Laurenz Wiskott

Humboldt-Universität zu Berlin, Berlin, Germany 
"Laurenz Wiskott"
Mean distance: 12.26 (cluster 17)
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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
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