Year |
Citation |
Score |
2015 |
Neher T, Cheng S, Wiskott L. Memory storage fidelity in the hippocampal circuit: the role of subregions and input statistics. Plos Computational Biology. 11: e1004250. PMID 25954996 DOI: 10.1371/journal.pcbi.1004250 |
1 |
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2015 |
Schönfeld F, Wiskott L. Modeling place field activity with hierarchical slow feature analysis Frontiers in Computational Neuroscience. 9. DOI: 10.3389/fncom.2015.00051 |
1 |
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2014 |
Zhang S, Schönfeld F, Wiskott L, Manahan-Vaughan D. Spatial representations of place cells in darkness are supported by path integration and border information. Frontiers in Behavioral Neuroscience. 8: 222. PMID 25009477 DOI: 10.3389/fnbeh.2014.00222 |
1 |
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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 |
1 |
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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. |
1 |
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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 |
1 |
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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 |
1 |
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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 |
1 |
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2013 |
Minh HQ, Wiskott L. Multivariate slow feature analysis and decorrelation filtering for blind source separation. Ieee Transactions On Image Processing : a Publication of the Ieee Signal Processing Society. 22: 2737-50. PMID 23591489 DOI: 10.1109/TIP.2013.2257808 |
1 |
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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 |
1 |
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2013 |
Escalante-B. AN, Wiskott L. How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis Journal of Machine Learning Research. 14: 3686-3719. |
1 |
|
2012 |
Wang N, Melchior J, Wiskott L. An analysis of gaussian-binary restricted boltzmann machines for natural images Esann 2012 Proceedings, 20th European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning. 287-292. |
1 |
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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 |
1 |
|
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 |
1 |
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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 |
1 |
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2011 |
Escalante-B. AN, Wiskott L. Heuristic evaluation of expansions for non-linear hierarchical slow feature analysis Proceedings - 10th International Conference On Machine Learning and Applications, Icmla 2011. 1: 133-138. DOI: 10.1109/ICMLA.2011.72 |
1 |
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2011 |
Minh HQ, Wiskott L. Slow feature analysis and decorrelation filtering for separating correlated sources Proceedings of the Ieee International Conference On Computer Vision. 866-873. DOI: 10.1109/ICCV.2011.6126327 |
1 |
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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 |
1 |
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2010 |
Escalante ANB, Wiskott L. Gender and age estimation from synthetic face images Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6178: 240-249. DOI: 10.1007/978-3-642-14049-5_25 |
1 |
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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 |
1 |
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2009 |
Wiskott L. How Does Our Visual System Achieve Shift and Size Invariance? 23 Problems in Systems Neuroscience. DOI: 10.1093/acprof:oso/9780195148220.003.0016 |
1 |
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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 |
1 |
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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 |
1 |
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2008 |
Franzius M, Wilbert N, Wiskott L. Invariant object recognition with slow feature analysis Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5163: 961-970. DOI: 10.1007/978-3-540-87536-9_98 |
1 |
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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 |
0.48 |
|
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 |
0.48 |
|
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 |
1 |
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2007 |
Franzius M, Vollgraf R, Wiskott L. From grids to places. Journal of Computational Neuroscience. 22: 297-9. PMID 17195112 DOI: 10.1007/s10827-006-0013-7 |
0.48 |
|
2007 |
Blaschke T, Zito T, Wiskott L. Independent slow feature analysis and nonlinear blind source separation Neural Computation. 19: 994-1021. |
1 |
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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 |
1 |
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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 |
1 |
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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 |
1 |
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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 |
1 |
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2005 |
Blaschke T, Wiskott L. Nonlinear blind source separation by integrating independent component analysis and slow feature analysis Advances in Neural Information Processing Systems. |
1 |
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2004 |
Kempermann G, Wiskott L, Gage FH. Functional significance of adult neurogenesis. Current Opinion in Neurobiology. 14: 186-91. PMID 15082323 DOI: 10.1016/j.conb.2004.03.001 |
1 |
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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 |
1 |
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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 |
1 |
|
2003 |
Wiskott L. Slow feature analysis: a theoretical analysis of optimal free responses. Neural Computation. 15: 2147-77. PMID 12959670 DOI: 10.1162/089976603322297331 |
1 |
|
2002 |
Wiskott L, Sejnowski TJ. Slow feature analysis: unsupervised learning of invariances. Neural Computation. 14: 715-70. PMID 11936959 DOI: 10.1162/089976602317318938 |
1 |
|
2002 |
Blaschke T, Wiskott L. An improved cumulant based method for independent component analysis Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2415: 1087-1093. |
1 |
|
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. |
1 |
|
1999 |
Wiskott L. Learning invariance manifolds Neurocomputing. 26: 925-932. DOI: 10.1016/S0925-2312(99)00011-9 |
1 |
|
1999 |
Wiskott L. Segmentation from motion: Combining Gabor- and Mallat-wavelets to overcome the aperture and correspondence problems Pattern Recognition. 32: 1751-1766. |
1 |
|
1999 |
Wiskott L. The role of topographical constraints in face recognition Pattern Recognition Letters. 20: 89-96. |
1 |
|
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 |
1 |
|
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 |
1 |
|
1997 |
Wiskott L. Phantom faces for face analysis Pattern Recognition. 30: 837-846. |
1 |
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1997 |
Wiskott L, Sejnowski T. Objective functions for neural map formation Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1327: 243-248. |
1 |
|
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. |
1 |
|
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 |
1 |
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1996 |
Pötzsch M, Maurer T, Wiskott L, Malsburg CVD. Reconstruction from graphs labeled with responses of Gabor filters Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1112: 845-850. DOI: 10.1007/3-540-61510-5_142 |
1 |
|
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 |
0.72 |
|
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