Publications

2018

Fang, J., Rüther, N., Bellebaum, C., Wiskott, L., & Cheng, S.. (2018). The Interaction between Semantic Representation and Episodic Memory. Neural Computation, 30(2), 293–332. http://doi.org/10.1162/neco_a_01044
Akimoto, Y., Auger, A., & Glasmachers, T.. (2018). Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule (No. arxiv:1802.03209). arxiv.org.
Horn, D., Ibisch, A., & Tschentscher, M.. (2018). Automatisierte Videoanalyse. In C. Moritz & Corsten, M. (Eds.), Handbuch Qualitative Videoanalyse (pp. 1–17). Springer VS Verlag.
Walther, T., & Würtz, R. P.. (2018). Unsupervised Acquisition of Human Body Models. Cognitive Systems Research, 47, 68–84. http://doi.org/10.1016/j.cogsys.2017.08.001
Werning, M., & Cheng, S.. (2018). Doing without metarepresentation: Scenario construction explains the epistemic generativity and privileged status of episodic memory. Behavioral and Brain Sciences, 41. http://doi.org/10.1017/s0140525x17001534
2017

Walther, T., & Würtz, R. P.. (2017). Unsupervised Acquisition of Human Body Models using Principles of Organic Computing. ArXiv e-prints. Retrieved from http://arxiv.org/abs/1704.03724
Knips, G., Zibner, S. K. U., Reimann, H., & Schöner, G.. (2017). A Neural Dynamic Architecture for Reaching and Grasping Integrates Perception and Movement Generation and Enables On-Line Updating. Frontiers in Neurorobotics, 11(March), 9:1–14. http://doi.org/10.3389/fnbot.2017.00009
Melchior, J., Wang, N., & Wiskott, L.. (2017). Gaussian-binary restricted Boltzmann machines for modeling natural image statistics. PLOS ONE, 12(2), 1–24. http://doi.org/10.1371/journal.pone.0171015
Cheng, S. (2017). Gedächtnisverbesserung: Möglichkeiten und kritische Betrachtung. In F. Hüttemann & Liggieri, K. (Eds.), Die Grenze . Diskurse des Transhumanismus. (p. invited contribution). Bielefeld: transcript Verlag.
Cheng, S. (2017). Consolidation of Episodic Memory: An Epiphenomenon of Semantic Learning. In N. Axmacher & Rasch, B. (Eds.), Cognitive Neuroscience of Memory Consolidation (pp. 57–72). Cham, Switzerland: Springer International Publishing. http://doi.org/10.1007/978-3-319-45066-7_4
Draht, F., Zhang, S., Rayan, A., Schönfeld, F., Wiskott, L., & Manahan-Vaughan, D. (2017). Experience-Dependency of Reliance on Local Visual and Idiothetic Cues for Spatial Representations Created in the Absence of Distal Information. Frontiers in Behavioral Neuroscience, 11(92). http://doi.org/10.3389/fnbeh.2017.00092
Escalante-B., A. N. (2017). Extensions of Hierarchical Slow Feature Analysis for Efficient Classification and Regression on High-Dimensional Data. Doctoral thesis, Ruhr University Bochum, Faculty of Electrical Engineering and Information Technology.
Glasmachers, T. (2017). A Fast Incremental BSP Tree Archive for Non-dominated Points. In Evolutionary Multi-Criterion Optimization (EMO). Springer.
Glasmachers, T. (2017). Limits of End-to-End Learning. In Proceedings of the 9th Asian Conference on Machine Learning (ACML).
Glasmachers, T. (2017). Global Convergence of the (1+1) Evolution Strategy (No. arxiv:1706.02887). arxiv.org.
Grieben, R., & Würtz, R. P.. (2017). Cue Integration by Similarity Rank List Coding — Application to Invariant Object Recognition. In Proceedings of IEEE International Workshops on Foundations and Applications of Self* Systems (pp. 132–137). http://doi.org/10.1109/FAS*W.2017.38
Irmer, T., Glasmachers, T., & Maji, S. (2017). Texture attribute synthesis and transfer using feed-forward CNNs. In Winter Conference on Applications of Computer Vision (WACV). IEEE.
