Pietro Berkes
Pietro Berkes
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Cited by
Statistically optimal perception and learning: from behavior to neural representations
J Fiser, P Berkes, G OrbŠn, M Lengyel
Trends in cognitive sciences 14 (3), 119-130, 2010
Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment
P Berkes, G OrbŠn, M Lengyel, J Fiser
Science 331 (6013), 83-87, 2011
Slow feature analysis yields a rich repertoire of complex cell properties
P Berkes, L Wiskott
Journal of vision 5 (6), 9-9, 2005
Neural variability and sampling-based probabilistic representations in the visual cortex
G OrbŠn, P Berkes, J Fiser, M Lengyel
Neuron 92 (2), 530-543, 2016
Perceptual decision-making as probabilistic inference by neural sampling
RM Haefner, P Berkes, J Fiser
Neuron 90 (3), 649-660, 2016
Modular toolkit for Data Processing (MDP): a Python data processing framework
T Zito, N Wilbert, L Wiskott, P Berkes
Frontiers in neuroinformatics, 8, 2009
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
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
On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields
P Berkes, L Wiskott
Neural computation 18 (8), 1868-1895, 2006
Characterizing neural dependencies with copula models
P Berkes, F Wood, J Pillow
Advances in neural information processing systems 21, 2008
Slow feature analysis
L Wiskott, P Berkes, M Franzius, H Sprekeler, N Wilbert
Scholarpedia 6 (4), 5282, 2011
Pattern recognition with slow feature analysis
P Berkes
A structured model of video reproduces primary visual cortical organisation
P Berkes, RE Turner, M Sahani
PLoS computational biology 5 (9), e1000495, 2009
No evidence for active sparsification in the visual cortex
P Berkes, B White, J Fiser
Advances in neural information processing systems 22, 2009
Applying slow feature analysis to image sequences yields a rich repertoire of complex cell properties
P Berkes, L Wiskott
Artificial Neural Networks—ICANN 2002: International Conference Madrid†…, 2002
On sparsity and overcompleteness in image models
P Berkes, R Turner, M Sahani
Advances in neural information processing systems 20, 2007
Select and sample-a model of efficient neural inference and learning
J Shelton, A Sheikh, P Berkes, J Bornschein, J LŁcke
Advances in neural information processing systems 24, 2011
Handwritten digit recognition with nonlinear fisher discriminant analysis
P Berkes
Artificial Neural Networks: Formal Models and Their Applications–ICANN 2005†…, 2005
Is slowness a learning principle of the visual cortex?
L Wiskott, P Berkes
Zoology 106 (4), 373-382, 2003
Discovering Customer Journey Maps using a Mixture of Markov Models.
M Harbich, G Bernard, P Berkes, B Garbinato, P Andritsos
SIMPDA, 3-7, 2017
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