Prof. Dr. Laurenz Wiskott
Theory of Neural Systems
Theory of Neural Systems
Ruhr-Universität Bochum
Institut für Neuroinformatik
Universitätsstraße 150
Building NB, Room NB 3/29
Universitätsstraße 150
Building NB, Room NB 3/29
D-44801 Bochum, Germany
Affiliations at the Ruhr-University
Department of Physics and Astronomy
Department of Electrical Engineering and Information Sciences
Research Department of Neuroscience
International Graduate School of Neuroscience
Center for Mind and Cognition
Selected Publications
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Analysis of a Generative Model of Episodic Memory Based on Hierarchical VQ-VAE and TransformerReyhanian, S., Fayyaz, Z., & Wiskott, L.In Proceedings of the 33rd International Conference on Artificial Neural Networks (ICANN 2024), Lugano, Switzerland Springer Nature Switzerland
@inproceedings{ReyhanianFayyazWiskott2024, author = {Reyhanian, Shirin and Fayyaz, Zahra and Wiskott, Laurenz}, title = {Analysis of a Generative Model of Episodic Memory Based on Hierarchical VQ-VAE and Transformer}, booktitle = {Proceedings of the 33rd International Conference on Artificial Neural Networks (ICANN 2024), Lugano, Switzerland}, publisher = {Springer Nature Switzerland}, month = {September}, year = {2024}, doi = {10.1007/978-3-031-72341-4_6}, }
Reyhanian, S., Fayyaz, Z., & Wiskott, L.. (2024). Analysis of a Generative Model of Episodic Memory Based on Hierarchical VQ-VAE and Transformer. In Proceedings of the 33rd International Conference on Artificial Neural Networks (ICANN 2024), Lugano, Switzerland. Springer Nature Switzerland. http://doi.org/10.1007/978-3-031-72341-4_6Hebbian Descent: A Unified View on Log-Likelihood LearningMelchior, J., Schiewer, R., & Wiskott, L.Neural Computation, 36(9), 1669–1712@article{MelchiorSchiewerWiskott2024, author = {Melchior, Jan and Schiewer, Robin and Wiskott, Laurenz}, title = {Hebbian Descent: A Unified View on Log-Likelihood Learning}, journal = {Neural Computation}, volume = {36}, number = {9}, pages = {1669–1712}, month = {August}, year = {2024}, doi = {10.1162/neco_a_01684}, }
Melchior, J., Schiewer, R., & Wiskott, L.. (2024). Hebbian Descent: A Unified View on Log-Likelihood Learning. Neural Computation, 36(9), 1669–1712. http://doi.org/10.1162/neco_a_01684A neural network model for online one-shot storage of pattern sequencesMelchior, J., Altamimi, A., Bayati, M., Cheng, S., & Wiskott, L.PLOS ONE, 19(6), 1–28@article{MelchiorAltamimiBayatiEtAl2024, author = {Melchior, Jan and Altamimi, Aya and Bayati, Mehdi and Cheng, Sen and Wiskott, Laurenz}, title = {A neural network model for online one-shot storage of pattern sequences}, journal = {PLOS ONE}, volume = {19}, number = {6}, pages = {1–28}, month = {June}, year = {2024}, doi = {10.1371/journal.pone.0304076}, }
Melchior, J., Altamimi, A., Bayati, M., Cheng, S., & Wiskott, L.. (2024). A neural network model for online one-shot storage of pattern sequences. PLOS ONE, 19(6), 1–28. http://doi.org/10.1371/journal.pone.0304076*Best Paper Nominee* Gaining Insights into Course Difficulty Variations Using Item Response TheoryBaucks, F., Schmucker, R., & Wiskott, L.In LAK24: 14th International Learning Analytics and Knowledge Conference (pp. 450–461) New York, NY, USA: Association for Computing Machinery@inproceedings{BaucksSchmuckerWiskott2024, author = {Baucks, Frederik and Schmucker, Robin and Wiskott, Laurenz}, title = {*Best Paper Nominee* Gaining Insights into Course Difficulty Variations Using Item Response Theory}, booktitle = {LAK24: 14th International Learning Analytics and Knowledge Conference}, pages = {450–461}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, month = {March}, year = {2024}, doi = {10.1145/3636555.3636902}, }
