Neurorobotik
Publications
Neurorobotik 

Lab Publications

2025

Coelho S., Kaden S., Beccard M., Röhrbein F., Sanchez-Stockhammer C. “Another bit. Upwards. Okay, stop.” Do we talk differently to humans and robots when assembling a object together?. MuC 2025, Chemnitz, doi.org/10.1145/3743049.3748536
Oyunbat G., Lange S., Bing Z., Röhrbein F.: Position Estimation Experiment on Bio-inspired Robotic Mouse with Inertial Measurement Unit. SWARM 2025, Göttingen.

Dang Y., Zhang Y., Huang Y., Wen L., Zhang Y., Bing Z., Röhrbein F., Knoll A.: Instantaneous Contact Localization on a Magnetically Transduced Tapered Whisker. 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Stricker P., Knoblauch A., Röhrbein F.: Weight Perturbation and Competitive Hebbian Plasticity for Sparse Text Representations. Bernstein Conference 2025, Frankfurt am Main.
Coelho, S. G., Kaden, S., Beccard, M., Röhrbein, F., Sanchez-Stockhammer, C.: “Another bit. Upwards. Okay, stop.” Do we talk differently to humans and robots when assembling an object together? Proceedings of Mensch und Computer 2025, Chemnitz.
Krug, V., Röhrbein, F., Stober, S.: Intersectional Bias Quantification in Facial Image Processing with Pre-Trained ImageNet Classifiers. Proceedings of IJCNN.
Langer D., Dettmann A., Kaden S., Legler F., Röhrbein F., Bullinger A. C.: Transparency of Robot Motion Intent Depending on Human Attention. 1st German Robotics Conference, Nürnberg.
Kaden S., Vohr F., Beesk M., Lange S., Röhrbein F.: Onboard AI-Driven Anomaly Detection and Speech Interaction for Autonomous Mobile Robots. 1st German Robotics Conference, Nürnberg.
Konitzer J., Lange S., Kaden S., Röhrbein F.: From speech to action: Translation and execution of natural language commands to control a robot. 1st German Robotics Conference, Nürnberg.
Lecomte J., Neumeier M., Schindler B., Kloss A., von Arnim A., Röhrbein F.: CORINNE - Semi-supervised and federated learning for industrial welding cobots. 1st German Robotics Conference, Nürnberg.
Schwarz L., Kaden S., Röhrbein F.: Prototype-based Approximate Geodesics for Learning from Demonstrations. 1st German Robotics Conference, Nürnberg.

Dang Y., Xu Q., Zhang Y., Yao X., Zhang L., Bing Z., Röhrbein F., Knoll A.: Whisker-based Active Tactile Perception for Contour Reconstruction. IEEE International Conference on Robotics and Automation (ICRA).

2024

Kuske N., Röhrbein F.: Habit-inspired methods for embodied intelligence. 2024 IOP Conference Series: Materials Science and Engineering. doi:10.1088/1757-899X/1321/1/012002.
Mollard F., Becker M., Röhrbein F.: Adversarial Evasion Attacks on Computer Vision using SHAP Values. 10th bwHPC Symposium in Freiburg, Germany.
Stricker P., Knoblauch A., Röhrbein F.: Energy-Efficient Information Representation in MNIST Classification Using Biologically Inspired Learning. 10th bwHPC Symposium in Freiburg, Germany.
Su H., Manoonpong P., Röhrbein F.: Editorial: Horizons in Neurorobotics 2022. Frontiers in Neurorobotics, Vol. 18, 2024. doi:10.3389/fnbot.2024.1426355.
Ahn M., Edelman B.J., He B., Müller-Putz G., Röhrbein F.: Editorial: Advances in hybrid and application-driven BCI systems. Frontiers in Human Neuroscience, Vol. 18, 2024. doi:10.3389/fnhum.2024.1498196.

Haotian L., Sanqing Q., Lu F., Zongtao B., Röhrbein F., Knoll A., Guang C.: PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds, Proceedings of the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024).

Kaden S., Schwarz L., Röhrbein F.: A Research Platform for Human-Robot-Interaction with Focus on Collaborative Assembly Scenarios. IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Aug 26 - Aug 30, 2024 to be held in Pasadena, California.

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).

Röhrbein F., Kress K.: The Digital Laboratory, Artificial Intelligence and the Interface Standard LADS. Trend Report 2024 - Analytical, Bio and Laboratory Technology for Markets, Developments, Potential, p. 14-15.

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).

Qu S., Zou T., He L., Röhrbein F., Knoll A., Chen G., Jiang C. (2024).: Lead: Learning decomposition for source-free universal domain adaptation. arXiv:2403.03421.

