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in press

  • Gedächtnisverbesserung: Möglichkeiten und kritische Betrachtung
    Cheng, S.
    In F. Hüttemann & Liggieri, K. (Eds.), Die Grenze "Mensch". Diskurse des Transhumanismus. Bielefeld: transcript Verlag

2021

  • 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)

2020

  • Improving sensory representations using episodic memory
    Görler, R., Wiskott, L., & Cheng, S.
    Hippocampus, 30(6), 638–656
  • 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, 101901
  • 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
  • The Hessian Estimation Evolution Strategy
    Glasmachers, T., & Krause, O.
    In Parallel Problem Solving from Nature (PPSN XVII) Springer
  • Convergence Analysis of the Hessian Estimation Evolution Strategy
    Glasmachers, T., & Krause, O.
    arxiv.org
  • 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)
  • Learning Cognitive Map Representations for Navigation by Sensory-Motor Integration
    Zhao, D., Zhang, Z., Lu, H., Cheng, S., Si, B., & Feng, X.
    IEEE Transactions on Cybernetics, 1–14

2019

  • Improved graph-based SFA: information preservation complements the slowness principle
    Escalante-B., A. N., & Wiskott, L.
    Machine Learning
  • Hippocampal Reactivation Extends for Several Hours Following Novel Experience
    Giri, B., Miyawaki, H., Mizuseki, K., Cheng, S., & Diba, K.
    The Journal of Neuroscience, 39(5), 866–875
  • Computer mouse tracking reveals motor signatures in a cognitive task of spatial language grounding
    Lins, J., & Schöner, G.
    Attention, Perception, & Psychophysics
  • Moment Vector Encoding of Protein Sequences for Supervised Classification
    Altartouri, H., & Glasmachers, T.
    In Practical Applications of Computational Biology and Bioinformatics, 13th International Conference (pp. 25–35) Springer International Publishing
  • CaMello-XR enables visualization and optogenetic control of Gq/11 signals and receptor trafficking in GPCR-specific domains
    Eickelbeck, D., Karapinar, R., Jack, A., Suess, S. T., Barzan, R., Azimi, Z., et al.
    Communications Biology, 2(1)
  • 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
  • 6DoF Vehicle Pose Estimation Using Segmentation-Based Part Correspondences
    Barowski, T., Szczot, M., & Houben, S.
    In Proceedings of the IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 573–580)
  • A Parallel RatSlam C++ Library Implementation
    de Souza Muñoz, M. E., Menezes, M. C., de Freitas, E. P., Cheng, S., de Almeida Neto, A., de Oliveira, A. C. M., & de Almeida Ribeiro, P. R.
    In Communications in Computer and Information Science (pp. 173–183) Springer International Publishing
  • Challenges of Convex Quadratic Bi-objective Benchmark Problems
    Glasmachers, T.
    In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 559–567) ACM
  • Global Convergence of the (1+1) Evolution Strategy
    Glasmachers, T.
    Evolutionary Computation Journal (ECJ)
  • Boosting Reinforcement Learning with Unsupervised Feature Extraction
    Hakenes, S., & Glasmachers, T.
    In Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation (pp. 555–566) Springer International Publishing
  • How do memory modules differentially contribute to familiarity and recollection?
    Hakobyan, O., & Cheng, S.
    Behavioral and Brain Sciences, 42, e288
  • Measuring the Data Efficiency of Deep Learning Methods
    Hlynsson, H., Escalante-B., A., & Wiskott, L.
    In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods SCITEPRESS - Science and Technology Publications
  • Learning Gradient-Based ICA by Neurally Estimating Mutual Information
    Hlynsson, H. D. ′ið, & Wiskott, L.
    In C. Benzmüller & Stuckenschmidt, H. (Eds.), KI 2019: Advances in Artificial Intelligence (pp. 182–187) Cham: Springer International Publishing
  • Learning gradient-based ICA by neurally estimating mutual information
    Hlynsson, H. D., & Wiskott, L.
    arXiv, arXiv–1904
  • Measuring the Data Efficiency of Deep Learning Methods
    Hlynsson, H. D., Wiskott, L., & others,
    arXiv, arXiv–1907