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Publications

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2026

  1. Colomb, J., Maxeiner, M., Landgraf, T., & Mies, R. (2026). Research hardware publications should help build communities, careers and better hardware. Journal of Open Hardware, 10(1). https://doi.org/10.5206/joh.v10i1.23082 Link>
  2. Heurich, M., Granz, M., & Landgraf, T. (2026). RareCP: Regime-Aware Retrieval for Efficient Conformal Prediction. arXiv. https://doi.org/10.48550/arXiv.2605.08857 Link>
  3. Hocke, M., Gerken, A., Bierbach, D., Krause, J., & Landgraf, T. (2026). Robots that learn to evaluate models of collective behavior. arXiv. https://doi.org/10.48550/arXiv.2604.07303 Link>
  4. Kedia, P., Messerich, M., & Landgraf, T. (2026). COMB: Common Open Modular robotic platform for Bees. arXiv. https://doi.org/10.48550/arXiv.2604.04980 Link>
  5. Van Havermaet, S., Gerken, A., Mazrekaj, D., Bierbach, D., Simoens, P., Landgraf, T., & Khaluf, Y. (2026). Fish adapt and dynamically avoid an approaching robotic fish across repeated exposures. Scientific Reports, 16(1). https://doi.org/10.1038/s41598-026-44115-1 Link>

2025

  1. Busch, F., Kaibel, L., Nguyen, H., Lemke, T., Ziegelmayer, S., Graf, M., Marka, A. W., Endrös, L., Prucker, P., Spitzl, D., Mergen, M., Makowski, M. R., Bressem, K. K., Petzoldt, S., Adams, L. C., & Landgraf, T. (2025). Evaluation of a Retrieval-Augmented Generation-Powered Chatbot for Pre-CT Informed Consent: a Prospective Comparative Study. Journal of Imaging Informatics in Medicine, 38(6), 4312–4323. https://doi.org/10.1007/s10278-025-01483-w Link>
  2. Hadjitofi, A., Messerich, M., Landgraf, T., & Webb, B. (2025). Honeybees express foodward flight vectors after a detour. Journal of Experimental Biology, 228(22). https://doi.org/10.1242/jeb.251072 Link>
  3. Papadopoulou, M., Ball, M., Bartashevich, P., Burns, A. L. J., Chiara, V., Clark, M. A., Costelloe, B. R., Fele, M., French, F., Hauert, S., Heinrich, M. K., Herbert-Read, J. E., Hoitt, J., Ioannou, C. C., Landgraf, T., Matchette, S. R., Polverino, G., Sankey, D. W. E., Scott, D. M., … King, A. J. (2025). Active interactions between animals and technology: biohybrid approaches for animal behaviour research. Animal Behaviour, 224, 123160. https://doi.org/10.1016/j.anbehav.2025.123160 Link>
  4. Weimar, N., Maxeiner, M., Wong, E. M., Rick, I. P., Hocke, M., Landgraf, T., & von der Emde, G. (2025). Interactive electrical behaviour in mormyrid weakly electric fish: jamming avoidance response or social interaction? Animal Behaviour, 230, 123372. https://doi.org/10.1016/j.anbehav.2025.123372 Link>

2024

  1. Solopova, V., Herman, V., Benzmüller, C., & Landgraf, T. (2024). Check News in One Click: NLP-Empowered Pro-Kremlin Propaganda Detection. In N. Aletras & O. De Clercq (Eds.), Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations (pp. 44–51). Association for Computational Linguistics. https://aclanthology.org/2024.eacl-demo.6 Link>
  2. Granz, M., Heurich, M., & Landgraf, T. (2024, May). WeiPer: OOD Detection using Weight Perturbations of Class Projections. Advances in Neural Information Processing Systems 37 (NeurIPS 2024). https://doi.org/10.48550/arXiv.2405.17164 Link>
  3. Mellert, J., Kłos, W., Dormagen, D. M., Wild, B., Zachariae, A., Smith, M. L., Galizia, C. G., & Landgraf, T. (2024). Collective flow of circadian clock information in honeybee colonies. bioRxiv. https://doi.org/10.1101/2024.07.29.605620 Link>
  4. Solopova, V., Romeike, R., Gläser-Zikuda, M., Benzmüller, C., Landgraf, T., Hofmann, F., Schießl, J., Zhang, C., Plößl, L., & Witte, S. (2024). AI-powered automatic feedback on reflective writing. Annual Conference of the European Teacher Education Network (ETEN)“Teacher Education–Connecting Glocal.” http://fis.uni-bamberg.de/bitstreams/c052c8b0-c990-4780-99f0-8b387c693fc5/download Link>
  5. Panagiotou, E., Heurich, M., Landgraf, T., & Ntoutsi, E. (2024). TABCF: Counterfactual Explanations for Tabular Data Using a Transformer-Based VAE. Proceedings of the 5th ACM International Conference on AI in Finance, 274–282. https://doi.org/10.1145/3677052.3698673 Link>

