Theory of Neural Systems
Institut für Neuroinformatik
Faculty of Computer Science
Ruhr University Bochum
Universitätsstr. 150
44801 Bochum
Room:
NB 3/29
Phone:
+49 (0)234 32-27997
Email:
laurenz.wiskott@ini.rub.de
We are an interdisciplinary research group focusing on principles of self-organization in neural systems, ranging from artificial neural networks to the hippocampus/memory system. By bringing together machine learning and computational neuroscience we explore ways of extracting representations from data that are useful for goal-directed learning.
On the machine learning side our work is centered around reinforcement learning, where an agent learns to interact with its environment. In this context we investigate learning of representations for different kinds of data, such as visual data and graphs, by means of deep learning as well as classical unsupervised methods. Additionally, we research model-based agents that can remember their environment and are capable of planning ahead. On all these frontiers, we do not only seek to improve algorithmic performance but also to develop new ways of building more interpretable, explainable and human-friendly AI.
On the neuroscience side, our work focuses on computational modeling of brain functions concerned with encoding, storage and recall of memories. Through this we aim to understand how information is learned, represented within different types of memory and finally reconstructed from memory.
Parra-Barrero, E., Vijayabaskaran, S., Seabrook, E., Wiskott, L., & Cheng, S. (2023). A map of spatial navigation for neuroscience. Neuroscience and Biobehavioral Reviews, 152, 105200. https://doi.org/10.1016/j.neubiorev.2023.105200
Zeng, X., Diekmann, N., Wiskott, L., & Cheng, S. (2023). Modeling the function of episodic memory in spatial learning. Frontiers in Psychology, 14, 1160648. https://doi.org/10.3389/fpsyg.2023.1160648
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. https://doi.org/10.1162/neco_a_01520
Walther, T., Diekmann, N., Vijayabaskaran, S., Donoso, J. R., Manahan-Vaughan, D., Wiskott, L., & Cheng, S. (2021). Context-dependent extinction learning emerging from raw sensory inputs: A reinforcement learning approach. Scientific Reports, 11(1), 2713. https://doi.org/10.1038/s41598-021-81157-z
Görler, R., Wiskott, L., & Cheng, S. (2020). Improving sensory representations using episodic memory. Hippocampus, 30(6), 638–656. https://doi.org/10.1002/hipo.23186
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. https://doi.org/10.1162/neco_a_01044
Weghenkel, B., & Wiskott, L. (2018). Slowness as a Proxy for Temporal Predictability: An Empirical Comparison. Neural Computation, 30(5), 1151–1179. https://doi.org/10.1162/NECO_a_01070
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. https://doi.org/10.1371/journal.pcbi.1004250
Schönfeld, F., & Wiskott, L. (2015). Modeling place field activity with hierarchical slow feature analysis. Frontiers in Computational Neuroscience, 9, 51. https://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 Computational Biology, 10(5), e1003564. https://doi.org/10.1371/journal.pcbi.1003564
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Last update: Feb 07, 2024
