Nambusubramaniyan S., Knoblauch A.: "Beyond Adam: Achieving Biological Fidelity with Dopamine, a Reward-based Optimizer for Accelerated Gradient-Free Learning". 5th International Conference on Mathematics of Neuroscience and AI (2024).

Stricker P., Knoblauch, A., Röhrbein, F.: Weight Perturbation and Competitive Hebbian Plasticity for Training Sparse Excitatory Neural Networks . International Joint Conference on Neural Networks ( IJCNN 2024).

Nambusubramaniyan, S., Knoblauch, A., Röhrbein, F.: Asynchronous Derivative-Free Learning Solving Synaptic Credit Assignment in Recurrent Neural Networks. accepted for Cosyne 2024.

Kaden, S., Köpferl, K., Andreas, B.,  Röhrbein, F.: The robot should motivate: „Come on, you can still do it!“ An Exploratory Pilot Study. 2nd Workshop on Care Robots for Older Adults at RO-MAN 2023 (2023). croa-roman.com

Kohler M., Röhrbein F., Knoll A., Schäffer A., Jörntell H.: The Bcm rule allows a spinal cord model to learn rhythmic movements. Biol Cybern (2023). doi:10.1007/s00422-023-00970-z

Qu S., Zou T., Röhrbein F., Lu C., Chen G., Tao D., Jiang C.: Upcycling Models under Domain and Category Shift. Accepted for CVPR 2023.

Kohler M., Bengtsson F., Stratmann P.  Röhrbein F, Knoll A., Schäffer A.A., Jörntell H.: Diversified physiological sensory input connectivity questions the existence of distinct classes of spinal interneurons. iScience,Volume 25, Issue 4,2022,104083,ISSN 2589-0042, doi.org/10.1016/j.isci.2022.104083

Röhrbein, F., Tani, J., eds. Insights in Neurorobotics: 2021. Lausanne: Frontiers Media SA. doi:10.3389/978-2-83250-590-8

Nambusubramaniyan S., Knoblauch A., Röhrbein F.: Scalable Layer-Parallel Readout Training Rule for Self-Organizing Recurrent Neural Networks. Bernstein Conference 2022.  doi:10.12751/nncn.bc2022.165

A. K. Gharehbagh, R. Judeh, J. Ng, C. v. Reventlow and Röhrbein F.: Real-time 3D Semantic Mapping based on Keyframes and Octomap for Autonomous Cobot. 2021 9th International Conference on Control, Mechatronics and Automation (ICCMA), 2021, pp. 33-38, doi: 10.1109/ICCMA54375.2021.9646203

M. Kohler, P. Stratmann, F. Röhrbein, A. Knoll, A. A. Schäffer, H. Jörntell: The BCM rule allows a spinal cord model to learn rhythmic movements. bioRxiv 2021.11.12.467473 doi: 10.1101/2021.11.12.467473

Senden, M., Peters, J., Röhrbein, F., Deco, G., Goebel, R.: The Embodied Brain: Computational Mechanisms of Integrated Sensorimotor Interactions With a Dynamic Environment. Frontiers in Computational Neuroscience, Vol. 14. doi:10.3389/fncom.2020.00053

Kohler, M., Stratmann, P., Röhrbein, F., Knoll, A., Albu-Schäffer, A., Jörntell, H.: Biological data questions the support of the self inhibition required for pattern generation in the half center model. PloS one 15 (9), e0238586 doi:10.1371/journal.pone.0238586

Tenbrink, L., Feldotto, B., Röhrbein, F., Knoll, A.: Motion Prediction of Virtual Patterns, Human Hand Motions, and a simplified Hand Manipulation Task with Hierarchical Temporal Memory. 2019 IEEE International Conference on Cyborg and Bionic Systems (CBS): 305-9. doi:10.1109/CBS46900.2019.9114416

Chen, G., Chen, J., Lienen, M., Conradt, J., Röhrbein, F., Knoll, A.: FLGR: Fixed length gists representation learning for RNN-HMM hybrid-based neuromorphic continuous gesture recognition. Frontiers in Neuroscience, Vol. 13, p. 73. doi:10.3389/fnins.2019.00073/

Bing, Z., Meschede, C., Huang, K., Chen, G., Röhrbein, F., Akl, M., Knoll, A.: End to end learning of spiking neural network based on R-STDP for a lane keeping vehicle. 2018 IEEE International Conference on Robotics and Automation (ICRA): doi:10.1109/ICRA.2018.8460482.