2023

  1. Neubauer, L. C., Davidson, J. D., Wild, B., Dormagen, D. M., Landgraf, T., Couzin, I. D., & Smith, M. L. (2023). Honey bee drones are synchronously hyperactive inside the nest. bioRxiv. https://doi.org/10.1101/2023.01.19.524638 Link>
  2. Landgraf, T., Bierbach, D., Moenck, H. J., Musiolek, L., Hocke, M., & Maxeiner, M. (2023). Data for the publication "Socially competent robots". https://doi.org/10.17169/refubium-36430 Link>
  3. Solopova, V., Popescu, O.-I., Benzmüller, C., & Landgraf, T. (2023). Automated multilingual detection of Pro-Kremlin propaganda in newspapers and Telegram posts. arXiv. https://doi.org/10.48550/arXiv.2301.10604 Link>
  4. Solopova, V., Benzmüller, C., & Landgraf, T. (2023). The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective. Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP), 40–48. https://aclanthology.org/2023.unlp-1.5 Link>
  5. Maxeiner, M., Hocke, M., Moenck, H. J., Gebhardt, G. H. W., Weimar, N., Musiolek, L., Krause, J., Bierbach, D., & Landgraf, T. (2023). Social competence improves the performance of biomimetic robots leading live fish. Bioinspiration & Biomimetics, 18(4), 045001. https://doi.org/10.1088/1748-3190/acca59 Link>
  6. Van Havermaet, S., Simoens, P., Landgraf, T., & Khaluf, Y. (2023). Steering herds away from dangers in dynamic environments. Royal Society Open Science, 10(5), 230015. https://doi.org/10.1098/rsos.230015 Link>
  7. Solopova, V., Rostom, E., Cremer, F., Gruszczynski, A., Witte, S., Zhang, C., López, F. R., Plößl, L., Hofmann, F., Romeike, R., Gläser-Zikuda, M., Benzmüller, C., & Landgraf, T. (2023). PapagAI: Automated Feedback for Reflective Essays. In D. Seipel & A. Steen (Eds.), KI 2023: Advances in Artificial Intelligence (pp. 198–206). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-42608-7_16
  8. Neubauer, L. C., Davidson, J. D., Wild, B., Dormagen, D. M., Landgraf, T., Couzin, I. D., & Smith, M. L. (2023). Honey bee drones are synchronously hyperactive inside the nest. Animal Behaviour, 203, 207–223. https://doi.org/10.1016/j.anbehav.2023.05.018 Link>
  9. Dormagen, D. M., Wild, B., Wario, F., & Landgraf, T. (2023). Machine learning reveals the waggle drift’s role in the honey bee dance communication system. PNAS Nexus, 2(9), pgad275. https://doi.org/10.1093/pnasnexus/pgad275 Link>
  10. Jhawar, J., Davidson, J. D., Weidenmüller, A., Wild, B., Dormagen, D. M., Landgraf, T., Couzin, I. D., & Smith, M. L. (2023). How honeybees respond to heat stress from the individual to colony level. Journal of The Royal Society Interface, 20(207), 20230290. https://doi.org/10.1098/rsif.2023.0290 Link>