Jancke, D. (2017). Catching the voltage gradient—asymmetric boost of cortical spread generates motion signals across visual cortex: a brief review with special thanks to Amiram Grinvald. Neurophoton., 4(3), 031206. http://doi.org/10.1117/1.NPh.4.3.031206
Kompella, V. R., & Wiskott, L.. (2017). Intrinsically Motivated Acquisition of Modular Slow Features for Humanoids in Continuous and Non-Stationary Environments. arXiv preprint arXiv:1701.04663.
Krause, O., Glasmachers, T., & Igel, C. (2017). Qualitative and Quantitative Assessment of Step Size Adaptation Rules. In Conference on Foundations of Genetic Algorithms (FOGA). ACM.
Lins, J., & Schöner, G.. (2017). Mouse Tracking Shows Attraction to Alternative Targets While Grounding Spatial Relations. In Proceedings of the 39th Annual Conference of the Cognitive Science Society (to appear). Austin, TX: Cognitive Science Society.
Lomp, O., Faubel, C., & Schöner, G.. (2017). A Neural-Dynamic Architecture for Concurrent Estimation of Object Pose and Identity. Frontiers in Neurorobotics, 11(April), 23. http://doi.org/10.3389/fnbot.2017.00023
Neher, T., Azizi, A. H., & Cheng, S.. (2017). From grid cells to place cells with realistic field sizes. PLoS ONE, 12(7), e0181618. http://doi.org/10.1371/JOURNAL.PONE.0181618
Reimann, H., & Schöner, G.. (2017). A multi-joint model of quiet , upright stance accounts for the “uncontrolled manifold”-structure of joint variance. Biological Cybernetics, in press.
Richter, M., Lins, J., & Schöner, G.. (2017). A neural dynamic model generates descriptions of object-oriented actions. Topics in Cognitive Science, 9(1), 35–47. http://doi.org/10.1111/tops.12240
Schöner, G., Tekülve, J., & Zibner, S. (2017). Reaching for objects : a neural process account in a developmental perspective. In D. Corbetta & Santello, M. (Eds.), The selection and production of goal-directed behaviors: Neural correlates, development, learning, and modeling of reach-to-grasp movements. Taylor & Francis.
Strub, C., Schöner, G., Wörgötter, F., & Sandamirskaya, Y.. (2017). Dynamic Neural Fields with Intrinsic Plasticity. Frontiers in Computational Neuroscience, 11(August), 74. http://doi.org/10.3389/FNCOM.2017.00074
Tschentscher, M., Pruß, B., & Horn, D.. (2017). A simulated car-park environment for the evaluation of video-based on-site parking guidance systems. In Proceedings of the IEEE Intelligent Vehicles Symposium (IV) (pp. 1571–1576).
Weghenkel, B., Fischer, A., & Wiskott, L.. (2017). Graph-based predictable feature analysis. Machine Learning, 1–22. http://doi.org/10.1007/s10994-017-5632-x
Werning, M., & Cheng, S.. (2017). Taxonomy and Unity of Memory. In S. Bernecker & Michaelian, K. (Eds.), The Routledge Handbook of Philosophy of Memory (p. forthcoming). London: Routledge.
Werning, M., & Cheng, S.. (2017). No need for meta-representation: How scenario construction explains the epistemic generativity and privileged epistemic status of episodic memory. Behavioral and Brain Sciences, in press.
2016

Babichev, A., Cheng, S., & Dabaghian, Y. A. (2016). Topological Schemas of Cognitive Maps and Spatial Learning. Frontiers in Computational Neuroscience, 10, 18. http://doi.org/10.3389/fncom.2016.00018
Cheng, S., & Werning, M. (2016). What is episodic memory if it is a natural kind? Synthese, 193(5), 1345–1385. http://doi.org/10.1007/s11229-014-0628-6
Cheng, S., Werning, M., & Suddendorf, T. (2016). Dissociating memory traces and scenario construction in mental time travel. Neuroscience & Biobehavioral Reviews, 60, 82–89. http://doi.org/10.1016/j.neubiorev.2015.11.011
Demircioğlu, A., Horn, D., Glasmachers, T., Bischl, B., & Weihs, C. (2016). Fast model selection by limiting SVM training times (No. arxiv:1302.1602.03368v1). arxiv.org.
