| 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 | 881 | 2010 |
| 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 | 879 | 2011 |
| Slow feature analysis yields a rich repertoire of complex cell properties P Berkes, L Wiskott Journal of vision 5 (6), 9-9, 2005 | 406 | 2005 |
| 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 | 317 | 2016 |
| Perceptual decision-making as probabilistic inference by neural sampling RM Haefner, P Berkes, J Fiser Neuron 90 (3), 649-660, 2016 | 229 | 2016 |
| Modular toolkit for Data Processing (MDP): a Python data processing framework T Zito, N Wilbert, L Wiskott, P Berkes Frontiers in neuroinformatics 2, 338, 2009 | 155 | 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 | 133 | 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 | 91 | 2006 |
| On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields P Berkes, L Wiskott Neural computation 18 (8), 1868-1895, 2006 | 67 | 2006 |
| Characterizing neural dependencies with copula models P Berkes, F Wood, J Pillow Advances in neural information processing systems 21, 2008 | 64 | 2008 |
| Slow feature analysis L Wiskott Encyclopedia of Computational Neuroscience, 3142-3143, 2022 | 61 | 2022 |
| A structured model of video reproduces primary visual cortical organisation P Berkes, RE Turner, M Sahani PLoS computational biology 5 (9), e1000495, 2009 | 52 | 2009 |
| Pattern recognition with slow feature analysis P Berkes | 51 | 2005 |
| No evidence for active sparsification in the visual cortex P Berkes, B White, J Fiser Advances in neural information processing systems 22, 2009 | 44 | 2009 |
| 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 | 41 | 2002 |
| On sparsity and overcompleteness in image models P Berkes, R Turner, M Sahani Advances in neural information processing systems 20, 2007 | 32 | 2007 |
| Select and sample-a model of efficient neural inference and learning J Shelton, A Sheikh, P Berkes, J Bornschein, J Lücke Advances in neural information processing systems 24, 2011 | 25 | 2011 |
| Handwritten digit recognition with nonlinear fisher discriminant analysis P Berkes International Conference on Artificial Neural Networks, 285-287, 2005 | 23 | 2005 |
| Discovering Customer Journey Maps using a Mixture of Markov Models. M Harbich, G Bernard, P Berkes, B Garbinato, P Andritsos SIMPDA, 3-7, 2017 | 20 | 2017 |
| Is slowness a learning principle of the visual cortex? L Wiskott, P Berkes Zoology 106 (4), 373-382, 2003 | 18 | 2003 |