Baucks, F., Schmucker, R., & Wiskott, L.. (2024). *Best Paper Nominee* Gaining Insights into Course Difficulty Variations Using Item Response Theory. In LAK24: 14th International Learning Analytics and Knowledge Conference (pp. 450–461). New York, NY, USA: Association for Computing Machinery. http://doi.org/10.1145/3636555.3636902tachAId—An interactive tool supporting the design of human-centered AI solutionsBauroth, M., Rath-Manakidis, P., Langholf, V., Wiskott, L., & Glasmachers, T.Frontiers in Artificial Intelligence, 7@article{BaurothRath-ManakidisLangholfEtAl2024, author = {Bauroth, Max and Rath-Manakidis, Pavlos and Langholf, Valentin and Wiskott, Laurenz and Glasmachers, Tobias}, title = {tachAId—An interactive tool supporting the design of human-centered AI solutions}, journal = {Frontiers in Artificial Intelligence}, volume = {7}, year = {2024}, doi = {10.3389/frai.2024.1354114}, }
Bauroth, M., Rath-Manakidis, P., Langholf, V., Wiskott, L., & Glasmachers, T.. (2024). tachAId—An interactive tool supporting the design of human-centered AI solutions. Frontiers in Artificial Intelligence, 7. http://doi.org/10.3389/frai.2024.1354114Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation TasksLange, M., Engelhardt, R. C., Konen, W., & Wiskott, L.In eXplainable AI approaches for Deep Reinforcement Learning@inproceedings{LangeEngelhardtKonenEtAl2024, author = {Lange, Moritz and Engelhardt, Raphael C. and Konen, Wolfgang and Wiskott, Laurenz}, title = {Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks}, booktitle = {eXplainable AI approaches for Deep Reinforcement Learning}, year = {2024}, }
Lange, M., Engelhardt, R. C., Konen, W., & Wiskott, L.. (2024). Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks. In eXplainable AI approaches for Deep Reinforcement Learning. Retrieved from https://openreview.net/forum?id=s1oVgaZ3dQ*Best Paper Award* Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-visual Environments: A ComparisonLange, M., Krystiniak, N., Engelhardt, R. C., Konen, W., & Wiskott, L.In G. Nicosia, Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P. M., & Umeton, R. (Eds.), Machine Learning, Optimization, and Data Science (pp. 177–191) Cham: Springer Nature Switzerland@inproceedings{LangeKrystiniakEngelhardtEtAl2024, author = {Lange, Moritz and Krystiniak, Noah and Engelhardt, Raphael C. and Konen, Wolfgang and Wiskott, Laurenz}, title = {*Best Paper Award* Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-visual Environments: A Comparison}, booktitle = {Machine Learning, Optimization, and Data Science}, editor = {Nicosia, Giuseppe and Ojha, Varun and La Malfa, Emanuele and La Malfa, Gabriele and Pardalos, Panos M. and Umeton, Renato}, pages = {177–191}, publisher = {Springer Nature Switzerland}, address = {Cham}, year = {2024}, doi = {10.1007/978-3-031-53966-4_14}, }
Lange, M., Krystiniak, N., Engelhardt, R. C., Konen, W., & Wiskott, L.. (2024). *Best Paper Award* Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-visual Environments: A Comparison. In G. Nicosia, Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P. M., & Umeton, R. (Eds.), Machine Learning, Optimization, and Data Science (pp. 177–191). Cham: Springer Nature Switzerland. http://doi.org/10.1007/978-3-031-53966-4_14Modularity in Nervous Systems - a Key to Efficient Adaptivity for Deep Reinforcement LearningSchilling, M., Hammer, B., Ohl, F. W., Ritter, H. J., & Wiskott, L.Cogn. Comput., 16(5), 2358–2373@article{SchillingHammerOhlEtAl2024, author = {Schilling, Malte and Hammer, Barbara and Ohl, Frank W. and Ritter, Helge J. and Wiskott, Laurenz}, title = {Modularity in Nervous Systems - a Key to Efficient Adaptivity for Deep Reinforcement Learning}, journal = {Cogn. Comput.}, volume = {16}, number = {5}, pages = {2358–2373}, year = {2024}, doi = {10.1007/S12559-022-10080-W}, }
