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2019

  • Emerging category representation in the visual forebrain hierarchy of pigeons (Columba livia)
    Azizi, A. H., Pusch, R., Koenen, C., Klatt, S., Bröcker, F., Thiele, S., et al.
    Behavioural Brain Research, 356, 423–434

2018

  • Subtraction and division of visual cortical population responses by the serotonergic system
    Azimi, Z., Spoida, K., Barzan, R., Wollenweber, P., Mark, M. D., Herlitze, S., & Jancke, D.
    Preprint at bioRxiv
  • Storage fidelity for sequence memory in the hippocampal circuit
    Bayati, M., Neher, T., Melchior, J., Diba, K., Wiskott, L., & Cheng, S.
    PLOS ONE, 13(10), e0204685
  • The reduction of adult neurogenesis in depression impairs the retrieval of new as well as remote episodic memory
    Fang, J., Demic, S., & Cheng, S.
    PLOS ONE, 13(6), e0198406
  • TMS-induced neuronal plasticity enables targeted remodeling of visual cortical maps
    Kozyrev, V., Staadt, R., Eysel, U. T., & Jancke, D.
    Proceedings of the National Academy of Sciences, 115(25), 6476–6481
  • The Interaction between Semantic Representation and Episodic Memory
    Fang, J., Rüther, N., Bellebaum, C., Wiskott, L., & Cheng, S.
    Neural Computation, 30(2), 293–332
  • Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule
    Akimoto, Y., Auger, A., & Glasmachers, T.
    In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) ACM
  • Fine-Grained Vehicle Representations for Autonomous Driving
    Barowski, T., Szczot, M., & Houben, S.
    In Accepted for IEEE International Conference on Intelligent Transportation Systems (ITSC)
  • Self-improving System Integration – Status and Challenges After Five Years of SISSY
    Bellman, K., Botev, J., Diaconescu, A., Esterle, L., Gruhl, C., Landauer, C., et al.
    In Proceedings of IEEE International Workshops on Foundations and Applications of Self* Systems (pp. 160–167)
  • Challenges of Convex Quadratic Bi-objective Benchmark Problems
    Glasmachers, T.
    arXiv.org
  • Speeding Up Budgeted Dual SVM Training with Precomputed GSS
    Glasmachers, T., & Qaadan, S.
    (M. M. -y-G. Ruben Vera-Rodriguez Sergio Velastin & Morales, A., Eds.), The 23rd Iberoamerican Congress on Pattern Recognition
  • Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search
    Glasmachers, T., & Qaadan, S.
    In G. Nicosia, Pardalos, P., Giuffrida, G., Umeton, R., & Sciacca, V. (Eds.), The 4th International Conference on machine Learning, Optimization and Data science - LOD 2018
  • Sequences of discrete attentional shifts emerge from a neural dynamic architecture for conjunctive visual search that operates in continuous time
    Grieben, R., Tekülve, J., Zibner, S. K. U., Schneegans, S., & Schöner, G.
    In T. T. Rogers, Rau, M., Zhu, X., & Kalish, C. W. (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (pp. 429–434)
  • Anticipatory coarticulation in non-speeded arm movements can be motor-equivalent, carry-over coarticulation always is
    Hansen, E., Grimme, B., Reimann, H., & Schöner, G.
    Experimental Brain Research
  • Evaluation of Synthetic Video Data in Machine Learning Approaches for Parking Space Classification
    Horn, D., & Houben, S.
    In Proceedings of the IEEE Intelligent Vehicles Symposium (IV) (pp. 2157–2162)
  • Erste Ansätze zur automatischen Erkennung von Gruppenverhalten mithilfe des Computersehens
    Horn, D., Houben, S., & Schöner, G.
    In J. Reichertz & Keysers, V. (Eds.), Emotion. Eskalation. Gewalt. (pp. 130–147) Beltz Juventa
  • Automatisierte Videoanalyse
    Horn, D., Ibisch, A., & Tschentscher, M.
    In C. Moritz & Corsten, M. (Eds.), Handbuch Qualitative Videoanalyse (pp. 445–456) Springer VS Verlag
  • A Neural Dynamic Architecture That Autonomously Builds Mental Models
    Kounatidou, P., Richter, M., & Schöner, G.
    In T. T. Rogers, Rau, M., Zhu, X., & Kalish, C. W. (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (pp. 643–648)
  • Large Scale Black-box Optimization by Limited-Memory Matrix Adaptation
    Loshchilov, I., Glasmachers, T., & Beyer, H. -G.
    IEEE Transactions on Evolutionary Computation, 99
  • Challenges in High-dimensional Controller Design with Evolution Strategies
    Müller, N., & Glasmachers, T.
    In Parallel Problem Solving from Nature (PPSN XVI) Springer
  • Organic Face Graph Classification
    Nilkens, A.
    In S. Tomforde & Sick, B. (Eds.), Organic Computing: Doctoral Dissertation Colloquium 2017 (Vol. 11, pp. 79–92) kassel university press GmbH
  • Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training
    Qaadan, S., & Glasmachers, T.
    arXiv.org
  • Adaptive Multi-Merge Budget Maintenance for Stochastic Coordinate Ascent SVM Training
    Qaadan, S., & Glasmachers, T.
    Artificial Intelligence International Conference – A2IC 2018
  • Dual SVM Training on a Budget
    Qaadan, S., Schüler, M., & Glasmachers, T.
    arXiv.org
  • Universal Spike Classifier
    Saif-ur-Rehman, M., Lienkämper, R., Parpaley, Y., Wellmer, J., Liu, C., Lee, B., et al.
    arXiv.org
  • Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
    Schüler, M., Hlynsson, H. D., & Wiskott, L.
    arXiv.org
  • Unsupervised Acquisition of Human Body Models
    Walther, T., & Würtz, R. P.
    Cognitive Systems Research, 47, 68–84
  • Slowness as a Proxy for Temporal Predictability: An Empirical Comparison
    Weghenkel, B., & Wiskott, L.
    Neural Computation, 30(5), 1151–1179
  • Doing without metarepresentation: Scenario construction explains the epistemic generativity and privileged status of episodic memory
    Werning, M., & Cheng, S.
    Behavioral and Brain Sciences, 41
  • Interactive Learning Without Ground Truth
    Würtz, R. P., Tomforde, S., Calma, A., Kottke, D., & Sick, B.
    In S. Tomforde & Sick, B. (Eds.), Organic Computing: Doctoral Dissertation Colloquium 2017 (Vol. 11, p. II.1–II.4) kassel university press GmbH