2022

  1. Doran, C., Bierbach, D., Lukas, J., Klamser, P., Landgraf, T., Klenz, H., Habedank, M., Arias-Rodriguez, L., Krause, S., Romanczuk, P., & Krause, J. (2022). Fish waves as emergent collective antipredator behavior. Current Biology, 32(3), 708–714.e4. https://doi.org/10.1016/j.cub.2021.11.068 Link>
  2. Smith, M. L., Davidson, J. D., Wild, B., Dormagen, D. M., Landgraf, T., & Couzin, I. D. (2022). Behavioral variation across the days and lives of honey bees. IScience, 25(9), 104842. https://doi.org/10.1016/j.isci.2022.104842 Link>
  3. Bierbach, D., Gómez-Nava, L., Francisco, F. A., Lukas, J., Musiolek, L., Hafner, V. V., Landgraf, T., Romanczuk, P., & Krause, J. (2022). Live fish learn to anticipate the movement of a fish-like robot. Bioinspiration & Biomimetics, 17(6), 065007. https://doi.org/10.1088/1748-3190/ac8e3e Link>
  4. Nader, Y., Sixt, L., & Landgraf, T. (2022). DNNR: Differential Nearest Neighbors Regression. Proceedings of the 39th International Conference on Machine Learning, 16296–16317. https://proceedings.mlr.press/v162/nader22a.html Link>
  5. Sixt, L., Schuessler, M., Popescu, O.-I., Weiß, P., & Landgraf, T. (2022, March). Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset. Proceedings of the International Conference on Learning Representations. https://openreview.net/forum?id=v6s3HVjPerv Link>
  6. Herrmann, L., Granz, M., & Landgraf, T. (2022, October). Chaotic Dynamics are Intrinsic to Neural Network Training with SGD. Advances in Neural Information Processing Systems. https://openreview.net/forum?id=ffy-h0GKZbK Link>
  7. Sixt, L., Schuessler, M., Popescu, O.-I., Weiß, P., & Landgraf, T. (2022). Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset. arXiv. https://doi.org/10.48550/arXiv.2204.11642 Link>
  8. Nader, Y., Sixt, L., & Landgraf, T. (2022). DNNR: Differential Nearest Neighbors Regression. arXiv. https://doi.org/10.48550/arXiv.2205.08434 Link>
  9. Sixt, L., & Landgraf, T. (2022). A Rigorous Study Of The Deep Taylor Decomposition. arXiv. https://doi.org/10.48550/arXiv.2211.08425 Link>