Schilling, M., Hammer, B., Ohl, F. W., Ritter, H. J., & Wiskott, L.. (2024). Modularity in Nervous Systems - a Key to Efficient Adaptivity for Deep Reinforcement Learning. Cogn. Comput., 16(5), 2358–2373. http://doi.org/10.1007/S12559-022-10080-W2023
A Tutorial on the Spectral Theory of Markov ChainsSeabrook, E., & Wiskott, L.Neural Computation, 35(11), 1713–1796@article{SeabrookWiskott2023, author = {Seabrook, Eddie and Wiskott, Laurenz}, title = {A Tutorial on the Spectral Theory of Markov Chains}, journal = {Neural Computation}, volume = {35}, number = {11}, pages = {1713–1796}, month = {October}, year = {2023}, doi = {10.1162/neco_a_01611}, }
Seabrook, E., & Wiskott, L.. (2023). A Tutorial on the Spectral Theory of Markov Chains. Neural Computation, 35(11), 1713–1796. http://doi.org/10.1162/neco_a_01611A map of spatial navigation for neuroscienceParra-Barrero, E., Vijayabaskaran, S., Seabrook, E., Wiskott, L., & Cheng, S.Neuroscience & Biobehavioral Reviews, 152, 105200@article{Parra-BarreroVijayabaskaranSeabrookEtAl2023, author = {Parra-Barrero, Eloy and Vijayabaskaran, Sandhiya and Seabrook, Eddie and Wiskott, Laurenz and Cheng, Sen}, title = {A map of spatial navigation for neuroscience}, journal = {Neuroscience & Biobehavioral Reviews}, volume = {152}, pages = {105200}, month = {September}, year = {2023}, doi = {10.1016/j.neubiorev.2023.105200}, }
Parra-Barrero, E., Vijayabaskaran, S., Seabrook, E., Wiskott, L., & Cheng, S.. (2023). A map of spatial navigation for neuroscience. Neuroscience & Biobehavioral Reviews, 152, 105200. http://doi.org/10.1016/j.neubiorev.2023.105200Modeling the function of episodic memory in spatial learningZeng, X., Diekmann, N., Wiskott, L., & Cheng, S.Frontiers in Psychology, 14@article{ZengDiekmannWiskottEtAl2023, author = {Zeng, Xiangshuai and Diekmann, Nicolas and Wiskott, Laurenz and Cheng, Sen}, title = {Modeling the function of episodic memory in spatial learning}, journal = {Frontiers in Psychology}, volume = {14}, year = {2023}, doi = {10.3389/fpsyg.2023.1160648}, }
Zeng, X., Diekmann, N., Wiskott, L., & Cheng, S.. (2023). Modeling the function of episodic memory in spatial learning. Frontiers in Psychology, 14. http://doi.org/10.3389/fpsyg.2023.11606482022
A Model of Semantic Completion in Generative Episodic MemoryFayyaz, Z., Altamimi, A., Zoellner, C., Klein, N., Wolf, O. T., Cheng, S., & Wiskott, L.Neural Computation, 34(9), 1841–1870@article{FayyazAltamimiZoellnerEtAl2022, author = {Fayyaz, Zahra and Altamimi, Aya and Zoellner, Carina and Klein, Nicole and Wolf, Oliver T. and Cheng, Sen and Wiskott, Laurenz}, title = {A Model of Semantic Completion in Generative Episodic Memory}, journal = {Neural Computation}, volume = {34}, number = {9}, pages = {1841–1870}, month = {August}, year = {2022}, doi = {10.1162/neco_a_01520}, }
Fayyaz, Z., Altamimi, A., Zoellner, C., Klein, N., Wolf, O. T., Cheng, S., & Wiskott, L.. (2022). A Model of Semantic Completion in Generative Episodic Memory. Neural Computation, 34(9), 1841–1870. http://doi.org/10.1162/neco_a_01520Latent Representation Prediction NetworksHlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.International Journal of Pattern Recognition and Artificial Intelligence, 36(01), 2251002@article{HlynssonSchülerSchiewerEtAl2022, author = {Hlynsson, Hlynur David and Schüler, Merlin and Schiewer, Robin and Glasmachers, Tobias and Wiskott, Laurenz}, title = {Latent Representation Prediction Networks}, journal = {International Journal of Pattern Recognition and Artificial Intelligence}, volume = {36}, number = {01}, pages = {2251002}, year = {2022}, doi = {10.1142/S0218001422510028}, }
Hlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.. (2022). Latent Representation Prediction Networks. International Journal of Pattern Recognition and Artificial Intelligence, 36(01), 2251002. http://doi.org/10.1142/S02180014225100282019
Improved graph-based SFA: information preservation complements the slowness principleEscalante-B., A. N., & Wiskott, L.Machine Learning@article{Escalante-B.Wiskott2019, author = {Escalante-B., Alberto N. and Wiskott, Laurenz}, title = {Improved graph-based SFA: information preservation complements the slowness principle}, journal = {Machine Learning}, month = {December}, year = {2019}, doi = {10.1007/s10994-019-05860-9}, }
Escalante-B., A. N., & Wiskott, L.. (2019). Improved graph-based SFA: information preservation complements the slowness principle. Machine Learning. http://doi.org/10.1007/s10994-019-05860-9Measuring the Data Efficiency of Deep Learning MethodsHlynsson, H., Escalante-B., A., & Wiskott, L.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods SCITEPRESS - Science and Technology Publications@inproceedings{HlynssonEscalante-B.Wiskott2019, author = {Hlynsson, Hlynur and Escalante-B., Alberto and Wiskott, Laurenz}, title = {Measuring the Data Efficiency of Deep Learning Methods}, booktitle = {Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods}, publisher = {SCITEPRESS - Science and Technology Publications}, year = {2019}, doi = {10.5220/0007456306910698}, }
Hlynsson, H., Escalante-B., A., & Wiskott, L.. (2019). Measuring the Data Efficiency of Deep Learning Methods. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications. http://doi.org/10.5220/00074563069106982018
The Interaction between Semantic Representation and Episodic MemoryFang, J., Rüther, N., Bellebaum, C., Wiskott, L., & Cheng, S.Neural Computation, 30(2), 293–332@article{FangRütherBellebaumEtAl2018, author = {Fang, Jing and Rüther, Naima and Bellebaum, Christian and Wiskott, Laurenz and Cheng, Sen}, title = {The Interaction between Semantic Representation and Episodic Memory}, journal = {Neural Computation}, volume = {30}, number = {2}, pages = {293–332}, month = {February}, year = {2018}, doi = {10.1162/neco_a_01044}, }
Fang, J., Rüther, N., Bellebaum, C., Wiskott, L., & Cheng, S.. (2018). The Interaction between Semantic Representation and Episodic Memory. Neural Computation, 30(2), 293–332. http://doi.org/10.1162/neco_a_01044Slowness as a Proxy for Temporal Predictability: An Empirical ComparisonWeghenkel, B., & Wiskott, L.Neural Computation, 30(5), 1151–1179@article{WeghenkelWiskott2018, author = {Weghenkel, Björn and Wiskott, Laurenz}, title = {Slowness as a Proxy for Temporal Predictability: An Empirical Comparison}, journal = {Neural Computation}, volume = {30}, number = {5}, pages = {1151–1179}, year = {2018}, doi = {10.1162/neco_a_01070}, }
Weghenkel, B., & Wiskott, L.. (2018). Slowness as a Proxy for Temporal Predictability: An Empirical Comparison. Neural Computation, 30(5), 1151–1179. http://doi.org/10.1162/neco_a_010702017
Gaussian-binary restricted Boltzmann machines for modeling natural image statisticsMelchior, J., Wang, N., & Wiskott, L.PLOS ONE, 12(2), 1–24@article{MelchiorWangWiskott2017, author = {Melchior, Jan and Wang, Nan and Wiskott, Laurenz}, title = {Gaussian-binary restricted Boltzmann machines for modeling natural image statistics}, journal = {PLOS ONE}, volume = {12}, number = {2}, pages = {1–24}, month = {February}, year = {2017}, doi = {10.1371/journal.pone.0171015}, }
