Pietro Berkes
Pietro Berkes
Unknown affiliation
Verified email at brandeis.edu
Title
Cited by
Cited by
Year
Statistically optimal perception and learning: from behavior to neural representations
J Fiser, P Berkes, G Orbán, M Lengyel
Trends in cognitive sciences 14 (3), 119-130, 2010
5682010
Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment
P Berkes, G Orbán, M Lengyel, J Fiser
Science 331 (6013), 83-87, 2011
5582011
Slow feature analysis yields a rich repertoire of complex cell properties
P Berkes, L Wiskott
Journal of vision 5 (6), 9-9, 2005
3182005
Modular toolkit for Data Processing (MDP): a Python data processing framework
T Zito, N Wilbert, L Wiskott, P Berkes
Frontiers in neuroinformatics 2, 8, 2009
1352009
Neural variability and sampling-based probabilistic representations in the visual cortex
G Orbán, P Berkes, J Fiser, M Lengyel
Neuron 92 (2), 530-543, 2016
1202016
Perceptual decision-making as probabilistic inference by neural sampling
RM Haefner, P Berkes, J Fiser
Neuron 90 (3), 649-660, 2016
1112016
Improved constraints on cosmological parameters from Type Ia supernova data
MC March, R Trotta, P Berkes, GD Starkman, PM Vaudrevange
Monthly Notices of the Royal Astronomical Society 418 (4), 2308-2329, 2011
902011
What is the relation between slow feature analysis and independent component analysis?
T Blaschke, P Berkes, L Wiskott
Neural computation 18 (10), 2495-2508, 2006
582006
On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields
P Berkes, L Wiskott
Neural computation 18 (8), 1868-1895, 2006
512006
Characterizing neural dependencies with copula models
P Berkes, F Wood, JW Pillow
Advances in neural information processing systems, 129-136, 2009
482009
Pattern recognition with slow feature analysis
P Berkes
452005
A structured model of video reproduces primary visual cortical organisation
P Berkes, RE Turner, M Sahani
PLoS computational biology 5 (9), e1000495, 2009
422009
Applying slow feature analysis to image sequences yields a rich repertoire of complex cell properties
P Berkes, L Wiskott
International Conference on Artificial Neural Networks, 81-86, 2002
352002
No evidence for active sparsification in the visual cortex
P Berkes, B White, J Fiser
Advances in neural information processing systems, 108-116, 2009
322009
Slow feature analysis
L Wiskott, P Berkes, M Franzius, H Sprekeler, N Wilbert
Scholarpedia 6 (4), 5282, 2011
282011
On sparsity and overcompleteness in image models
P Berkes, R Turner, M Sahani
Advances in neural information processing systems, 89-96, 2008
272008
Handwritten digit recognition with nonlinear fisher discriminant analysis
P Berkes
International Conference on Artificial Neural Networks, 285-287, 2005
212005
Select and sample-a model of efficient neural inference and learning
JA Shelton, AS Sheikh, P Berkes, J Bornschein, J Lücke
Advances in neural information processing systems, 2618-2626, 2011
192011
Is slowness a learning principle of the visual cortex?
L Wiskott, P Berkes
Zoology 106 (4), 373-382, 2003
152003
Analysis and interpretation of quadratic models of receptive fields
P Berkes, L Wiskott
Nature protocols 2 (2), 400, 2007
122007
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