Feldotto, B., Walter, F., Röhrbein, F., Knoll, A.: Hebbian learning for online prediction, neural recall and classical conditioning of anthropomimetic robot arm motions. Bioinspiration & Biomimetics.doi:10.1088/1748-3190/aae1c2

Dikov, G. , Firouzi, M., Röhrbein, F. Conradt, J., Richter, C. Spiking cooperative stereo-matching at 2ms latency with neuromorphic hardware. Proc. of Living Machines 2017: 119-137, Springer. doi:10.1007/978-3-319-63537-8_11

Falotico, E., Vannucci, L., Ambrosano, A., Albanese, U., Ulbrich, S., Tieck, J., Hinkel, G., Kaiser, J., Peric, I., Denninger, O., Cauli, N., Kirtay, M., Roennau, A., Klinker, G., von Arnim, A., Guyot, L., Peppicelli, D., Martinez-Canada, P., Ros, E., Maier, P., Weber, S., Huber, M., Plecher, D., Röhrbein, F., Deser, S., Roitberg, A., van der Smagt, P., Dillman, R., Levi, P., Laschi, C., Knoll, A., Gewaltig, M. (2017). Connecting artificial brains to robots in a comprehensive simulation framework: The neurorobotics platform. Frontiers in Neurorobotics, doi: 10.3389/fnbot.2017.00002

Qiao, H., Zhang, H., Röhrbein, F. . Special Issue on Human-inspired Computing. International Journal of Automation and Computing, 14(5):501-502. doi:10.1007/s11633-017-1097-4

Bing, Z., Cheng, L., Chen, G., Röhrbein, F., Huang, K., Knoll, A. . Towards autonomous locomotion: CPG-based control of smooth 3d slithering gait transition of a snake-like robot. Bioinspiration & Biomimetics, 12(3):03500. doi:10.1088/1748-3190/aa644c

Oess, T, Krichmar, J.L., Röhrbein, F. . A Computational Model for Spatial Navigation Based on Reference Frames in the Hippocampus, Retrosplenial Cortex, and Posterior Parietal Cortex. Frontiers in Neurorobotics. doi:10.3389/fnbot.2017.00004

Knoll, A., Röhrbein, F, Kuhn, A., Akl, M., Sharma, K. . Neurorobotics - From computational neuroscience to intelligent robots and back. Informatik-Spektrum, 40(2): 161- 164. Book link here.

Röhrbein, F., Gewaltig, M.-O., Laschi, C., Klinker, G., Levi, P., Knoll, A. . The Neurorobotic Platform: A simulation environment for brain-inspired robotics. ISR International Symposium on Robotics, Munich. ISBN: 978-3-8007-4231-8

Richter, C., Jentzsch, S., Hostettler, R., Garrido, J., Ros, E., Knoll, A., Röhrbein, F., van der Smagt, P., Conradt, J. . Musculoskeletal Robots: Scalability in Neural Control, in IEEE Robotics & Automation Magazine, vol. 23, no. 4, pp. 128-137. doi:10.1109/MRA.2016.2535081

Walter, F., Röhrbein, F. Knoll, A.: Neuromorphic Implementations of Neurobiological Learning Algorithms for Spiking Neural Networks. Neural Networks, 72:152 - 167, 2015. doi:10.1016/j.neunet.2015.07.004

von Waldow, U., Röhrbein, F.: Structure Learning in Bayesian Networks with Parent Divorcing. EAPCogSci, p.1-6. pdf here

Griffiths, S., Natale, C., Araujo, R., Veiga, G., Chiacchio, P., Röhrbein, F., Chiaverini, S. Lafrenz, R.: The ECHORD project: A general perspective. In Röhrbein, Veiga, Natale, editors, Gearing Up and Accelerating Cross‐fertilization between Academic and Industrial Robotics Research in Europe, nr. 94 in Springer Tracts in Advanced Robotics, Springer International Publishing, 1-24. doi:10.1007/978-3-319-02934-4_1

Griffiths, S., Voss, K., Röhrbein, F.: Industry-Academia Collaborations in Robotics: Comparing Asia, Europe and North-America. In IEEE International Conference on Robotics and Automation (ICRA), 748-753. doi:10.1109/ICRA.2014.6906938

Griffiths, S., Voss, L., Röhrbein, F.: Proximity in Industry-Academia Collaborations: The Case of the ECHORD Project. International Journal of Materials Science and Engineering, 3(1):71-76. pdf here.

