Henning Sprekeler
Photo: SCIoI
Henning Sprekeler is Bernstein Professor of Modeling of Cognitive Processes. For SCIoI, he represents the analytical discipline computational neuroscience. He has extensive expertise in computational modelling of synaptic plasticity and its consequences for behavioural learning and the formation of memories. He has also worked on the self-organised development of high-level sensory representations, an essential prerequisite for intelligent behaviour.
Projects
Henning Sprekeler is member of:
• Project 03: Mouse lock box
• Project 12: Learning of intelligent swarm behavior
• Project 13: Architectural design principles for intelligence: Modularity vs Integrity
• Project 25: Mice learning in social interaction
• Project 33: Shepherding behaviour in predator-prey interactions
• Project 40: Big mouse data
• Project 58: From exploring basic maze elements to algorithmic rules for the escape room
• Project 12: Learning of intelligent swarm behavior
• Project 13: Architectural design principles for intelligence: Modularity vs Integrity
• Project 25: Mice learning in social interaction
• Project 33: Shepherding behaviour in predator-prey interactions
• Project 40: Big mouse data
• Project 58: From exploring basic maze elements to algorithmic rules for the escape room
Publications +
Oksuz, H. Y., Molinari, F., Sprekeler, H., & Raisch, J. (2024). Boosting Fairness and Robustness in Over-the-Air Federated Learning. IEEE Control Systems Letters (LCSS), 8, 682–687. https://doi.org/10.1109/lcsys.2024.3402123
Hohlbaum, K., Andresen, N., Mieske, P., Kahnau, P., Lang, B., Diederich, K., Palme, R., Mundhenk, L., Sprekeler, H., Hellwich, O., Thöne-Reineke, C., & Lewejohann, L. (2024). Lockbox enrichment facilitates manipulative and cognitive activities for mice. Open Research Europe. https://doi.org/10.12688/openreseurope.17624.2
Boon, M. N., Andresen, N., Traverso, S., Meier, S., Schuessle, F., Hellwich, O., Lewejohann, L., Thöne-Reineke, C., Sprekeler, H., & Hohlbaum, K. (2024). Mechanical problem solving in mice. bioRxiv. https://doi.org/10.1101/2024.07.29.605658
Boon, M. N., Andresen, N., Traverso, S., Meier, S., Hellwich, O., Lewejohann, L., Thöne-Reineke, C., Sprekeler, H., & Hohlbaum, K. (2024). Mouse lockbox: A sequential mechanical decision-making task to investigate complex mouse behavior. ICN 2024.
Lukas, J., Krause, J., Träger, A. S., Piotrowski, J. M., Romanczuk, P., Sprekeler, H., Arias-Rodriguez, L., Krause, S., Schutz, C., & Bierbach, D. (2023). Multispecies collective waving behaviour in fish. Philosophical Transactions of the Royal Society B. https://doi.org/10.1098/rstb.2022.0069
Gómez-Nava, L., Lange, R. T., Klamser, P. P., Lukas, J., Arias-Rodriguez, L., Bierbach, D., Krause, J., Sprekeler, H., & Romanczuk, P. (2023). Fish shoals resemble a stochastic excitable system driven by environmental perturbations. Nature Physics. https://doi.org/10.1038/s41567-022-01916-1
Boon, M. N., Andresen, N., Meier, S., Hellwich, O., Lewejohann, L., Thöne-Reineke, C., Sprekeler, H., & Hohlbaum, K. (2023). Mouse lock box: a sequential mechanical decision-making task to investigate complex mouse behavior. Bernstein Conference. https://doi.org/10.12751/nncn.bc2023.056
Oksuz, H. Y., Molinari, F., Sprekeler, H., & Raisch, J. (2023). Federated Learning in Wireless Networks via Over-the-Air Computations. 62nd IEEE Conference on Decision and Control (CDC), 4379–4386. https://doi.org/10.1109/CDC49753.2023.10384001
Vischer, M., Lange, R., & Sprekeler, H. (2022). On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning. ICLR 2022. https://doi.org/10.48550/arXiv.2105.01648
Lange, R., & Sprekeler, H. (2022). Learning not to learn: Nature versus Nurture in Silico. AAAI 2022. https://doi.org/10.48550/arXiv.2010.04466