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.01710152016
Theoretical analysis of the optimal free responses of graph-based SFA for the design of training graphs.Escalante-B., A. N., & Wiskott, L.Journal of Machine Learning Research, 17(157), 1–36@article{Escalante-B.Wiskott2016b, author = {Escalante-B., Alberto N. and Wiskott, Laurenz}, title = {Theoretical analysis of the optimal free responses of graph-based SFA for the design of training graphs.}, journal = {Journal of Machine Learning Research}, volume = {17}, number = {157}, pages = {1–36}, year = {2016}, }
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.htmlHow to Center Deep Boltzmann MachinesMelchior, J., Fischer, A., & Wiskott, L.Journal of Machine Learning Research, 17(99), 1–61@article{MelchiorFischerWiskott2016, author = {Melchior, Jan and Fischer, Asja and Wiskott, Laurenz}, title = {How to Center Deep Boltzmann Machines}, journal = {Journal of Machine Learning Research}, volume = {17}, number = {99}, pages = {1–61}, year = {2016}, }
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.html2015
Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input StatisticsNeher, T., Cheng, S., & Wiskott, L.PLoS Computational Biology, 11(5), e1004250@article{NeherChengWiskott2015, author = {Neher, Torsten and Cheng, Sen and Wiskott, Laurenz}, title = {Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input Statistics}, journal = {PLoS Computational Biology}, volume = {11}, number = {5}, pages = {e1004250}, year = {2015}, doi = {10.1371/journal.pcbi.1004250}, }
Neher, T., Cheng, S., & Wiskott, L.. (2015). Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input Statistics. PLoS Computational Biology, 11(5), e1004250. http://doi.org/10.1371/journal.pcbi.1004250Modeling place field activity with hierarchical slow feature analysisSchoenfeld, F., & Wiskott, L.Frontiers in Computational Neuroscience, 9(51)@article{SchoenfeldWiskott2015, author = {Schoenfeld, Fabian and Wiskott, Laurenz}, title = {Modeling place field activity with hierarchical slow feature analysis}, journal = {Frontiers in Computational Neuroscience}, volume = {9}, number = {51}, year = {2015}, doi = {10.3389/fncom.2015.00051}, }
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.2015.000512011
A theory of Slow Feature Analysis for transformation-based input signals with an application to complex cellsSprekeler, H., & Wiskott, L.Neural Computation, 23(2), 303–335@article{SprekelerWiskott2011, author = {Sprekeler, Henning and Wiskott, Laurenz}, title = {A theory of Slow Feature Analysis for transformation-based input signals with an application to complex cells}, journal = {Neural Computation}, volume = {23}, number = {2}, pages = {303–335}, year = {2011}, doi = {10.1162/NECO_a_00072}, }
Sprekeler, H., & Wiskott, L.. (2011). A theory of Slow Feature Analysis for transformation-based input signals with an application to complex cells. Neural Computation, 23(2), 303–335. http://doi.org/10.1162/NECO_a_00072-
2018-01 - 2018-05
Simons, Berkeley, California - Visiting scholar
Visiting scholar at the Simons Institute for the Theory of Computing at the University of California, Berkeley, USA
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since Nov 2008
INI, Bochum, Germany - Professor (W3)
Professor (W3) at the Institute for Neural Computation at the Ruhr-University Bochum
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2006-2008
ITB, Berlin, Germany - Professor (W2)
Professor (W2) for Computational Neuroscience and Neuroinformatics at the Institute for Theoretical Biology at the Humboldt-University Berlin
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2000-2006