Doğan, Ü., Glasmachers, T., & Igel, C. (2016). A Unified View on Multi-class Support Vector Classification. Journal of Machine Learning Research, 17(45), 1–32.
Escalante-B., A. N., & Wiskott, L.. (2016). Theoretical analysis of the optimal free responses of graph-based SFA for the design of training graphs. Journal of Machine Learning Research, 17(157), 1–36. Retrieved from http://jmlr.org/papers/v17/15-311.html
Escalante-B., A. N., & Wiskott, L.. (2016, January). Improved graph-based SFA: Information preservation complements the slowness principle. e-print arXiv:1601.03945. Retrieved from http://arxiv.org/abs/1601.03945
Fahmy, G., & Wurtz, R.. (2016). Phase based forgery detection of JPEG anti forensics. In Signal Processing and Information Technology (ISSPIT), 2016 IEEE International Symposium on (pp. 144–149). IEEE.
Glasmachers, T. (2016). Finite Sum Acceleration vs. Adaptive Learning Rates for the Training of Kernel Machines on a Budget. In NIPS workshop on Optimization for Machine Learning.
Glasmachers, T. (2016). Small Stochastic Average Gradient Steps. In NIPS workshop on Optimizing the Optimizers.
Hock, H. S., & Schöner, G.. (2016). Nonlinear dynamics in the perceptual grouping of connected surfaces. Vision Research, 126, 80–96. http://doi.org/10.1016/j.visres.2015.06.006
Horn, D., Demirciğlu, A., Bischl, B., Glasmachers, T., & Weihs, C. (2016). A Comparative Study on Large Scale Kernelized Support Vector Machines. Advances in Data Analysis and Classification (ADAC), 1–17.
Krause, O., Glasmachers, T., Hansen, N., & Igel, C. (2016). Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
Krause, O., Glasmachers, T., & Igel, C. (2016). Multi-objective Optimization with Unbounded Solution Sets. In NIPS workshop on Bayesian Optimization.
Lomp, O., Richter, M., Zibner, S. K. U., & Schöner, G.. (2016). Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar. Frontiers in Neurorobotics, 10(November), 14. http://doi.org/10.3389/fnbot.2016.00014
Loshchilov, I., & Glasmachers, T.. (2016). Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES (HMO-CMA-ES). In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
Melchior, J., Fischer, A., & Wiskott, L.. (2016). How to Center Deep Boltzmann Machines. Journal of Machine Learning Research, 17(99), 1–61. Retrieved from http://jmlr.org/papers/v17/14-237.html
Michael, M., Feist, C., Schuller, F., & Tschentscher, M.. (2016). Fast Change Detection for Camera-based Surveillance Systems. In Proceedings of the IEEE International Conference on Intelligent Transportation Systems (Vol. 19, pp. 1–8).
Park, E., Reimann, H., & Schöner, G.. (2016). Coordination of muscle torques stabilizes upright standing posture: an UCM analysis. Experimental Brain Research, 234(6), 1757–1767. http://doi.org/10.1007/s00221-016-4576-x
Raket, L. L., Grimme, B., Schöner, G., Igel, C., & Markussen, B. (2016). Separating Timing, Movement Conditions and Individual Differences in the Analysis of Human Movement. PLoS Computational Biology, 12(9), 1–27. http://doi.org/10.1371/journal.pcbi.1005092
Rekauzke, S., Nortmann, N., Staadt, R., Hock, H. S., Schöner, G., & Jancke, D.. (2016). Temporal asymmetry in dark-bright processing initiates propagating activity across primary visual cortex. J Neurosci, 36(6), 1902–1913.
Schönfeld, F. (2016). A computational model of spatial encoding in the hippocampus. Doctoral thesis, Ruhr-Universität Bochum.
