Laurenz Wiskott
Affiliations: | Humboldt-Universität zu Berlin, Berlin, Germany |
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"Laurenz Wiskott"Mean distance: 12.26 (cluster 17) | S | N | B | C | P |
Parents
Sign in to add mentor| Terrence J. Sejnowski | grad student | HU Berlin | |
| Christoph von der Malsburg | grad student | HU Berlin |
Children
Sign in to add trainee| Malte J. Rasch | research assistant | 2002-2003 | HU Berlin |
| Sven Dähne | research assistant | 2008-2010 | Bernstein Center for Computational Neuroscience (BCCN) Berlin |
| Pietro Berkes | grad student | HU Berlin | |
| Henning Sprekeler | grad student | 2004-2008 | HU Berlin |
| Kai Görgen | grad student | 2007-2010 | BCCN Berlin |
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Publications
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| Escalante-B. AN, Wiskott L. (2019) Improved graph-based SFA: information preservation complements the slowness principle Machine Learning. 109: 999-1037 |
| Weghenkel B, Wiskott L. (2018) Slowness as a Proxy for Temporal Predictability: An Empirical Comparison. Neural Computation. 1-29 |
| Draht F, Zhang S, Rayan A, et al. (2017) Experience-Dependency of Reliance on Local Visual and Idiothetic Cues for Spatial Representations Created in the Absence of Distal Information. Frontiers in Behavioral Neuroscience. 11: 92 |
| Melchior J, Wang N, Wiskott L. (2017) Gaussian-binary restricted Boltzmann machines for modeling natural image statistics. Plos One. 12: e0171015 |
| Rubchinsky LL, Ahn S, Klijn W, et al. (2017) 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2 Bmc Neuroscience. 18 |
| Weghenkel B, Fischer A, Wiskott L. (2017) Graph-based predictable feature analysis Machine Learning. 106: 1359-1380 |
| Schönfeld F, Wiskott L. (2015) Modeling place field activity with hierarchical slow feature analysis. Frontiers in Computational Neuroscience. 9: 51 |
| Dähne S, Wilbert N, Wiskott L. (2014) Slow feature analysis on retinal waves leads to V1 complex cells. Plos Computational Biology. 10: e1003564 |
| Sprekeler H, Zito T, Wiskott L. (2014) An extension of slow feature analysis for nonlinear blind source separation Journal of Machine Learning Research. 15: 921-947 |
| Azizi AH, Wiskott L, Cheng S. (2013) A computational model for preplay in the hippocampus. Frontiers in Computational Neuroscience. 7: 161 |