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Dominik Endres

Prof. Dr.

Philipps-University Marburg FB 04 Psychology
Gutenbergstr. 18
35036 Marburg, Germany

+49 (0)6421 28 238 18 send e-mail visit website

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Motivation: how do our brains represent the knowledge that both dogs and birds are animals, or that a car is a special type of vehicle with four wheels and an engine? More generally speaking, how do entities come to have meaning? Answering this fundamental cognitive neuroscience question would have several important applications. For example, it might enable us to design assistive technology for patients with certain degenerative diseases, e.g. semantic dementia (visual associative agnosia, Alzheimer's). On the more technical side, if we understood how the brain represents relational information on different levels of the (visual) cortical hierarchy, we would be able to bride the gap between mostly sensory-driven, bottom-up approaches in computer vision and machine learning on the one hand, and semantic-level, logical AI approaches (such as Markov logic or Bayesian logic programs) on the other hand.

project-related publications
Meibodi, N., Schubö, A., & Endres, D. M. (2024). Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model.Computational Brain & Behavior, 1-18. find paper
Meibodi, N., Schubö, A., Endres, D. M. (2022). Sensorimotor processes are not a source of much noise: Sensory-motor and decision components of reaction times. Proceedings of the Annual Meeting of the Cognitive Science Society, 44. find paper, CODE
former project-related publications
Chiovetto, E., Curio, C., Endres, D., & Giese, M. (2018). Perceptual integration of kinematic components in the recognition of emotional facial expressions. Journal of vision, 18(4), 13-13. find paper
Clever, D., Harant, M., Koch, K. H., Mombaur, K. and Endres, D. (2016). A novel approach for the generation of complex humanoid walking sequences based on a combination of optimal control and learning of movement primitives. Robotics and Autonomous Systems, Volume 83, 287–298. find paper
Clever, D., Harant, M., Mombaur, K., Naveau, M., Stasse, O. and Endres, D. (2017). COCoMoPL: A Novel Approach for Humanoid Walking Generation Combining Optimal Control, Movement Primitives and Learning and its transfer to the real robot HRP-2. IEEE Robotics and Automation Letters ,2(2):977 – 984. find paper
Junker, M., Endres, D., Sun, Z. P., Dicke, P. W., Giese, M., & Thier, P. (2018). Learning from the past: A reverberation of past errors in the cerebellar climbing fiber signal. PLoS biology, 16(8), e2004344. find paper
Khoozani, P. A., Schrater, P. R., Endres, D., Fiehler, K., & Blohm, G. (2019). Models of allocentric coding for reaching in naturalistic visual scenes. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, 4 pages.
Knopp, B., Velychko, D., Dreibrodt, J., & Endres, D. (2019). Predicting Perceived Naturalness of Human Animations Based on Generative Movement Primitive Models. ACM Transactions on Applied Perception (TAP), 16(3), 1-18. find paper DOI
Knopp, B., Velychko, D., Dreibrodt, J., Schütz, A. C., & Endres, D. (2020). Evaluating perceptual predictions based on movement primitive models in VR- and online-experiments. In ACM 32 Symposium on Applied Perception 2020, SAP ·20, New York, NY, USA. Association for Computing Machinery. find paper DOI
Meibodi, N., Abbasi, H., Schubö, A., and Endres, D. (2021a). A model of selection history in visual attention. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 43, No. 43). find paper, DATA
Meibodi, N., Abbasi, H., Schubö, A., and Endres, D. (2021b). Distracted by previous reward: Integrating selection history, current task demands and saliency in a computational model. find preprint
Mukovskiy, A., Taubert, N., Endres, D., Vassallo, C., Naveau, M., Stasse, O., Souères, P. and Giese, M. A. (2017). Modeling of coordinated human body motion by learning of structured dynamic representations. In J.-P. Laumond, N. Mansard, and J.-B. Lasserre, editors, Geometric and Numerical Foundations of Movements, volume 117 of STAR Series, pages 1–26. Springer. find paper
Quaglio, P., Yegenoglu, A., Torre, E., Endres, D. M., & Grün, S. (2017). Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE. Frontiers in Computational Neuroscience, 11, 41. find paper
Schubert, M., & Endres, D. (2018). Empirically Evaluating the Similarity Model of Geist, Lengnink and Wille. In International Conference on Conceptual Structures (pp. 88-95). Springer, Cham. find paper, DATA
Serr, A., Schubert, M., & Endres, D. (2019, July). Mathematical Similarity Models: Do We Need Incomparability to Be Precise? In International Conference on Conceptual Structures (pp. 257-261). Springer, Cham. find paper
Velychko, D., Endres , D., Taubert, N., and Giese, M. A. (2014). Coupling Gaussian process dynamical models with product-of-experts kernels. In Proceeding of the 24th International Conference on Artificial Neural Networks, LNCS 8681, pages 603–610. Springer. find paper
Velychko, D., Knopp, B. and Endres D. (2017). The coupled variational Gaussian process dynamical model. In Proceedings of the 27th International Conference on Artificial Neural Networks,pages 1–9. DOI find paper
Velychko, D., Knopp, B. and Endres, D. (2016). The variational coupled Gaussian process dynamical model (Abstract). NIPS Workshop on Neurorobotics. DOI find paper
Velychko, D., Knopp, B., & Endres, D (2018). Making the Coupled Gaussian Process Dynamical Model Modular and Scalable with Variational Approximations. Entropy, 20(10), 724. find paper DOI
Projektbeteiligung