Spoida, K., Eickelbeck, D., Karapinar, R., Eckhardt, T., Mark, M. D., Jancke, D., et al. (2016). Melanopsin variants as intrinsic optogenetic On and Off switches for transient versus sustained activation of G protein pathways. Curr Biol., 26(9), 1206–1212.
Tekülve, J., Zibner, S. K. U., & Schöner, G.. (2016). A neural process model of learning to sequentially organize and activate pre-reaches. In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2016 Joint IEEE International Conferences on.
Tomforde, S., Rudolph, S., Bellman, K., & Würtz, R. P.. (2016). "Self-Improving System Integration" – Preface for the SISSY′16 Workshop. In Proc. ICAC (p. 275). http://doi.org/10.1109/ICAC.2016.74
Tomforde, S., Rudolph, S., Bellman, K., & Würtz, R. P.. (2016). An Organic Computing Perspective on Self-Improving System Interweaving at Runtime. In Proc. ICAC (pp. 276–284). http://doi.org/10.1109/ICAC.2016.15
Weihs, C., & Glasmachers, T.. (2016). Supervised Classification. In C. Weihs, Jannach, D., Vatolkin, I., & Rudolph, G. (Eds.), Music Data Analysis: Foundations and Applications.
2015

Fahmy, G., Alqallaf, A., & Wurtz, R.. (2015). Phase based detection of JPEG counter forensics. In 2015 IEEE International Conference on Electronics, Circuits, and Systems (ICECS) (pp. 37–40). http://doi.org/10.1109/ICECS.2015.7440243
Neher, T., Cheng, S., & Wiskott, L. (2015). Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input Statistics. PLOS Computational Biology, 11(5), 1–25. http://doi.org/10.1371/journal.pcbi.1004250
Bayati, M., Valizadeh, A., Abbassian, A., & Cheng, S.. (2015). Self-organization of synchronous activity propagation in neuronal networks driven by local excitation. Frontiers in Computational Neuroscience, 9, 69. http://doi.org/10.3389/fncom.2015.00069
Bodenstein, C., Tremer, M., Overhoff, J., & Würtz, R. P.. (2015). A smartphone-controlled autonomous robot. In Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery, Zhangjiajie, China, Aug. 15-17 (pp. 2360–2367). http://doi.org/10.1109/FSKD.2015.7382314
Dinse, H. R., & Tegenthoff, M. (2015). Evoking plasticity through sensory stimulation: Implications for learning and rehabilitation . e-Neuroforum, 1(1), .
Escalante-B., A. N., & Wiskott, L.. (2015). Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs. e-print arXiv:1509.08329 (Accepted in Journal of Machine Learning Research). Retrieved from http://arxiv.org/abs/1509.08329
Günther, M., Böhringer, S., Wieczorek, D., & Würtz, R. P.. (2015). Reconstruction of Images from Gabor Graphs with Applications in Facial Image Processing. International Journal of Wavelets, Multiresolution and Information Processing, 13(4), 1550019-1–25. http://doi.org/10.1142/S0219691315500198
Haag, L. M., Heba, S., Lenz, M., Glaubitz, B., Höffken, O., Kalisch, T., et al. (2015). Resting BOLD fluctuations in the primary somatosensory cortex correlate with tactile acuity. Cortex, 64, 20–28.
Hansen, E., Grimme, B., Reimann, H., & Schöner, G.. (2015). Carry-over coarticulation in joint angles. Experimental Brain Research, 233(9), 2555–2569. http://doi.org/10.1007/s00221-015-4327-4
Horn, D., & Brüggenthies, M. (2015). Video-based Parking Space Detection: Localisation of Vehicles and Development of an Infrastructure for a Routeing System. In Proceedings of the Forum Bauinformatik (pp. 175–182).
Houben, S., Neuhausen, M., Michael, M., Kesten, R., Mickler, F., & Schuller, F. (2015). Park marking-based vehicle self-localization with a fisheye topview system. Journal of Real-Time Image Processing, 1–16.
Ibisch, A., Houben, S., Michael, M., Kesten, R., & Schuller, F. (2015). Arbitrary object localization and tracking via multiple-camera surveillance system embedded in a parking garage. In SPIE/IS&T Electronic Imaging. International Society for Optics and Photonics.