2017

  • Unsupervised Acquisition of Human Body Models using Principles of Organic Computing
    Walther, T., & Würtz, R. P.
    ArXiv e-prints
  • A Neural Dynamic Architecture for Reaching and Grasping Integrates Perception and Movement Generation and Enables On-Line Updating
    Knips, G., Zibner, S. K. U., Reimann, H., & Schöner, G.
    Frontiers in Neurorobotics, 11(March), 9:1–14
  • Gaussian-binary restricted Boltzmann machines for modeling natural image statistics
    Melchior, J., Wang, N., & Wiskott, L.
    PLOS ONE, 12(2), 1–24
  • Generating sequences in recurrent neural networks for storing and retrieving episodic memories
    Bayati, M., Melchior, J., Wiskott, L., & Cheng, S.
    In Proc. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2
  • Gedächtnisverbesserung: Möglichkeiten und kritische Betrachtung
    Cheng, S.
    In F. Hüttemann & Liggieri, K. (Eds.), Die Grenze . Diskurse des Transhumanismus. (p. invited contribution) Bielefeld: transcript Verlag
  • Consolidation of Episodic Memory: An Epiphenomenon of Semantic Learning
    Cheng, S.
    In N. Axmacher & Rasch, B. (Eds.), Cognitive Neuroscience of Memory Consolidation (pp. 57–72) Cham, Switzerland: Springer International Publishing
  • Experience-Dependency of Reliance on Local Visual and Idiothetic Cues for Spatial Representations Created in the Absence of Distal Information
    Draht, F., Zhang, S., Rayan, A., Schönfeld, F., Wiskott, L., & Manahan-Vaughan, D.
    Frontiers in Behavioral Neuroscience, 11(92)
  • Extensions of Hierarchical Slow Feature Analysis for Efficient Classification and Regression on High-Dimensional Data
    Escalante-B., A. N.
    Doctoral thesis, Ruhr University Bochum, Faculty of Electrical Engineering and Information Technology
  • A Fast Incremental BSP Tree Archive for Non-dominated Points
    Glasmachers, T.
    In Evolutionary Multi-Criterion Optimization (EMO) Springer
  • Limits of End-to-End Learning
    Glasmachers, T.
    In Proceedings of the 9th Asian Conference on Machine Learning (ACML)
  • Global Convergence of the (1+1) Evolution Strategy
    Glasmachers, T.
    arxiv.org
  • Cue Integration by Similarity Rank List Coding — Application to Invariant Object Recognition
    Grieben, R., & Würtz, R. P.
    In Proceedings of IEEE International Workshops on Foundations and Applications of Self* Systems (pp. 132–137)
  • Texture attribute synthesis and transfer using feed-forward CNNs
    Irmer, T., Glasmachers, T., & Maji, S.
    In Winter Conference on Applications of Computer Vision (WACV) IEEE
  • Catching the voltage gradient—asymmetric boost of cortical spread generates motion signals across visual cortex: a brief review with special thanks to Amiram Grinvald
    Jancke, D.
    Neurophoton., 4(3), 031206
  • Intrinsically Motivated Acquisition of Modular Slow Features for Humanoids in Continuous and Non-Stationary Environments
    Kompella, V. R., & Wiskott, L.
    arXiv preprint arXiv:1701.04663
  • Qualitative and Quantitative Assessment of Step Size Adaptation Rules
    Krause, O., Glasmachers, T., & Igel, C.
    In Conference on Foundations of Genetic Algorithms (FOGA) ACM
  • Mouse Tracking Shows Attraction to Alternative Targets While Grounding Spatial Relations
    Lins, J., & Schöner, G.