2021

  1. Wild, B., Dormagen, D. M., Zachariae, A., Smith, M. L., Traynor, K. S., Brockmann, D., Couzin, I. D., & Landgraf, T. (2021). Social networks predict the life and death of honey bees. Nature Communications, 12(1), 1110. https://doi.org/10.1038/s41467-021-21212-5 Link>
  2. Bierbach, D., Francisco, F., Lukas, J., Landgraf, T., Maxeiner, M., Romanczuk, P., Musiolek, L., Hafner, V. V., & Krause, J. (2021, July). Biomimetic robots promote the 3Rs Principle in animal testing. ALIFE 2021: The 2021 Conference on Artificial Life. https://doi.org/10.1162/isal_a_00375 Link>
  3. Ilgün, A., Angelov, K., Stefanec, M., Schönwetter-Fuchs, S., Stokanic, V., Vollmann, J., Hofstadler, D. N., Kärcher, M. H., Mellmann, H., Taliaronak, V., Kviesis, A., Komasilovs, V., Becher, M. A., Szopek, M., Dormagen, D. M., Barmak, R., Bairaktarov, E., Broisin, M., Thenius, R., … Schmickl, T. (2021, July). Bio-Hybrid Systems for Ecosystem Level Effects. ALIFE 2021: The 2021 Conference on Artificial Life. https://doi.org/10.1162/isal_a_00396 Link>
  4. Worm, M., Landgraf, T., & von der Emde, G. (2021). Electric signal synchronization as a behavioural strategy to generate social attention in small groups of mormyrid weakly electric fish and a mobile fish robot. Biological Cybernetics. https://doi.org/10.1007/s00422-021-00892-8 Link>
  5. Paffhausen, B. H., Petrasch, J., Wild, B., Meurers, T., Schülke, T., Polster, J., Fuchs, I., Drexler, H., Kuriatnyk, O., Menzel, R., & Landgraf, T. (2021). A flying platform to investigate neuronal correlates of navigation in the honey bee (Apis mellifera). Frontiers in Behavioral Neuroscience, 15. https://doi.org/10.3389/fnbeh.2021.690571 Link>
  6. Lukas, J., Kalinkat, G., Miesen, F. W., Landgraf, T., Krause, J., & Bierbach, D. (2021). Consistent Behavioral Syndrome Across Seasons in an Invasive Freshwater Fish. Frontiers in Ecology and Evolution, 8. https://doi.org/10.3389/fevo.2020.583670 Link>
  7. Landgraf, T., Gebhardt, G. H. W., Bierbach, D., Romanczuk, P., Musiolek, L., Hafner, V. V., & Krause, J. (2021). Animal-in-the-Loop: Using Interactive Robotic Conspecifics to Study Social Behavior in Animal Groups. Annual Review of Control, Robotics, and Autonomous Systems, 4(1), 487–507. https://doi.org/10.1146/annurev-control-061920-103228 Link>
  8. Klamser, P. P., Gómez-Nava, L., Landgraf, T., Jolles, J. W., Bierbach, D., & Romanczuk, P. (2021). Impact of Variable Speed on Collective Movement of Animal Groups. Frontiers in Physics, 9. https://www.frontiersin.org/articles/10.3389/fphy.2021.715996 Link>
  9. Smith, M. L., Davidson, J. D., Wild, B., Dormagen, D. M., Landgraf, T., & Couzin, I. D. (2021). The dominant axes of lifetime behavioral variation in honey bees. bioRxiv. https://doi.org/10.1101/2021.04.15.440020 Link>
  10. Wild, B., Dormagen, D. M., Smith, M. L., & Landgraf, T. (2021). Learning to embed lifetime social behavior from interaction dynamics. bioRxiv. https://doi.org/10.1101/2021.09.01.458538 Link>
  11. Klamser, P. P., Gómez-Nava, L., Landgraf, T., Jolles, J. W., Bierbach, D., & Romanczuk, P. (2021). Impact of Variable Speed on Collective Movement of Animal Groups. arXiv. https://doi.org/10.48550/arXiv.2106.00959 Link>
  12. Solopova, V., Popescu, O.-I., Chikobava, M., Romeike, R., Landgraf, T., & Benzmüller, C. (2021). A German Corpus of Reflective Sentences. Proceedings of the 18th International Conference on Natural Language Processing (ICON), 593–600. https://aclanthology.org/2021.icon-main.72 Link>

