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in press

  • Improved Protein Function Prediction by Combining Clustering with Ensemble Classification
    Altartouri, H., & Glasmachers, T.
    Journal of Advances in Information Technology (JAIT)
  • Gedächtnisverbesserung: Möglichkeiten und kritische Betrachtung
    Cheng, S.
    In F. Hüttemann & Liggieri, K. (Eds.), Die Grenze "Mensch". Diskurse des Transhumanismus. Bielefeld: transcript Verlag
  • Emergence of complex dynamics of choice due to repeated exposures to extinction learning
    Donoso, J. R., Packheiser, J., Pusch, R., Lederer, Z., Walther, T., Uengoer, M., et al.
    Animal Cognition
  • Bridging the gap between single receptor type activity and whole-brain dynamics
    Jancke, D., Herlitze, S., Kringelbach, M. L., & Deco, G.
    The FEBS Journal
  • Fully Automated, Realistic License Plate Substitution in Real-Life Images
    Kacmaz, U., Melchior, J., Horn, D., Witte, A., Schoenen, S., & Houben, S.
    In Proceedings of the IEEE Intelligent Transportation Systems Conference (ITSC), accepted
  • Self-referential False Associations: A Self-enhanced Constructive Effect for Verbal but not Pictorial Stimuli
    Wang, J., Otgaar, H., Howe, M. L., & Cheng, S.
    Quarterly Journal of Experimental Psychology

2021

  • Recognition Receiver Operating Characteristic Curves: The Complex Influence of Input Statistics, Memory, and Decision-making
    Hakobyan, O., & Cheng, S.
    Journal of Cognitive Neuroscience, 33(6), 1032–1055
  • Darks and Lights, the `Yin–Yang′ of Vision Depends on Luminance
    Jancke, D.
    Trends in Neurosciences, 44(5), 339–341
  • Trial-by-trial dynamics of reward prediction error-associated signals during extinction learning and renewal
    Packheiser, J., Donoso, J. R., Cheng, S., Güntürkün, O., & Pusch, R.
    Progress in Neurobiology, 197, 101901
  • Context-dependent extinction learning emerging from raw sensory inputs: a reinforcement learning approach
    Walther, T., Diekmann, N., Vijayabaskaran, S., Donoso, J. R., Manahan-Vaughan, D., Wiskott, L., & Cheng, S.
    Scientific Reports, 11(1)
  • Application of Reinforcement Learning to a Mining System
    Fidencio, A., Naro, D., & Glasmachers, T.
    In 19th IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI′2021)
  • Convergence Analysis of the Hessian Estimation Evolution Strategy
    Glasmachers, T., & Krause, O.
    Evolutionary Computation Journal (ECJ) MIT Press
  • A neural dynamic process model of combined bottom-up and top-down guidance in triple conjunction visual search
    Grieben, R., & Schöner, G.
    In T. Fitch, Lamm, C., Leder, H., & Teßmar-Raible, K. (Eds.), Proceedings of the 43nd Annual Conference of the Cognitive Science Society Cognitive Science Society
  • Automated Selection of High-Quality Synthetic Images for Data-Driven Machine Learning: A Study on Traffic Signs
    Horn, D., Janssen, L., & Houben, S.
    In Proceedings of the IEEE Intelligent Vehicles Symposium (IV) (pp. 832–837)
  • Exploring Slow Feature Analysis for Extracting Generative Latent Factors
    Menne, M., Schüler, M., & Wiskott, L.
    In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods SCITEPRESS - Science and Technology Publications

2020

  • Improving sensory representations using episodic memory
    Görler, R., Wiskott, L., & Cheng, S.
    Hippocampus, 30(6), 638–656
  • Automatic Tuning of RatSLAM′s Parameters by Irace and Iterative Closest Point
    Menezes, M. C., Muñoz, M. E. S., Freitas, E. P., Cheng, S., Walther, T., Neto, A. A., et al.
    In IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society (pp. 562–568)
  • Perception of the difference between past and present stimulus: A rare orientation illusion may indicate incidental access to prediction error-like signals
    Staadt, R., Philipp, S. T., Cremers, J. L., Kornmeier, J., & Jancke, D.
    PLOS ONE, 15(5), e0232349
  • Separable gain control of ongoing and evoked activity in the visual cortex by serotonergic input
    Azimi, Z., Barzan, R., Spoida, K., Surdin, T., Wollenweber, P., Mark, M. D., et al.
    eLife, 9
  • Motor Habituation: Theory and Experiment
    Aerdker, S., Feng, J., & Schöner, G.
    10th Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob 2020) pp. 160-167
  • Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation
    Ali, O., Saif-ur-Rehman, M., Dyck, S., Glasmachers, T., Iossifidis, I., & Klaes, C.
    arXiv.org
  • A Versatile Combination of Classifiers for Protein Function Prediction
    Altartouri, H., & Glasmachers, T.
    The Twelfth International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies
  • The Hessian Estimation Evolution Strategy
    Glasmachers, T., & Krause, O.
    In Parallel Problem Solving from Nature (PPSN XVII) Springer
  • Scene memory and spatial inhibition in visual search: A neural dynamic process model and new experimental evidence
    Grieben, R., Tekülve, J., Zibner, S. K. U., Lins, J., Schneegans, S., & Schöner, G.
    Attention, Perception, & Psychophysics
  • Latent Representation Prediction Networks
    Hlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.
    arXiv preprint arXiv:2009.09439
  • Fully Automated Traffic Sign Substitution in Real-World Images for Large-Scale Data Augmentation
    Horn, D., & Houben, S.
    In Proceedings of the IEEE Intelligent Vehicles Symposium (IV) (pp. 194–200)
  • Non-local Optimization: Imposing Structure on Optimization Problems by Relaxation
    Müller, N., & Glasmachers, T.
    arXiv.org
  • Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing
    Oller, D., Cuccu, G., & Glasmachers, T.
    In International Conference on Autonomous Agents and Multi-Agent Systems
  • Singular Sturm-Liouville Problems with Zero Potential (q=0) and Singular Slow Feature Analysis
    Richthofer, S., & Wiskott, L.
    CoRR e-print arXiv:2011.04765
  • Grounding Spatial Language in Perception by Combining Concepts in a Neural Dynamic Architecture
    Sabinasz, D., Richter, M., Lins, J., & Schöner, G.
    In S. Denison, Mack, M., Xu, Y., & Armstrong, B. C. (Eds.), Proceedings of the 42nd Annual Conference of the Cognitive Science Society (pp. 620–626) Cognitive Science Society
  • SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm
    Saif-ur-Rehman, M., Ali, O., Dyck, S., Lienkämper, R., Metzler, M., Parpaley, Y., et al.
    Journal of Neural Engineering
  • AI for Social Good: Unlocking the Opportunity for Positive Impact
    Tomašev, N., Cornebise, J., Hutter, F., Picciariello, A., Connelly, B., Belgrave, D. C. M., et al.
    Nature Communications, (2468)