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

2023

Abdelaal O.M. Röhrbein F.: Policy Learning with Spiking Neural Network for Robot Manipulation Tasks . 1st International Conference on Hybrid Societies 2023. Monarch.qucosa.de.

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

Noack B., Röhrbein F., Notni G.: Event-Based Sensor Fusion in Human-Machine Teaming. 60th ILMENAU SCIENTIFIC COLLOQUIUM (2023).

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

Röhrbein F., Senden M. (2023) Editorial: The embodied brain: computational mechanisms of integrated sensorimotor interactions with a dynamic environment volume II. Front. Comput. Neurosci. 17:1203717. doi: 10.3389/fncom.2023.1203717

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.

2022

Feldotto B., Lengenfelder H., Röhrbein F., Knoll A.C. Network Layer Analysis for a RL-Based Robotic Reaching Task.  Frontiers in Robotics and AI , Volume 9, 2022,  doi:10.3389/frobt.2022.799644

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

Abbas A.A, Röhrbein F., Hamker F.. The Role of the Superior Colliculus in Attention Demanding Tasks: A Computational Model.  forum.fens.org/abstract-e-book/

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

Kuske N., Ragni M., Röhrbein F., Vitay J., Hamker F.: Demands and potentials of different levels of neuro-cognitive models für human spatial cognition. In, E. Ferstl, L. Konieczny, & R. Stülpnagel (Eds.), Proceedings of KogWiss2022, the 15th Biannual Conference of the German Society for Cognitive Science (pp. 115-116). Albert-Ludwigs-Universität Freiburg. doi:10.6094/UNIFR/229611

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

Vijayan P., Atoofi P., Fietzek T., Röhrbein F.: Trajectory learning for an iCub arm generated by CPGs. Bernstein Conference 2022.  doi:10.12751/nncn.bc2022.194

2021

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

Röhrbein F.: Frontiers in Neurorobotics–Editor’s Pick 2021. pdf here.

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

2020

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

Aljalbout, E., Walter, F., Röhrbein, F., Knoll, A.: Task-independent spiking central pattern generator: A learning-based approach. Neural Processing Letters 51 (3), 2751-2764. doi.org/10.1007/s11063-020-10224-9

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

Chen, G., Cao, H., Conradt, J., Tang, H., Röhrbein, F., Knoll, A.: Event-based neuromorphic vision for autonomous driving: a paradigm shift for bio-inspired visual sensing and perception. IEEE Signal Processing Magazine 37 (4), 34-49. doi:10.1109/MSP.2020.2985815

2019

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., Cao, H., Ye, C., Zhang, Z., Liu, X., Mo, X., Qu, Z., Conradt, J., Röhrbein, F., Knoll, F. (2019): Multi-cue event information fusion for pedestrian detection with neuromorphic vision sensors. Frontiers in Neurorobotics. Vol. 13, p. 10. doi:10.3389/fnbot.2019.00010

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/

2018

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.

Chen, G., Cao, H., Aafaque, M, Chen, J., Ye, C., Röhrbein, F., Conradt, J., Chen, K., Bing, Z., Liu, X., Hinz, G., Stechele, W., Knoll, A. (2018): Neuromorphic vision-based multivehicle detection and tracking for intelligent transportation systems. Journal of Advanced Transportation. doi:10.1155/2018/4815383

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

Bing, Z., Meschede, C., Röhrbein, F., Huang, K., Knoll, A.: A survey of robotics control based on learning-inspired spiking neural networks. Frontiers in Neurorobotics.doi:10.3389/fnbot.2018.00035/

 

2017

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

Chen, G., Bing, Z., Röhrbein, F., Conradt, J., Huang, K., Cheng, L., Jiang, Z., Knoll, A. . Towards brain-inspired learning with the neuromorphic snake-like robot and the neurorobotic platform. In IEEE Transactions on Cognitive and Developmental Systems. doi: 10.1109/TCDS.2017.2712712

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

Zhenshan, B., Cheng, L., Huang, K., Jiang, Z., Chen, G., Röhrbein, F., Knoll, A.. Towards Autonomous Locomotion: Slithering Gait Design of Snake-Like Robot for Target Observation and Tracking. IEEE / RSJ International Conference on Intelligent Robots and Systems (IROS-2017). doi:10.1109/IROS.2017.8206095

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

Scardapane, S., Stoffl, L., Röhrbein, F., Uncini, A. . On the Use of Deep Recurrent Neural Networks for Detecting Audio Spoofing Attacks. International Joint Conference on Neural Networks (IJCNN-2017): 3483-3490.doi:10.1109/IJCNN.2017.7966294

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

Hinz, G., Chen, G., Aafaque, M., Röhrbein, F., Conradt, J., Bing, Z., Qu, Z., Stechele, W., Knoll, A. (2017). Online Multi-Object Tracking-by-Clustering for Intelligent Transportation System with Neuromorphic Vision Sensor. German Conference on Artificial Intelligence KI-2017. doi:10.1007/978-3-319-67190-1_11

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

Walter, F., Sanden, M., Röhrbein, F., Knoll, A. . Towards a Neuromorphic Implementation of Hierarchical Temporal Memory on SpiNNaker. IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, MD, USA. doi:10.1109/ISCAS.2017.8050983

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.