2020

  1. Sixt, L., Granz, M., & Landgraf, T. (2020). When Explanations Lie: Why Many Modified BP Attributions Fail. Proceedings of the International Conference on Machine Learning, 1. https://proceedings.icml.cc/paper/2020/hash/af21d0c97db2e27e13572cbf59eb343d Link>
  2. Bierbach, D., Mönck, H. J., Lukas, J., Habedank, M., Romanczuk, P., Landgraf, T., & Krause, J. (2020). Guppies Prefer to Follow Large (Robot) Leaders Irrespective of Own Size. Frontiers in Bioengineering and Biotechnology, 8. https://doi.org/10.3389/fbioe.2020.00441 Link>
  3. Sixt, L., Schuessler, M., Weiß, P., & Landgraf, T. (2020). Interpretability Through Invertibility: A Deep Convolutional Network With Ideal Counterfactuals And Isosurfaces. https://openreview.net/forum?id=8YFhXYe1Ps Link>
  4. Wild, B., Dormagen, D., Smith, M. L., & Landgraf, T. (2020). Individuality in the hive - Learning to embed lifetime social behaviour of honey bees. https://openreview.net/forum?id=2LBhynkS2SC Link>
  5. Musiolek, L., Hafner, V. V., Krause, J., Landgraf, T., & Bierbach, D. (2020). Robofish as Social Partner for Live Guppies. In V. Vouloutsi, A. Mura, F. Tauber, T. Speck, T. J. Prescott, & P. F. M. J. Verschure (Eds.), Biomimetic and Biohybrid Systems (pp. 270–274). Springer International Publishing. https://doi.org/10.1007/978-3-030-64313-3_26
  6. Wario, F., Wild, B., Dormagen, D., Landgraf, T., & Trianni, V. (2020). Motion Dynamics of Foragers in Honey Bee Colonies. In M. Dorigo, T. Stützle, M. J. Blesa, C. Blum, H. Hamann, M. K. Heinrich, & V. Strobel (Eds.), Swarm Intelligence (pp. 203–215). Springer International Publishing. https://doi.org/10.1007/978-3-030-60376-2_16
  7. Jolles, J. W., Weimar, N., Landgraf, T., Romanczuk, P., Krause, J., & Bierbach, D. (2020). Group-level patterns emerge from individual speed as revealed by an extremely social robotic fish. Biology Letters, 16(9), 20200436. https://doi.org/10.1098/rsbl.2020.0436 Link>
  8. Schulz, K., Sixt, L., Tombari, F., & Landgraf, T. (2020, May). Restricting the Flow: Information Bottlenecks for Attribution. Proceedings of the International Conference on Learning Representations. https://openreview.net/forum?id=S1xWh1rYwB Link>
  9. Schulz, K., Sixt, L., Tombari, F., & Landgraf, T. (2020). Restricting the Flow: Information Bottlenecks for Attribution. arXiv. https://doi.org/10.48550/arXiv.2001.00396 Link>
  10. Landgraf, T., Moenck, H. J., Gebhardt, G. H. W., Weimar, N., Hocke, M., Maxeiner, M., Musiolek, L., Krause, J., & Bierbach, D. (2020). Socially competent robots: adaptation improves leadership performance in groups of live fish. arXiv. https://doi.org/10.48550/arXiv.2009.06633 Link>

2019

  1. Paffhausen, B., Petrasch, J., Wild, B., Fuchs, I., Drexler, H., Kuriatnyk, O., Meurers, T., Landgraf, T., & Menzel, R. (2019). Neural correlates of mushroom body output neurons measured during flight of a harnessed honey bee on a quad copter.
  2. Menzel, R., Tison, L., Fischer-Nakai, J., Cheeseman, J., Balbuena, M. S., Chen, X., Landgraf, T., Petrasch, J., Polster, J., & Greggers, U. (2019). Guidance of Navigating Honeybees by Learned Elongated Ground Structures. Frontiers in Behavioral Neuroscience, 12. https://doi.org/10.3389/fnbeh.2018.00322 Link>
  3. Polster, J., Petrasch, J., Menzel, R., & Landgraf, T. (2019). Reconstructing the visual perception of honey bees in complex 3-D worlds. arXiv. https://doi.org/10.48550/arXiv.1811.07560 Link>