Kompella, V. R., Stollenga, M., Luciw, M., & Schmidhuber, J. (2015). Continual curiosity-driven skill acquisition from high-dimensional video inputs for humanoid robots. Artificial Intelligence.
Kosilek, R. P., Frohner, R., Würtz, R. P., Berr, C. M., Schopohl, J., Reincke, M., & Schneider, H. J. (2015). Automatic face classification in Cushing′s syndrome and acromegaly: Review, current results and future perspectives. European Journal of Endocrinology, 173(4), M39–M44. http://doi.org/10.1530/EJE-15-0429
Krause, O., & Glasmachers, T.. (2015). A CMA-ES with Multiplicative Covariance Matrix Updates. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
Lobato, D., Sandamirskaya, Y., Richter, M., & Schöner, G.. (2015). Parsing of action sequences: A neural dynamics approach. Paladyn, Journal of Behavioral Robotics, 6(1), 119–135. http://doi.org/10.1515/pjbr-2015-0008
Mattos, D., Schöner, G., Zatsiorsky, V. M., & Latash, M. L. (2015). Task-specific stability of abundant systems: Structure of variance and motor equivalence. Neuroscience, 310, 600–615. http://doi.org/10.1016/j.neuroscience.2015.09.071
Mattos, D., Schöner, G., Zatsiorsky, V. M., & Latash, M. L. (2015). Motor equivalence during multi-finger accurate force production. Experimental Brain Research, 233, 487–502. http://doi.org/10.1007/s00221-014-4128-1
Michael, M., & Schlipsing, M.. (2015). Extending Traffic Light Recognition: Efficient Classification of Phase and Pictogram. In Proceedings of the IEEE International Joint Conference on Neural Networks.
Neher, T., Cheng, S., & Wiskott, L.. (2015). Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input Statistics. PLoS Computational Biology, 11(5), e1004250. http://doi.org/10.1371/journal.pcbi.1004250
Neher, T., Cheng, S., & Wiskott, L.. (2015). Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input Statistics. PLoS Comput Biol, 11, e1004250. http://doi.org/10.1371/journal.pcbi.1004250
Nortmann, N., Rekauzke, S., Azimi, Z., Onat, S., König, P., & Jancke, D.. (2015). Visual homeostatic processing in V1: When probability meets dynamics . Frontiers in Systems Neuroscience , 9.
Nortmann, N., Rekauzke, S., Onat, S., König, P., & Jancke, D.. (2015). Primary Visual Cortex Represents the Difference Between Past and Present. Cerebral Cortex, 25(6), 1427–1440.
Reimann, H., Lins, J., & Schöner, G.. (2015). The Dynamics of Neural Activation Variables. Paladyn, Journal of Behavioral Robotics, 6(1), 57–70.
Richthofer, S., & Wiskott, L.. (2015). Predictable Feature Analysis. In Workshop New Challenges in Neural Computation 2015 (NC2) (pp. 68–75). Retrieved from https://www.techfak.uni-bielefeld.de/ fschleif/mlr/mlr_03_2015.pdf
Richthofer, S., & Wiskott, L.. (2015). Predictable Feature Analysis. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (pp. 190–196). http://doi.org/10.1109/ICMLA.2015.158
Ritter, P., Born, J., Brecht, M., Dinse, H. R., Heinemann, U., Pleger, B., et al. (2015). State-dependencies of learning across brain scales. Frontiers in Computational Neuroscience, 9.
Sandamirskaya, Y., & Storck, T. (2015). Artificial Neural Networks — Methods and Applications in Bio-/Neuroinformatics. In P. Koprinkova-Hristova, Mladenov, V., & Kasabov, N. K. (Eds.) (Vol. 4). Springer.
Schoenfeld, F., & Wiskott, L.. (2015). Modeling place field activity with hierarchical slow feature analysis. Frontiers in Computational Neuroscience, 9(51). http://doi.org/10.3389/fncom.2015.00051
Schönfeld, F., & Wiskott, L.. (2015). Modeling place field activity with hierarchical slow feature analysis. frontiers in Computational Neuroscience, 9(51). http://doi.org/10.3389/fncom.2015.00051
Tschentscher, M., Koch, C., König, M., Salmen, J., & Schlipsing, M.. (2015). Scalable real-time parking lot classification: An evaluation of image features and supervised learning algorithms. In Proceedings of the IEEE International Joint Conference on Neural Networks.
