| Year |
Citation |
Score |
| 2016 |
Orbán G, Berkes P, Fiser J, Lengyel M. Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex. Neuron. 92: 530-543. PMID 27764674 DOI: 10.1016/J.Neuron.2016.09.038 |
0.627 |
|
| 2016 |
Haefner RM, Berkes P, Fiser J. Perceptual Decision-Making as Probabilistic Inference by Neural Sampling. Neuron. 90: 649-60. PMID 27146267 DOI: 10.1016/J.Neuron.2016.03.020 |
0.701 |
|
| 2013 |
Fiser J, Savin C, Berkes P, Chiu C, Lengyel M. Experience-based development of internal probabilistic representations in the primary visual cortex Journal of Vision. 13: 600-600. DOI: 10.1167/13.9.600 |
0.597 |
|
| 2013 |
Wilbert N, Zito T, Schuppner RB, Jedrzejewski-Szmek Z, Wiskott L, Berkes P. Building extensible frameworks for data processing: The case of MDP, Modular toolkit for Data Processing Journal of Computational Science. 4: 345-351. DOI: 10.1016/J.Jocs.2011.10.005 |
0.617 |
|
| 2012 |
Haefner RM, Berkes P, Fiser J. The relation of decision-making and endogenous covert attention to sampling-based neural representations Journal of Vision. 12: 159-159. DOI: 10.1167/12.9.159 |
0.69 |
|
| 2011 |
Berkes P, Orbán G, Lengyel M, Fiser J. Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science (New York, N.Y.). 331: 83-7. PMID 21212356 DOI: 10.1126/Science.1195870 |
0.589 |
|
| 2011 |
Wiskott L, Berkes P, Franzius M, Sprekeler H, Wilbert N. Slow feature analysis Scholarpedia. 6: 5282. DOI: 10.4249/Scholarpedia.5282 |
0.688 |
|
| 2011 |
Popovic M, Lisitsyn D, Berkes P, Lengyel M, Fiser J. Uncertainty representation of low-level visual attributes Journal of Vision. 11: 807-807. DOI: 10.1167/11.11.807 |
0.649 |
|
| 2011 |
Turner RE, Berkes P, Fiser J. Learning complex tasks with probabilistic population codes Nature Precedings. DOI: 10.1038/Npre.2011.5838.1 |
0.534 |
|
| 2011 |
Berkes P, Turner RE, Fiser J. The Army of One (Sample): the Characteristics of Sampling-based Probabilistic Neural Representations Nature Precedings. 6: 1-1. DOI: 10.1038/Npre.2011.5811.1 |
0.566 |
|
| 2010 |
Fiser J, Berkes P, Orbán G, Lengyel M. Statistically optimal perception and learning: from behavior to neural representations. Trends in Cognitive Sciences. 14: 119-30. PMID 20153683 DOI: 10.1016/J.Tics.2010.01.003 |
0.601 |
|
| 2009 |
Berkes P, Turner RE, Sahani M. A structured model of video reproduces primary visual cortical organisation. Plos Computational Biology. 5: e1000495. PMID 19730679 DOI: 10.1371/Journal.Pcbi.1000495 |
0.44 |
|
| 2009 |
Berkes P, White BL, Fiser J. No evidence for active sparsification in the visual cortex Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 108-116. |
0.542 |
|
| 2008 |
Zito T, Wilbert N, Wiskott L, Berkes P. Modular Toolkit for Data Processing (MDP): A Python Data Processing Framework. Frontiers in Neuroinformatics. 2: 8. PMID 19169361 DOI: 10.3389/Neuro.11.008.2008 |
0.6 |
|
| 2007 |
Berkes P, Wiskott L. Analysis and interpretation of quadratic models of receptive fields. Nature Protocols. 2: 400-7. PMID 17406601 DOI: 10.1038/Nprot.2007.27 |
0.617 |
|
| 2006 |
Blaschke T, Berkes P, Wiskott L. What is the relation between slow feature analysis and independent component analysis? Neural Computation. 18: 2495-508. PMID 16907634 DOI: 10.1162/Neco.2006.18.10.2495 |
0.613 |
|
| 2006 |
Berkes P, Wiskott L. On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields. Neural Computation. 18: 1868-95. PMID 16771656 DOI: 10.1162/Neco.2006.18.8.1868 |
0.632 |
|
| 2005 |
Berkes P, Wiskott L. Slow feature analysis yields a rich repertoire of complex cell properties. Journal of Vision. 5: 579-602. PMID 16097870 DOI: 10.1167/5.6.9 |
0.65 |
|
| 2003 |
Wiskott L, Berkes P. Is slowness a learning principle of the visual cortex? Zoology (Jena, Germany). 106: 373-82. PMID 16351921 DOI: 10.1078/0944-2006-00132 |
0.676 |
|
| 2002 |
Berkes P, Wiskott L. Applying Slow Feature Analysis to image sequences yields a rich repertoire of complex cell properties Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2415: 81-86. |
0.578 |
|
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