ITB, Berlin, Germany - Junior research group leader
Junior research group leader at the Institute for Theoretical Biology at the Humboldt-University Berlin (supported by the Volkswagen Foundation)
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1999-2000
ITB, Berlin, Germany - Research associate
Research associate with Prof. Andreas V. M. Herz at the Innovationskolleg Theoretische Biologie at the Humboldt-University Berlin
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1998-1999
Wiko, Berlin, Germany - Fellow
Fellow at the Wissenschaftskolleg zu Berlin / Institute for Advanced Study in Berlin, Germany
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1995-1998
CNL, San Diego, California - Research associate
Research associate with Prof. Terrence Sejnowski at the Computational Neurobiology Laboratory at the Salk Institute for Biological Studies, San Diego, California (supported by a Lynen Research Fellowship from the Alexander von Humboldt Foundation, Germany)
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Oct. 1995
INI, Bochum, Germany - PhD (Dr. rer. nat.) in physics
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1993, 1994
USC, Los Angeles, California - Visiting research assistant
Visiting research assistant (6 months) at the Laboratory for Computational and Biological Vision at the University of Southern California, Los Angeles, California
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1990-1995
INI, Bochum, Germany - Research assistant
Research assistant with Prof. Christoph von der Malsburg at the Institute for Neural Computation at the Ruhr-University Bochum, Germany with several visits at the Laboratory for Computational and Biological Vision at the University of Southern California, Los Angeles, California
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Sep. 1990
Uni, Osnabrück, Germany - Diploma in physics
Diploma in physics at the University Osnabrück, Germany
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1989-1990
Uni, Osnabrück, Germany - Studies of physics
Studies of physics at the University Osnabrück, Germany
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1986-1988
MPI, Göttingen, Germany - Undergraduate research assistant
Undergraduate research assistant at the Max-Planck-Institute for Biophysical Chemistry in Göttingen, developing an operating system for a parallel computer
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1985-1989
Uni, Göttingen, Germany - Studies of physics
Studies of physics at the University Göttingen, Germany
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A Taxonomy of Spatial Navigation in Mammals: Insights from Computational ModelingVijayabaskaran, S., Zeng, X., Ghazinouri, B., Wiskott, L., & Cheng, S.Center for Open Science
@misc{VijayabaskaranZengGhazinouriEtAl2025, author = {Vijayabaskaran, Sandhiya and Zeng, Xiangshuai and Ghazinouri, Behnam and Wiskott, Laurenz and Cheng, Sen}, title = {A Taxonomy of Spatial Navigation in Mammals: Insights from Computational Modeling}, month = {March}, year = {2025}, doi = {10.31219/osf.io/g9sfd_v1}, }
Vijayabaskaran, S., Zeng, X., Ghazinouri, B., Wiskott, L., & Cheng, S.. (2025, March). A Taxonomy of Spatial Navigation in Mammals: Insights from Computational Modeling. Center for Open Science. http://doi.org/10.31219/osf.io/g9sfd_v1Remembering without (representational) memory: A neuro-computational study on regaining categoricity and compositionality from minimal tracesFayyaz, Z., Righetti, F., Wiskott, L., & Werning, M.@article{FayyazRighettiWiskottEtAl2025, author = {Fayyaz, Zahra and Righetti, Francesca and Wiskott, Laurenz and Werning, Markus}, title = {Remembering without (representational) memory: A neuro-computational study on regaining categoricity and compositionality from minimal traces}, month = {February}, year = {2025}, doi = {10.31219/osf.io/zjprq_v1}, }
