Laurenz Wiskott - Publications

Affiliations: 
Humboldt-Universität zu Berlin, Berlin, Germany 

25 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2017 Rubchinsky LL, Ahn S, Klijn W, Cumming B, Yates S, Karakasis V, Peyser A, Woodman M, Diaz-Pier S, Deraeve J, Vassena E, Alexander W, Beeman D, Kudela P, Boatman-Reich D, ... ... Wiskott L, et al. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2 Bmc Neuroscience. 18. DOI: 10.1186/S12868-017-0371-2  0.712
2014 Dähne S, Wilbert N, Wiskott L. Slow feature analysis on retinal waves leads to V1 complex cells. Plos Computational Biology. 10: e1003564. PMID 24810948 DOI: 10.1371/journal.pcbi.1003564  0.613
2014 Sprekeler H, Zito T, Wiskott L. An extension of slow feature analysis for nonlinear blind source separation Journal of Machine Learning Research. 15: 921-947.  0.622
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  0.667
2011 Sprekeler H, Wiskott L. A theory of slow feature analysis for transformation-based input signals with an application to complex cells. Neural Computation. 23: 303-35. PMID 21105830 DOI: 10.1162/NECO_a_00072  0.649
2011 Wiskott L, Berkes P, Franzius M, Sprekeler H, Wilbert N. Slow feature analysis Scholarpedia. 6: 5282. DOI: 10.4249/Scholarpedia.5282  0.726
2010 Legenstein R, Wilbert N, Wiskott L. Reinforcement learning on slow features of high-dimensional input streams. Plos Computational Biology. 6. PMID 20808883 DOI: 10.1371/journal.pcbi.1000894  0.312
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  0.667
2008 Goodhill G, Baker C, Balasubramanian V, Bazhenov M, Beck J, Becker S, Bethge M, Boahen K, Boden M, Bonin V, Bouret S, Fairhall A, Flash T, French R, Gillies A, ... ... Wiskott L, et al. Network: Computation in Neural Systems: Editorial Network: Computation in Neural Systems. 19: 1-2. DOI: 10.1080/09548980801915409  0.556
2007 Franzius M, Sprekeler H, Wiskott L. Slowness and sparseness lead to place, head-direction, and spatial-view cells. Plos Computational Biology. 3: e166. PMID 17784780 DOI: 10.1371/journal.pcbi.0030166  0.651
2007 Sprekeler H, Michaelis C, Wiskott L. Slowness: an objective for spike-timing-dependent plasticity? Plos Computational Biology. 3: e112. PMID 17604445 DOI: 10.1371/journal.pcbi.0030112  0.66
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  0.667
2007 Sprekeler H, Wiskott L. Spike-timing-dependent plasticity and temporal input statistics Bmc Neuroscience. 8. DOI: 10.1186/1471-2202-8-S2-P86  0.654
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  0.667
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  0.667
2006 Wiskott L, Rasch MJ, Kempermann G. A functional hypothesis for adult hippocampal neurogenesis: avoidance of catastrophic interference in the dentate gyrus. Hippocampus. 16: 329-43. PMID 16435309 DOI: 10.1002/Hipo.20167  0.672
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  0.667
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  0.667
2002 Wiskott L, Sejnowski TJ. Slow feature analysis: unsupervised learning of invariances. Neural Computation. 14: 715-70. PMID 11936959 DOI: 10.1162/089976602317318938  0.5
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.  0.649
1998 Wiskott L, Sejnowski T. Constrained optimization for neural map formation: a unifying framework for weight growth and normalization. Neural Computation. 10: 671-716. PMID 9527838 DOI: 10.1162/089976698300017700  0.44
1997 Wiskott L, Fellous JM, Krüger N, Von Malsburg CD. Face recognition by elastic bunch graph matching Ieee Transactions On Pattern Analysis and Machine Intelligence. 19: 775-779. DOI: 10.1109/34.598235  0.556
1997 Wiskott L, Fellous JM, Krueger N, von der Malsburg C. Face recognition by elastic bunch graph matching Ieee International Conference On Image Processing. 1: 129-132.  0.526
1996 Wiskott L, von der Malsburg C. Recognizing faces by dynamic link matching. Neuroimage. 4: S14-8. PMID 9345518 DOI: 10.1006/nimg.1996.0043  0.568
1990 De Maeyer L, Di Nicola A, Maetche R, von der Malsburg C, Wiskott L. An experimental multiprocessor system for distributed parallel computations Microprocessing and Microprogramming. 26: 305-317. DOI: 10.1016/0165-6074(90)90330-C  0.524
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