Zibner, S. K. U., Tekülve, J., & Schöner, G.. (2015). The Neural Dynamics of Goal-Directed Arm Movements: A Developmental Perspective. In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2015 Joint IEEE International Conferences on (pp. 154–161).
Zibner, S. K. U., Tekülve, J., & Schöner, G.. (2015). The Sequential Organization of Movement is Critical to the Development of Reaching: A Neural Dynamics Account. In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2015 Joint IEEE International Conferences on (pp. 39–46).
2014

Demic, S., & Cheng, S.. (2014). Modeling the Dynamics of Disease States in Depression. PLOS ONE, 9(10), 1–14. http://doi.org/10.1371/journal.pone.0110358
Muret, D., Dinse, H. R., Macchione, S., Urquizar, C., Farne, A., & Reilly, K. T. (2014). Touch improvement at the hand transfers to the face. Curr. Biol., 24(16), R736–737.
Azizi, A. H., Schieferstein, N., & Cheng, S.. (2014). The transformation from grid cells to place cells is robust to noise in the grid pattern. Hippocampus, 24(8), 912–919. http://doi.org/10.1002/hipo.22306
Balliu, B., Würtz, R. P., Horsthemke, B., Wieczorek, D., & Böhringer, S. (2014). Classification and visualization based on derived image features: application to genetic syndromes. PLOS One, 9(11), e109033. http://doi.org/10.1371/journal.pone.0109033
Bell, C., Storck, T., & Sandamirskaya, Y.. (2014). Learning to Look: a Dynamic Neural Fields Architecture for Gaze Shift Generation. In International Conference for Artificial Neural Networks, ICANN. Hamburg, Germany.
Bellman, K., Tomforde, S., & Würtz, R. P.. (2014). "Self-Improving System Integration" – Preface for the SISSY14 Workshop. In Proc. SASO, London (p. 122). IEEE.
Bellman, K., Tomforde, S., & Würtz, R. P.. (2014). Interwoven Systems: Self-improving Systems Integration. In Proc. SASO, London (pp. 123–127). IEEE.
Bruns, P., Camargo, C. J., Campanella, H., Esteve, J., Dinse, H. R., & Röder, B. (2014). Tactile Acuity Charts: A Reliable Measure of Spatial Acuity. PloS one, 9(2), e87384.
Chavane, F., Sharon, D., Jancke, D., Marre, O., Fregnac, Y., & Grinvald, A. (2014). Optogenetic Assessment of Horizontal Interactions in Primary Visual Cortex (pg 4976, 2014). J Neurosci, 34(26), 8930–8930.
Dähne, S., Wilbert, N., & Wiskott, L.. (2014). Slow Feature Analysis on Retinal Waves Leads to V1 Complex Cells. PLoS Comput Biol, 10(5), e1003564. http://doi.org/10.1371/journal.pcbi.1003564
Danafar, S., Rancoita, P. M. V., Glasmachers, T., Whittingstall, K., & Schmidhuber, J. (2014). Testing Hypotheses by Regularized Maximum Mean Discrepancy. International Journal of Computer and Information Technology (IJCIT), 3(2).
Glasmachers, T. (2014). Handling Sharp Ridges with Local Supremum Transformations. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
Glasmachers, T. (2014). Optimized Approximation Sets for Low-dimensional Benchmark Pareto Fronts. In Parallel Problem Solving from Nature (PPSN). Springer.
Glasmachers, T., Naujoks, B., & Rudolph, G. (2014). Start Small, Grow Big - Saving Multiobjective Function Evaluations. In Parallel Problem Solving from Nature (PPSN). Springer.
Hernandes, A. C., Guerrero, H. B., Becker, M., Jokeit, J. -S., & Schöner, G.. (2014). A comparison between reactive potential fields and Attractor Dynamics. Circuits and Systems (CWCAS), 2014 IEEE 5th Colombian Workshop on.