Fayyaz, Z., Righetti, F., Wiskott, L., & Werning, M. (2025). Remembering without (representational) memory: A neuro-computational study on regaining categoricity and compositionality from minimal traces. http://doi.org/10.31219/osf.io/zjprq_v12024
Exploring the limits of hierarchical world models in reinforcement learningSchiewer, R., Subramoney, A., & Wiskott, L.Scientific Reports, 14(1)@article{SchiewerSubramoneyWiskott2024, author = {Schiewer, Robin and Subramoney, Anand and Wiskott, Laurenz}, title = {Exploring the limits of hierarchical world models in reinforcement learning}, journal = {Scientific Reports}, volume = {14}, number = {1}, month = {November}, year = {2024}, doi = {10.1038/s41598-024-76719-w}, }
Schiewer, R., Subramoney, A., & Wiskott, L.. (2024). Exploring the limits of hierarchical world models in reinforcement learning. Scientific Reports, 14(1). http://doi.org/10.1038/s41598-024-76719-wAnalysis of a Generative Model of Episodic Memory Based on Hierarchical VQ-VAE and TransformerReyhanian, S., Fayyaz, Z., & Wiskott, L.In Proceedings of the 33rd International Conference on Artificial Neural Networks (ICANN 2024), Lugano, Switzerland Springer Nature Switzerland@inproceedings{ReyhanianFayyazWiskott2024, author = {Reyhanian, Shirin and Fayyaz, Zahra and Wiskott, Laurenz}, title = {Analysis of a Generative Model of Episodic Memory Based on Hierarchical VQ-VAE and Transformer}, booktitle = {Proceedings of the 33rd International Conference on Artificial Neural Networks (ICANN 2024), Lugano, Switzerland}, publisher = {Springer Nature Switzerland}, month = {September}, year = {2024}, doi = {10.1007/978-3-031-72341-4_6}, }
Reyhanian, S., Fayyaz, Z., & Wiskott, L.. (2024). Analysis of a Generative Model of Episodic Memory Based on Hierarchical VQ-VAE and Transformer. In Proceedings of the 33rd International Conference on Artificial Neural Networks (ICANN 2024), Lugano, Switzerland. Springer Nature Switzerland. http://doi.org/10.1007/978-3-031-72341-4_6Hebbian Descent: A Unified View on Log-Likelihood LearningMelchior, J., Schiewer, R., & Wiskott, L.Neural Computation, 36(9), 1669–1712@article{MelchiorSchiewerWiskott2024, author = {Melchior, Jan and Schiewer, Robin and Wiskott, Laurenz}, title = {Hebbian Descent: A Unified View on Log-Likelihood Learning}, journal = {Neural Computation}, volume = {36}, number = {9}, pages = {1669–1712}, month = {August}, year = {2024}, doi = {10.1162/neco_a_01684}, }
Melchior, J., Schiewer, R., & Wiskott, L.. (2024). Hebbian Descent: A Unified View on Log-Likelihood Learning. Neural Computation, 36(9), 1669–1712. http://doi.org/10.1162/neco_a_01684A neural network model for online one-shot storage of pattern sequencesMelchior, J., Altamimi, A., Bayati, M., Cheng, S., & Wiskott, L.PLOS ONE, 19(6), 1–28@article{MelchiorAltamimiBayatiEtAl2024, author = {Melchior, Jan and Altamimi, Aya and Bayati, Mehdi and Cheng, Sen and Wiskott, Laurenz}, title = {A neural network model for online one-shot storage of pattern sequences}, journal = {PLOS ONE}, volume = {19}, number = {6}, pages = {1–28}, month = {June}, year = {2024}, doi = {10.1371/journal.pone.0304076}, }
