Prof. Dr. Laurenz Wiskott

Institut für Neuroinformatik
Ruhr-Universität Bochum
Universitätsstraße 150
Building NB, Room NB 3/29
D-44801 Bochum
Germany
Theory of Neural Systems

Groups

Melchior, J., Wang, N., & Wiskott, L.. (2017). Gaussian-binary restricted Boltzmann machines for modeling natural image statistics. PLOS ONE, 12(2), 1–24. http://doi.org/10.1371/journal.pone.0171015
Draht, F., Zhang, S., Rayan, A., Schönfeld, F., Wiskott, L., & Manahan-Vaughan, D. (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). http://doi.org/10.3389/fnbeh.2017.00092
Kompella, V. R., & Wiskott, L.. (2017). Intrinsically Motivated Acquisition of Modular Slow Features for Humanoids in Continuous and Non-Stationary Environments. arXiv preprint arXiv:1701.04663.
Weghenkel, B., Fischer, A., & Wiskott, L.. (2017). Graph-based predictable feature analysis. Machine Learning, 1–22. http://doi.org/10.1007/s10994-017-5632-x
Escalante-B., A. N., & Wiskott, L.. (2016). Theoretical analysis of the optimal free responses of graph-based SFA for the design of training graphs. Journal of Machine Learning Research, 17(157), 1–36. Retrieved from http://jmlr.org/papers/v17/15-311.html
Escalante-B., A. N., & Wiskott, L.. (2016, January). Improved graph-based SFA: Information preservation complements the slowness principle. e-print arXiv:1601.03945. Retrieved from http://arxiv.org/abs/1601.03945
Melchior, J., Fischer, A., & Wiskott, L.. (2016). How to Center Deep Boltzmann Machines. Journal of Machine Learning Research, 17(99), 1–61. Retrieved from http://jmlr.org/papers/v17/14-237.html
Escalante-B., A. N., & Wiskott, L.. (2015). Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs. e-print arXiv:1509.08329 (Accepted in Journal of Machine Learning Research). Retrieved from http://arxiv.org/abs/1509.08329
Neher, T., Cheng, S., & Wiskott, L.. (2015). Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input Statistics. PLoS Comput Biol, 11, e1004250. http://doi.org/10.1371/journal.pcbi.1004250
Richthofer, S., & Wiskott, L.. (2015). Predictable Feature Analysis. In Workshop New Challenges in Neural Computation 2015 (NC2) (pp. 68–75). Retrieved from https://www.techfak.uni-bielefeld.de/ fschleif/mlr/mlr_03_2015.pdf
Richthofer, S., & Wiskott, L.. (2015). Predictable Feature Analysis. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (pp. 190–196). http://doi.org/10.1109/ICMLA.2015.158
Schoenfeld, F., & Wiskott, L.. (2015). Modeling place field activity with hierarchical slow feature analysis. Frontiers in Computational Neuroscience, 9(51). http://doi.org/10.3389/fncom.2015.00051
Schönfeld, F., & Wiskott, L.. (2015). Modeling place field activity with hierarchical slow feature analysis. frontiers in Computational Neuroscience, 9(51). http://doi.org/10.3389/fncom.2015.00051
Dähne, S., Wilbert, N., & Wiskott, L.. (2014). Slow Feature Analysis on Retinal Waves Leads to V1 Complex Cells. PLoS Comput Biol, 10(5), e1003564. http://doi.org/10.1371/journal.pcbi.1003564
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. Retrieved from http://jmlr.org/papers/v15/sprekeler14a.html
Wang, N., Jancke, D., & Wiskott, L.. (2014). Modeling correlations in spontaneous activity of visual cortex with Gaussian-binary deep Boltzmann machines. In Proc. Bernstein Conference for Computational Neuroscience, Sep 3–5,Göttingen, Germany (pp. 263–264). BFNT Göttingen.
Wang, N., Jancke, D., & Wiskott, L.. (2014). Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines. In Proc. International Conference of Learning Representations (ICLR′14, workshop), Apr 14–16,Banff, Alberta, Canada.
Wang, N., Melchior, J., & Wiskott, L.. (2014). Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image statistics (Vol. 1401.5900). arXiv.org e-Print archive. Retrieved from http://arxiv.org/abs/1401.5900
Weghenkel, B., & Wiskott, L.. (2014). Learning predictive partitions for continuous feature spaces. In Proc. 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 23-25, Bruges, Belgium (pp. 577–582).