2016

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

Vandesompele, A., Walter, F., Röhrbein, F. Neuro-Evolution of Spiking Neural Networks on SpiNNaker Neuromorphic Hardware. IEEE Symposium Series on Computational Intelligence. doi:10.1109/SSCI.2016.7850250

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

2015

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

Röhrbein, F., Goddard, P., Schneider, M., James, G., Guo, K.: How does image noise affect actual and predicted human gaze allocation in assessing image quality? Vision Research, 112:11 - 25, 2015. doi:10.1016/j.visres.2015.03.029

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

Walter, F., Röhrbein, F. Knoll, A.: Computation by Time. Neural Processing Letters, pages 1-22, 2015. doi:10.1007/s11063-015-9478-6

2014

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

Ostankov, A., Röhrbein, F., Waltinger, U. LinkedHealthAnswers: Towards Linked Data-driven Question Answering for the Health Care Domain. Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), Reykjavik, Iceland, 2613-2620. pdf here.

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

Röhrbein, F., Veiga, G., Natale, C.: Gearing Up and Accelerating - Crossfertilization between Academic and Industrial Robotics Research in Europe: Technology transfer experiments from the ECHORD project. Number 94 in Springer Tracts in Advanced Robotics. Springer International Publishing. doi:10.1007/978-3-319-02934-4

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.

2013

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

Krichmar, J., F. Röhrbein, F.: Value and Reward Based Learning in Neurobots. volume 7 of Frontiers in Neurorobotics. doi:10.3389/fnbot.2013.00013

2010

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.

2009

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

Röhrbein, F., Eggert, J., Körner, E.: Child-friendly divorcing: Incremental hierarchy learning in Bayesian Networks. Proceedings of International Joint Conference on Neural Networks (IJCNN), 2711-2716. doi:10.1109/IJCNN.2009.5178995

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.

2008

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.: Bayesian columnar networks for grounded cognitive systems. In B.C. Love, K. McRae, V.M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (CogSci), 1423-1428. pdf here.

2007

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., Körner, E.: Prototypical relations for cortex-inspired semantic representations. Proceedings of the 8th International Conference on Cognitive Modeling (ICCM), Ann Arbor, U.S.A. 307-312. pdf here.

2006

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.

2005

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

2004

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., Artmann, S., Hirsch, M.: IKAR/OS - Intelligent knowledge and research operating system. In: Rumpe, B., Hesse, W. (Eds): Modellierung, GI-Edition Lecture Notes in Informatics (LNI), Vol. P-45, Köllen Verlag, Bonn: 327-328. pdf here

2002

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

Röhrbein, F., Zetzsche, C.: The statistics of natural scenes and Weber’s law. In Würtz, P., Lappe, M. (Eds), 4. Workshop Dynamic Perception, Akademische Verlagsgesellschaft, Berlin: 233-238.

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.

2001

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

Schill, K., Baier, V., Röhrbein, F., Brauer, W.: A hierarchical network model for the analyses of human spatio-temporal information processing. In Rogowitz, B.E., Pappas, T.N. (Eds), Human Vision and Electronic Imaging, Proceedings SPIE. Vol. 4299: 615-621. doi:10.1117/12.429535

2000

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.

Baier, V., Schill, K., Röhrbein, F., Brauer, W.:. Processing of spatio-temporal structures: A hierarchical neural network model. In Baratoff, G., Neumann, H. (Eds), 3. Workshop Dynamic Perception, Vol. 9 of Proceedings in Artificial Intelligence, Berlin: 223- 226.

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.

Eisenkolb, A., Schill, K., Röhrbein, F., Baier, V., Musto, A., Brauer, W.: Visual processing and representation of spatio-temporal patterns. In Freksa, C., Habel, C., Wender, K. (Eds), Spatial Cognition II - Integrating Abstract Theories, Empirical Studies, Formal Methods and Practical Applications, Lecture Notes in Artificial Intelligence, vol. 1849, Springer, Berlin: 145-156. doi:10.1007/3-540-45460-8_11

1998

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.