2018

  1. Boenisch, F., Rosemann, B., Wild, B., Dormagen, D., Wario, F., & Landgraf, T. (2018). Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence. Frontiers in Robotics and AI, 5. https://doi.org/10.3389/frobt.2018.00035 Link>
  2. Bierbach, D., Lukas, J., Bergmann, A., Elsner, K., Höhne, L., Weber, C., Weimar, N., Arias-Rodriguez, L., Mönck, H. J., Nguyen, H., Romanczuk, P., Landgraf, T., & Krause, J. (2018). Insights into the Social Behavior of Surface and Cave-Dwelling Fish (Poecilia mexicana) in Light and Darkness through the Use of a Biomimetic Robot. Frontiers in Robotics and AI, 5. https://doi.org/10.3389/frobt.2018.00003 Link>
  3. Bierbach, D., Landgraf, T., Romanczuk, P., Lukas, J., Nguyen, H., Wolf, M., & Krause, J. (2018). Using a robotic fish to investigate individual differences in social responsiveness in the guppy. Royal Society Open Science, 5(8), 181026. https://doi.org/10.1098/rsos.181026 Link>
  4. Worm, M., Landgraf, T., Prume, J., Nguyen, H., Kirschbaum, F., & Emde, G. von der. (2018). Evidence for mutual allocation of social attention through interactive signaling in a mormyrid weakly electric fish. Proceedings of the National Academy of Sciences, 115(26), 6852–6857. https://doi.org/10.1073/pnas.1801283115 Link>
  5. Sixt, L., Wild, B., & Landgraf, T. (2018). RenderGAN: Generating Realistic Labeled Data. Frontiers in Robotics and AI, 5. https://doi.org/10.3389/frobt.2018.00066 Link>
  6. Müller, J., Nawrot, M., Menzel, R., & Landgraf, T. (2018). A neural network model for familiarity and context learning during honeybee foraging flights. Biological Cybernetics, 112(1-2), 113–126. https://doi.org/10.1007/s00422-017-0732-z Link>
  7. Boenisch, F., Rosemann, B., Wild, B., Wario, F., Dormagen, D., & Landgraf, T. (2018). Tracking all members of a honey bee colony over their lifetime. arXiv. https://doi.org/10.48550/arXiv.1802.03192 Link>
  8. Wild, B., Sixt, L., & Landgraf, T. (2018). Automatic localization and decoding of honeybee markers using deep convolutional neural networks. arXiv. https://doi.org/10.48550/arXiv.1802.04557 Link>
  9. Landgraf, T., Bierbach, D., Kirbach, A., Cusing, R., Oertel, M., Lehmann, K., Greggers, U., Menzel, R., & Rojas, R. (2018). Dancing Honey bee Robot Elicits Dance-Following and Recruits Foragers. arXiv. https://doi.org/10.48550/arXiv.1803.07126 Link>
  10. Mönck, H. J., Jörg, A., von Falkenhausen, T., Tanke, J., Wild, B., Dormagen, D., Piotrowski, J., Winklmayr, C., Bierbach, D., & Landgraf, T. (2018). BioTracker: An Open-Source Computer Vision Framework for Visual Animal Tracking. arXiv. https://doi.org/10.48550/arXiv.1803.07985 Link>

2017

  1. Sixt, L., Wild, B., & Landgraf, T. (2017). RenderGAN: Generating Realistic Labeled Data. http://arxiv.org/abs/1611.01331 Link>
  2. Landgraf, T., & Nawrot, M. (2017). Künstliche Mini-Gehirne für Roboter. In Planen und Handeln (pp. 135–150). Springer Spektrum, Wiesbaden.
  3. Lam, C., Li, Y., Landgraf, T., & Nieh, J. (2017). Dancing attraction: followers of honey bee tremble and waggle dances exhibit similar behaviors. Biology Open, bio–025445.
  4. Wario, F., Wild, B., Rojas, R., & Landgraf, T. (2017). Automatic detection and decoding of honey bee waggle dances. PLOS ONE, 12(12), e0188626. https://doi.org/10.1371/journal.pone.0188626 Link>

2016

  1. Landgraf, T., Bierbach, D., Nguyen, H., Muggelberg, N., Romanczuk, P., & Krause, J. (2016). RoboFish: increased acceptance of interactive robotic fish with realistic eyes and natural motion patterns by live Trinidadian guppies. Bioinspiration & Biomimetics, 11(1), 015001.

2015

  1. Wario, F., Wild, B., Couvillon, M. J., Rojas, R., & Landgraf, T. (2015). Automatic methods for long-term tracking and the detection and decoding of communication dances in honeybees. Frontiers in Ecology and Evolution, 3. https://doi.org/10.3389/fevo.2015.00103

2014

  1. Landgraf, T., Nguyen, H., Schröer, J., Szengel, A., Clément, R. J. G., Bierbach, D., & Krause, J. (2014). Blending in with the shoal: robotic fish swarms for investigating strategies of group formation in guppies. Conference on Biomimetic and Biohybrid Systems, 178–189.
  2. Jin, N., Landgraf, T., Klein, S., & Menzel, R. (2014). Walking bumblebees memorize panorama and local cues in a laboratory test of navigation. Animal Behaviour, 97, 13–23. http://www.sciencedirect.com/science/article/pii/S0003347214003273 Link>
  3. Worm, M., Landgraf, T., Nguyen, H., & von der Emde, G. (2014). Electro-communicating dummy fish initiate group behavior in the weakly electric fish Mormyrus rume. Conference on Biomimetic and Biohybrid Systems, 446–448.

