Prof. Dr. Laurenz Wiskott
Project P5, Project P2
Ruhr University Bochum
Institut für Neuroinformatik
NB 3/29
Universitätsstraße 150
44801 Bochum
NB 3/29
Universitätsstraße 150
44801 Bochum
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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.0304076Interpretable 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=s1oVgaZ3dQModularity 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_01611Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A ComparisonLange, M., Krystiniak, N., Engelhardt, R. C., Konen, W., & Wiskott, L.In Proc. 9th International Conference on Machine Learning, Optimization and Data science (LOD)@inproceedings{LangeKrystiniakEngelhardtEtAl2023, author = {Lange, Moritz and Krystiniak, Noah and Engelhardt, Raphael C. and Konen, Wolfgang and Wiskott, Laurenz}, title = {Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison}, booktitle = {Proc. 9th International Conference on Machine Learning, Optimization and Data science (LOD)}, year = {2023}, }
Lange, M., Krystiniak, N., Engelhardt, R. C., Konen, W., & Wiskott, L.. (2023). Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison. In Proc. 9th International Conference on Machine Learning, Optimization and Data science (LOD). Retrieved from https://link.springer.com/chapter/10.1007/978-3-031-53966-4_14A 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}, 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_015202021
The computational benefits of episodic memory in spatial learning@article{ZengWiskottCheng2021, author = {Zeng, Xiangshuai and Wiskott, Laurenz and Cheng, Sen}, title = {The computational benefits of episodic memory in spatial learning}, month = {November}, year = {2021}, doi = {10.1101/2021.11.24.469830}, }
Zeng, X., Wiskott, L., & Cheng, S.. (2021). The computational benefits of episodic memory in spatial learning. http://doi.org/10.1101/2021.11.24.469830A model of semantic completion in generative episodic memory@misc{FayyazAltamimiChengEtAl2021, author = {Fayyaz, Zahra and Altamimi, Aya and Cheng, Sen and Wiskott, Laurenz}, title = {A model of semantic completion in generative episodic memory}, year = {2021}, }
Fayyaz, Z., Altamimi, A., Cheng, S., & Wiskott, L.. (2021). A model of semantic completion in generative episodic memory. Retrieved from https://arxiv.org/abs/2111.135372019
Improving sensory representations using episodic memoryGörler, R., Wiskott, L., & Cheng, S.Hippocampus@article{GörlerWiskottCheng2019, author = {Görler, Richard and Wiskott, Laurenz and Cheng, Sen}, title = {Improving sensory representations using episodic memory}, journal = {Hippocampus}, month = {December}, year = {2019}, doi = {10.1002/hipo.23186}, }
Görler, R., Wiskott, L., & Cheng, S.. (2019). Improving sensory representations using episodic memory. Hippocampus. http://doi.org/10.1002/hipo.23186The research unit FOR 2812 "Constructing scenarios of the past: A new framework in episodic memory" is a project funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG). The research unit studies the cognitive and neuronal mechanisms underlying scenario construction in episodic memory. We employ and integrate approaches from Philosophy, Psychology, and Experimental and Computational Neuroscience.
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