Houben, S. (2014). Towards the intrinsic self-calibration of a vehicle-mounted omni-directional radially symmetric camera. In Proceedings of the IEEE Intelligent Vehicles Symposium Proceedings (pp. 878–883).
Ibisch, A., Houben, S., Schlipsing, M., Kesten, R., Reimche, P., Schuller, F., & Altinger, H. (2014). Towards highly automated driving in a parking garage: General object localization and tracking using an environment-embedded camera system. In Proceedings of the IEEE Intelligent Vehicles Symposium Proceedings (pp. 426–431).
Kattenstroth, J. C., Kalisch, T., Tegenthoff, M., & Dinse, H. R.. (2014). Tanz im Alter: Fitness für Gehirn, Geist und Körper . In C. Behrens & Rosenberg, C. (Eds.), TanzZeit – LebensZeit, Tanzforschung 2014 (pp. 115–135). Leipzig: Henschel Verlag .
Knips, G., Zibner, S. K. U., Reimann, H., Popova, I., & Schöner, G.. (2014). A neural dynamics architecture for grasping that integrates perception and movement generation and enables on-line updating. In International Conference on Intelligent Robots and Systems (IROS) (pp. 646–653).
Knips, G., Zibner, S. K. U., Reimann, H., Popova, I., & Schöner, G.. (2014). Reaching and grasping novel objects: Using neural dynamics to integrate and organize scene and object perception with movement generation. In International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EPIROB) (pp. 416–423).
Kompella, V. R. (2014). Slow Feature Analysis for Curiosity-Driven Agents, 2014 IEEE WCCI Tutorial.
Kompella, V. R. (2014). Slowness Learning for Curiosity-Driven Agents. Doctoral thesis, Università della svizzera italiana (USI).
Kompella, V. R., Kazerounian, S., & Schmidhuber, J. (2014). An Anti-hebbian Learning Rule to Represent Drive Motivations for Reinforcement Learning. In From Animals to Animats 13 (pp. 176–187). Springer International Publishing.
Kompella, V. R., Stollenga, M. F., Luciw, M. D., & Schmidhuber, J. (2014). Explore to See, Learn to Perceive, Get the Actions for Free: SKILLABILITY. In Proceedings of IEEE Joint Conference of Neural Networks (IJCNN).
Kozyrev, V., Eysel, U. T., & Jancke, D.. (2014). Voltage-sensitive dye imaging of transcranial magnetic stimulation-induced intracortical dynamics. Proceedings of the National Academy of Sciences, 111(37), 13553–13558.
Krause, T. U., Schrör, P. Y., & Würtz, R. P.. (2014). Spiking network simulations. In B. Hammer, Martinetz, T., & Villmann, T. (Eds.), Proceedings of New Challenges in Neural Computation, Münster (pp. 14–15).
Ladda, A. M., Pfannmoeller, J. P., Kalisch, T., Roschka, S., Platz, T., Dinse, H. R., & Lotze, M. (2014). Effects of combining 2 weeks of passive sensory stimulation with active hand motor training in healthy adults. PloS one, 9(1), e84402.
Lessmann, M., & Würtz, R. P.. (2014). Learning of invariant object recognition from temporal correlation in a hierarchical network. Neural Networks, 54, 70–84. http://doi.org/10.1016/j.neunet.2014.02.011
Lessmann, M., & Würtz, R. P.. (2014). Online Learning of Invariant Object Recognition in a Hierarchical Neural Network. In S. Wermter, Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., et al. (Eds.), Proc. ICANN (pp. 427–434). Springer.
Lins, J., & Schöner, G.. (2014). Neural Fields. In S. Coombes, beim Graben, P., Potthast, R., & , J. W. (Eds.) (pp. 319–339). Springer Berlin Heidelberg.
Lissek, S., Vallana, G. S., Schlaffke, L., Lenz, M., Dinse, H. R., & Tegenthoff, M. (2014). Opposing effects of dopamine antagonism in a motor sequence task—tiapride increases cortical excitability and impairs motor learning. Frontiers in behavioral neuroscience, 8.
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