    In Proceedings of the 39th Annual Conference of the Cognitive Science Society Austin, TX: Cognitive Science Society
  • A Neural-Dynamic Architecture for Concurrent Estimation of Object Pose and Identity
    Lomp, O., Faubel, C., & Schöner, G.
    Frontiers in Neurorobotics, 11(April), 23
  • From grid cells to place cells with realistic field sizes
    Neher, T., Azizi, A. H., & Cheng, S.
    PLoS ONE, 12(7), e0181618
  • A multi-joint model of quiet , upright stance accounts for the “uncontrolled manifold”-structure of joint variance
    Reimann, H., & Schöner, G.
    Biological Cybernetics, in press
  • A neural dynamic model generates descriptions of object-oriented actions
    Richter, M., Lins, J., & Schöner, G.
    Topics in Cognitive Science, 9(1), 35–47
  • Reaching for objects : a neural process account in a developmental perspective
    Schöner, G., Tekülve, J., & Zibner, S.
    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
  • Dynamic Neural Fields with Intrinsic Plasticity
    Strub, C., Schöner, G., Wörgötter, F., & Sandamirskaya, Y.
    Frontiers in Computational Neuroscience, 11(August), 74
  • A simulated car-park environment for the evaluation of video-based on-site parking guidance systems
    Tschentscher, M., Pruß, B., & Horn, D.
    In Proceedings of the IEEE Intelligent Vehicles Symposium (IV) (pp. 1571–1576)
  • Graph-based predictable feature analysis
    Weghenkel, B., Fischer, A., & Wiskott, L.
    Machine Learning, 1–22
  • Taxonomy and Unity of Memory
    Werning, M., & Cheng, S.
    In S. Bernecker & Michaelian, K. (Eds.), The Routledge Handbook of Philosophy of Memory (p. forthcoming) London: Routledge
  • No need for meta-representation: How scenario construction explains the epistemic generativity and privileged epistemic status of episodic memory
    Werning, M., & Cheng, S.
    Behavioral and Brain Sciences, in press

2016

  • Topological Schemas of Cognitive Maps and Spatial Learning
    Babichev, A., Cheng, S., & Dabaghian, Y. A.
    Frontiers in Computational Neuroscience, 10, 18
  • What is episodic memory if it is a natural kind?
    Cheng, S., & Werning, M.
    Synthese, 193(5), 1345–1385
  • Dissociating memory traces and scenario construction in mental time travel
    Cheng, S., Werning, M., & Suddendorf, T.
    Neuroscience & Biobehavioral Reviews, 60, 82–89
  • Fast model selection by limiting SVM training times
    Demircioğlu, A., Horn, D., Glasmachers, T., Bischl, B., & Weihs, C.
    arxiv.org
  • A Unified View on Multi-class Support Vector Classification
    Doğan, Ü., Glasmachers, T., & Igel, C.
    Journal of Machine Learning Research, 17(45), 1–32
  • Theoretical analysis of the optimal free responses of graph-based SFA for the design of training graphs.
    Escalante-B., A. N., & Wiskott, L.
    Journal of Machine Learning Research, 17(157), 1–36
  • Improved graph-based SFA: Information preservation complements the slowness principle
    Escalante-B., A. N., & Wiskott, L.
    e-print arXiv:1601.03945
  • Phase based forgery detection of JPEG anti forensics
    Fahmy, G., & Wurtz, R.
    In Signal Processing and Information Technology (ISSPIT), 2016 IEEE International Symposium on (pp. 144–149) IEEE
  • Finite Sum Acceleration vs. Adaptive Learning Rates for the T