| Year |
Citation |
Score |
| 2019 |
Escalante-B. AN, Wiskott L. Improved graph-based SFA: information preservation complements the slowness principle Machine Learning. 109: 999-1037. DOI: 10.1007/s10994-019-05860-9 |
0.429 |
|
| 2018 |
Weghenkel B, Wiskott L. Slowness as a Proxy for Temporal Predictability: An Empirical Comparison. Neural Computation. 1-29. PMID 29566353 DOI: 10.1162/NECO_a_01070 |
0.354 |
|
| 2017 |
Draht F, Zhang S, Rayan A, Schönfeld F, Wiskott L, Manahan-Vaughan D. 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. PMID 28634444 DOI: 10.3389/fnbeh.2017.00092 |
0.312 |
|
| 2017 |
Melchior J, Wang N, Wiskott L. Gaussian-binary restricted Boltzmann machines for modeling natural image statistics. Plos One. 12: e0171015. PMID 28152552 DOI: 10.1371/journal.pone.0171015 |
0.316 |
|
| 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 |
0.705 |
|
| 2017 |
Weghenkel B, Fischer A, Wiskott L. Graph-based predictable feature analysis Machine Learning. 106: 1359-1380. DOI: 10.1007/s10994-017-5632-x |
0.335 |
|
| 2015 |
Schönfeld F, Wiskott L. Modeling place field activity with hierarchical slow feature analysis. Frontiers in Computational Neuroscience. 9: 51. PMID 26052279 DOI: 10.3389/fncom.2015.00051 |
0.43 |
|
| 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 |
0.663 |
|
| 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 |
Azizi AH, Wiskott L, Cheng S. A computational model for preplay in the hippocampus. Frontiers in Computational Neuroscience. 7: 161. PMID 24282402 DOI: 10.3389/fncom.2013.00161 |
0.314 |
|
| 2013 |
Schönfeld F, Wiskott L. RatLab: an easy to use tool for place code simulations. Frontiers in Computational Neuroscience. 7: 104. PMID 23908627 DOI: 10.3389/fncom.2013.00104 |
0.351 |
|
| 2013 |
Krüger N, Janssen P, Kalkan S, Lappe M, Leonardis A, Piater J, Rodríguez-Sánchez AJ, Wiskott L. Deep hierarchies in the primate visual cortex: what can we learn for computer vision? Ieee Transactions On Pattern Analysis and Machine Intelligence. 35: 1847-71. PMID 23787340 DOI: 10.1109/Tpami.2012.272 |
0.366 |
|
| 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 |
0.687 |
|
| 2012 |
Escalante-B. AN, Wiskott L. Slow Feature Analysis: Perspectives for Technical Applications of a Versatile Learning Algorithm Ki - KüNstliche Intelligenz. 26: 341-348. DOI: 10.1007/s13218-012-0190-7 |
0.408 |
|
| 2011 |
Franzius M, Wilbert N, Wiskott L. Invariant object recognition and pose estimation with slow feature analysis. Neural Computation. 23: 2289-323. PMID 21671784 DOI: 10.1162/NECO_a_00171 |
0.312 |
|
| 2011 |
Appleby PA, Kempermann G, Wiskott L. The role of additive neurogenesis and synaptic plasticity in a hippocampal memory model with grid-cell like input. Plos Computational Biology. 7: e1001063. PMID 21298080 DOI: 10.1371/journal.pcbi.1001063 |
0.331 |
|
| 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 |
0.679 |
|
| 2011 |
Wiskott L, Berkes P, Franzius M, Sprekeler H, Wilbert N. Slow feature analysis Scholarpedia. 6: 5282. DOI: 10.4249/Scholarpedia.5282 |
0.741 |
|
| 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 |
0.363 |
|
| 2009 |
Appleby PA, Wiskott L. Additive neurogenesis as a strategy for avoiding interference in a sparsely-coding dentate gyrus. Network (Bristol, England). 20: 137-61. PMID 19731146 DOI: 10.1080/09548980902993156 |
0.322 |
|
| 2009 |
Wilbert N, Legenstein R, Franzius M, Wiskott L. Reinforcement learning on complex visual stimuli Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P90 |
0.357 |
|
| 2009 |
Hinze C, Wilbert N, Wiskott L. Visualization of higher-level receptive fields in a hierarchical model of the visual system Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P158 |
0.373 |
|
| 2009 |
Dähne S, Wilbert N, Wiskott L. Learning complex cell units from simulated prenatal retinal waves with slow feature analysis Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P129 |
0.339 |
|
| 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 |
0.686 |
|
| 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 |
0.55 |
|
| 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 |
0.674 |
|
| 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 |
0.677 |
|
| 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 |
0.686 |
|
| 2007 |
Sprekeler H, Wiskott L. Spike-timing-dependent plasticity and temporal input statistics Bmc Neuroscience. 8. DOI: 10.1186/1471-2202-8-S2-P86 |
0.663 |
|
| 2007 |
Blaschke T, Zito T, Wiskott L. Independent slow feature analysis and nonlinear blind source separation Neural Computation. 19: 994-1021. DOI: 10.1162/Neco.2007.19.4.994 |
0.341 |
|
| 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 |
0.691 |
|
| 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 |
0.697 |
|
| 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 |
0.67 |
|
| 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 |
0.722 |
|
| 2004 |
Blaschke T, Wiskott L. CuBICA: Independent component analysis by simultaneous third- and fourth-order cumulant diagonalization Ieee Transactions On Signal Processing. 52: 1250-1256. DOI: 10.1109/TSP.2004.826173 |
0.306 |
|
| 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 |
0.726 |
|
| 2003 |
Wiskott L. Slow feature analysis: a theoretical analysis of optimal free responses. Neural Computation. 15: 2147-77. PMID 12959670 DOI: 10.1162/089976603322297331 |
0.425 |
|
| 2002 |
Wiskott L, Sejnowski TJ. Slow feature analysis: unsupervised learning of invariances. Neural Computation. 14: 715-70. PMID 11936959 DOI: 10.1162/089976602317318938 |
0.562 |
|
| 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. |
0.669 |
|
| 1999 |
Wiskott L. Learning invariance manifolds Neurocomputing. 26: 925-932. DOI: 10.1016/S0925-2312(99)00011-9 |
0.386 |
|
| 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 |
0.443 |
|
| 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 |
0.56 |
|
| 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. |
0.519 |
|
| 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 |
0.559 |
|
| 1993 |
WISKOTT L, MALSBURG CVD. A NEURAL SYSTEM FOR THE RECOGNITION OF PARTIALLY OCCLUDED OBJECTS IN CLUTTERED SCENES: A PILOT STUDY International Journal of Pattern Recognition and Artificial Intelligence. 7: 935-948. DOI: 10.1142/S0218001493000479 |
0.316 |
|
| 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 |
0.514 |
|
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