with novel views of 3D objects.
Simon M Stringer, Edmund T Rolls. Neural Comput 2002
Simon M Stringer, Edmund T Rolls. Neural Comput 2002
10
Slow feature analysis yields a rich repertoire of complex cell properties.
Pietro Berkes, Laurenz Wiskott. J Vis 2005
Pietro Berkes, Laurenz Wiskott. J Vis 2005
10
Object-centered encoding by face-selective neurons in the cortex in the superior temporal sulcus of the monkey.
M E Hasselmo, E T Rolls, G C Baylis, V Nalwa. Exp Brain Res 1989
M E Hasselmo, E T Rolls, G C Baylis, V Nalwa. Exp Brain Res 1989
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.