Melchior, J., Altamimi, A., Bayati, M., Cheng, S., & Wiskott, L.. (2024). A neural network model for online one-shot storage of pattern sequences. PLOS ONE, 19(6), 1–28. http://doi.org/10.1371/journal.pone.0304076*Best Paper Nominee* Gaining Insights into Course Difficulty Variations Using Item Response TheoryBaucks, F., Schmucker, R., & Wiskott, L.In LAK24: 14th International Learning Analytics and Knowledge Conference (pp. 450–461) New York, NY, USA: Association for Computing Machinery@inproceedings{BaucksSchmuckerWiskott2024, author = {Baucks, Frederik and Schmucker, Robin and Wiskott, Laurenz}, title = {*Best Paper Nominee* Gaining Insights into Course Difficulty Variations Using Item Response Theory}, booktitle = {LAK24: 14th International Learning Analytics and Knowledge Conference}, pages = {450–461}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, month = {March}, year = {2024}, doi = {10.1145/3636555.3636902}, }
Baucks, F., Schmucker, R., & Wiskott, L.. (2024). *Best Paper Nominee* Gaining Insights into Course Difficulty Variations Using Item Response Theory. In LAK24: 14th International Learning Analytics and Knowledge Conference (pp. 450–461). New York, NY, USA: Association for Computing Machinery. http://doi.org/10.1145/3636555.3636902Gaining Insights into Group-Level Course Difficulty via Differential Course FunctioningBaucks, F., Schmucker, R., Borchers, C., Pardos, Z. A., & Wiskott, L.In Proceedings of the Eleventh ACM Conference on Learning @ Scale (pp. 165–176) Atlanta, GA, USA: Association for Computing Machinery@inproceedings{BaucksSchmuckerBorchersEtAl2024, author = {Baucks, Frederik and Schmucker, Robin and Borchers, Conrad and Pardos, Zachary A. and Wiskott, Laurenz}, title = {Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning}, booktitle = {Proceedings of the Eleventh ACM Conference on Learning @ Scale}, pages = {165–176}, publisher = {Association for Computing Machinery}, series = {L@S ′24}, address = {New York, NY, USA}, year = {2024}, doi = {10.1145/3657604.3662028}, }
Baucks, F., Schmucker, R., Borchers, C., Pardos, Z. A., & Wiskott, L.. (2024). Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (pp. 165–176). Atlanta, GA, USA: Association for Computing Machinery. http://doi.org/10.1145/3657604.3662028Empowering Advisors: Designing a Dashboard for University Student GuidanceBaucks, F., & Wiskott, L.In P. Salden & Leschke, J. (Eds.), Learning Analytics und Künstliche Intelligenz in Studium und Lehre: Erfahrungen und Schlussfolgerungen aus einer hochschulweiten Erprobung (pp. 27–44) Wiesbaden: Springer Fachmedien Wiesbaden@inbook{BaucksWiskott2024, author = {Baucks, Frederik and Wiskott, Laurenz}, title = {Empowering Advisors: Designing a Dashboard for University Student Guidance}, editor = {Salden, Peter and Leschke, Jonas}, pages = {27–44}, publisher = {Springer Fachmedien Wiesbaden}, address = {Wiesbaden}, year = {2024}, doi = {10.1007/978-3-658-42993-5_2}, }
Baucks, F., & Wiskott, L.. (2024). Empowering Advisors: Designing a Dashboard for University Student Guidance. In P. Salden & Leschke, J. (Eds.), Learning Analytics und Künstliche Intelligenz in Studium und Lehre: Erfahrungen und Schlussfolgerungen aus einer hochschulweiten Erprobung (pp. 27–44). Wiesbaden: Springer Fachmedien Wiesbaden. http://doi.org/10.1007/978-3-658-42993-5_2tachAId—An interactive tool supporting the design of human-centered AI solutionsBauroth, M., Rath-Manakidis, P., Langholf, V., Wiskott, L., & Glasmachers, T.Frontiers in Artificial Intelligence, 7@article{BaurothRath-ManakidisLangholfEtAl2024, author = {Bauroth, Max and Rath-Manakidis, Pavlos and Langholf, Valentin and Wiskott, Laurenz and Glasmachers, Tobias}, title = {tachAId—An interactive tool supporting the design of human-centered AI solutions}, journal = {Frontiers in Artificial Intelligence}, volume = {7}, year = {2024}, doi = {10.3389/frai.2024.1354114}, }
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