Griffiths, S., Röhrbein, F.:Technology transfer in academia-industry collaborations. KI - Künstliche Intelligenz, 365-368. doi:10.1007/s13218-013-0276-x

Zetzsche, C., Röhrbein, F., Schill, K.: Weber’s Law and the Statistics of the Natural Environment. Fechner Day 2010 - 26th Annual Meeting of the International Society for Psychophysics. Padua, Italy: International Society for Psychophysics.

Weiler, D., Röhrbein, F., Eggert, J.: Level-Set Segmentation with Contour based Object Representation. Proceedings of International Joint Conference on Neural Networks (IJCNN), 3327-3334. doi:10.1109/IJCNN.2009.5179047

Riad, M., Röhrbein, F., Einecke, N., Eggert, J.: Exploiting Spatial Relational Knowledge for Visual Cognitive Tasks. In N. Taatgen and H. van Rijn (Eds.), Proceedings of the 31th Annual Conference of the Cognitive Science Society, 1535-1540. pdf here.

Rebhan, S., Röhrbein, F., Eggert, J., Körner, E.: Attention modulation using short- and long-term knowledge. In Gasteratos, A., Vincze, M., Tsotsos, J.K. (Eds), Proceedings of the 6th International Conference on Computer Vision Systems (ICVS), Lecture Notes in Computer Science 5008, Springer, Berlin: 151-160. doi:10.1007/978-3-540-79547-6_15

Röhrbein, F., Eggert, J., Körner, E.: A cortex-inspired neural-symbolic network for knowledge representation. Proceedings of the IJCAI Workshop on Neural-Symbolic Learning and Reasoning (NeSy). CEUR Workshop Proceedings. pdf here.

Röhrbein, F., Eggert, J., Hasler, S., Richter, A., Hirsch, M., Körner, E.: ProCog - a biologically motivated relational network. Proceedings of the 7th International Conference on Cognitive Modeling (ICCM), Trieste, Italy: 393-394. pdf here.

Meyer, G., Wuerger, S., Röhrbein, F., Zetzsche, C.: Low-level integration of auditory and visual motion signals requires spatial co-localisation. Experimental Brain Research, 166(3-4): 538-547. doi:10.1007/s00221-005-2394-7

Hofbauer, M., Wuerger, S., Meyer, G., Röhrbein, F., Schill, K., Zetzsche, C.: Catching audiovisual mice: Predicting the arrival time of auditory-visual motion signals. Cognitive, Affective and Behavioral Neuroscience, 4(2): 241-250. doi:10.3758/CABN.4.2.241

Röhrbein, F., Schill, K., Baier, V., Stein, K., Zetzsche, C., Brauer, W.: Motion shapes: Empirical studies and neural modeling. In Freksa, C., Brauer, W., Habel, C., Wender, K. (Eds), Spatial Cognition III - Routes and Navigation, Human Memory and Learning, Spatial Representation and Spatial Learning, Lecture Notes in Artificial Intelligence, vol. 2685, Springer, Berlin: 305-320. doi:10.1007/3-540-45004-1_18

Zetzsche, C., Röhrbein, F., Hofbauer, M., Schill, K.: Audio-visual sensory interactions and the statistical covariance of the natural environment, Proceedings of the Forum Acusticum. Special Issue of the Journal Revista de Acustica, Vol. XXXIII, no. 3-4, 2002, PSY-05-003-IP: 1-6. pdf here.

Zetzsche, C., Röhrbein, F.: Nonlinear and extra-classical receptive field properties and the statistics of natural scenes. Network: Computation in Neural Systems, 12: 331-350. doi:10.1088/0954-898X/12/3/306

Röhrbein, F., Zetzsche, C.: Auditory-visual interactions and the covariance structure generated by relative movements in natural environments. In Guidati, G., Hunt, H., Heiss, A. (Eds), Proceedings of the Seventh International Congress on Sound and Vibration (ICSV), International Institute of Acoustics and Vibration, Kramer Technology Publishing: 2427-2434.

Zetzsche, C., Röhrbein, F.: Cortical complex cells and the statistical structure of the natural environment. In Baratoff, G., Neumann, H. (Eds), 3. Workshop Dynamic Perception, Vol. 9 of Proceedings in Artificial Intelligence, Berlin: 57-62.

Röhrbein, F., Zetzsche, C.: Exploitation of natural image statistics by biological vision systems: 1/f 2 power spectra and self-similar bandpass decompositions. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR-98, IEEE Computer Society, Los Alamitos, CA: 86-91.