Wiskott, L., Würtz, R. P., & Westphal, G. (2014). Elastic Bunch Graph Matching. Scholarpedia, 9, 10587. http://doi.org/10.4249/scholarpedia.10587
Zhang, S., Schoenfeld, F., Wiskott, L., & Manahan-Vaughan, D. (2014). Spatial representations of place cells in darkness are supported by path integration and border information. Frontiers in Behavioral Neuroscience, 8(222). http://doi.org/10.3389/fnbeh.2014.00222
Azizi, A. H., Wiskott, L., & Cheng, S.. (2013). A computational model for preplay in the hippocampus. Frontiers in Computational Neuroscience, 7, 161. http://doi.org/10.3389/fncom.2013.00161
Azizi, A. H., Wiskott, L., & Cheng, S.. (2013). A computational model for preplay in the hippocampus. Frontiers in Computational Neuroscience, 7(161), 1–15. http://doi.org/10.3389/fncom.2013.00161
Escalante-B., A. -N., & Wiskott, L.. (2013). How to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis. Cognitive Sciences EPrint Archive (CogPrints). Retrieved from http://cogprints.org/8966/
Escalante-B., A. N., & Wiskott, L.. (2013). 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, 3683–3719. Retrieved from http://jmlr.org/papers/v14/escalante13a.html
Krüger, N., Janssen, P., Kalkan, S., Lappe, M., Leonardis, A., Piater, J., et al. (2013). Deep Hierarchies in the Primate Visual Cortex: What Can We Learn For Computer Vision? IEEE Trans. on Pattern Analysis and Machine Intelligence, 35(8), 1847–1871. http://doi.org/10.1109/TPAMI.2012.272
Melchior, J., Fischer, A., Wang, N., & Wiskott, L.. (2013). How to Center Binary Restricted Boltzmann Machines (Vol. 1311.1354). arXiv.org e-Print archive. Retrieved from http://arxiv.org/pdf/1311.1354.pdf
Neher, T., Cheng, S., & Wiskott, L.. (2013). Are memories really stored in the hippocampal CA3 region? BoNeuroMed.
Neher, T., Cheng, S., & Wiskott, L.. (2013). Are memories really stored in the hippocampal CA3 region? In Proc. 10th Göttinger Meeting of the German Neuroscience Society, Mar 13-16, Göttingen, Germany (p. 104).
Richthofer, S., & Wiskott, L.. (2013). Predictable Feature Analysis. arXiv.org e-Print archive. Retrieved from http://arxiv.org/abs/1311.2503
Schoenfeld, F., & Wiskott, L.. (2013). RatLab: An easy to use tool for place code simulations. Frontiers in Computational Neuroscience, 7(104). http://doi.org/10.3389/fncom.2013.00104
Wang, N., Jancke, D., & Wiskott, L.. (2013). Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines. arXiv preprint arXiv:1312.6108.
Escalante-B., A. N., & Wiskott, L.. (2012). Slow Feature Analysis: Perspectives for Technical Applications of a Versatile Learning Algorithm. Künstliche Intelligenz [Artificial Intelligence], 26(4), 341–348. Retrieved from http://www.springerlink.com/content/vk3738325250162k/
Schönfeld, F., & Wiskott, L.. (2012). Sensory integration of place and head-direction cells in a virtual environment. Poster at NeuroVisionen 8, 26. Oct 2012, Aachen, Germany.
Schönfeld, F., & Wiskott, L.. (2012). Sensory integration of place and head-direction cells in a virtual environment. Poster at the 8th FENS Forum of Neuroscience, Jul 14–18, Barcelona, Spain.
Wang, N., Melchior, J., & Wiskott, L.. (2012). An Analysis of Gaussian-Binary Restricted Boltzmann Machines for Natural Images. In Proc. 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 25–27, Bruges, Belgium (pp. 287–292).
Escalante, A., & Wiskott, L.. (2011). Heuristic Evaluation of Expansions for Non-Linear Hierarchical Slow Feature Analysis. In Proc. The 10th Intl. Conf. on Machine Learning and Applications (ICMLA′11), Dec 18–21, Honolulu, Hawaii (pp. 133–138). IEEE Computer Society. http://doi.org/10.1109/ICMLA.2011.72
Escalante, A., & Wiskott, L.. (2010). Gender and Age Estimation from Synthetic Face Images with Hierarchical Slow Feature Analysis. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU′10), Jun 28 – Jul 2, Dortmund. Retrieved from http://www.springerlink.com/content/r031104qv7228r35