2013

  1. Landgraf, T. (2013). RoboBee: A Biomimetic Honeybee Robot for the Analysis of the Dance Communication System [PhD thesis, Berlin, Freie Universität Berlin, 2013]. http://www.diss.fu-berlin.de/diss/receive/FUDISS_thesis_000000094818?lang=de Link>
  2. Landgraf, T., Nguyen, H., Forgo, S., Schneider, J., Schröer, J., Krüger, C., Matzke, H., Clément, R. O., Krause, J., & Rojas, R. (2013). Interactive robotic fish for the analysis of swarm behavior. International Conference in Swarm Intelligence, 1–10. http://link.springer.com/chapter/10.1007/978-3-642-38703-6_1 Link>
  3. Helgadóttir, L. I., Haenicke, J., Landgraf, T., Rojas, R., & Nawrot, M. P. (2013). Conditioned behavior in a robot controlled by a spiking neural network. Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference On, 891–894. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6696078 Link>
  4. Landgraf, T., Wild, B., Ludwig, T., Nowak, P., Helgadottir, L., Daumenlang, B., Breinlinger, P., Nawrot, M., & Rojas, R. (2013). NeuroCopter: neuromorphic computation of 6D ego-motion of a quadcopter. Conference on Biomimetic and Biohybrid Systems, 143–153. http://link.springer.com/chapter/10.1007/978-3-642-39802-5_13 Link>

2012

  1. Landgraf, T., Akkad, R., Nguyen, H., Clément, R. O., Krause, J., & Rojas, R. (2012). A Multi-agent Platform for Biomimetic Fish. Conference on Biomimetic and Biohybrid Systems, 365–366. http://link.springer.com/chapter/10.1007/978-3-642-31525-1_44 Link>
  2. Helgadottir, L. I., Haenicke, J., Landgraf, T., & Nawrot, M. P. (2012). A Robotic Platform for Spiking Neural Control Architectures. Bernstein Conference 2012, Munich, Germany, 12 Sep - 14 Sep, 2012., 154.
  3. Landgraf, T., Oertel, M., Kirbach, A., Menzel, R., & Rojas, R. (2012). Imitation of the honeybee dance communication system by means of a biomimetic robot. Conference on Biomimetic and Biohybrid Systems, 132–143. http://link.springer.com/chapter/10.1007/978-3-642-31525-1_12 Link>

2011

  1. Meyer, J., Haenicke, J., Landgraf, T., Schmuker, M., Rojas, R., & Nawrot, M. (2011). A digital receptor neuron connecting remote sensor hardware to spiking neural networks. BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.
  2. Landgraf, T. (2011). Blending into the Hive: A Novel Biomimetic Honeybee Robot for the Analysis of the Dance Communication System. International Workshop on Bio-Inspired Robots, Nantes April 6-8.
  3. Landgraf, T., Rojas, R., Nguyen, H., Kriegel, F., & Stettin, K. (2011). Analysis of the Waggle Dance Motion of Honeybees for the Design of a Biomimetic Honeybee Robot. PLoS ONE, 6(8), e21354. https://doi.org/10.1371/journal.pone.0021354 Link>

2007-2010

  1. Landgraf, T., Moballegh, H., & Rojas, R. (2008). Design and development of a robotic bee for the analysis of honeybee dance communication. Applied Bionics and Biomechanics, 5(3), 157–164. http://www.tandfonline.com/doi/abs/10.1080/11762320802617552 Link>
  2. Hussaini, S. A., Bogusch, L., Landgraf, T., & Menzel, R. (2009). Sleep deprivation affects extinction but not acquisition memory in honeybees. Learning & Memory, 16(11), 698–705. http://learnmem.cshlp.org/content/16/11/698.short Link>
  3. Landgraf, T., & Rojas, R. (2007). Tracking honey bee dances from sparse optical flow fields. https://refubium.fu-berlin.de/handle/fub188/19039 Link>
  4. Landgraf, T., Oertel, M., Rhiel, D., & Rojas, R. (2010). A biomimetic honeybee robot for the analysis of the honeybee dance communication system. Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference On, 3097–3102.