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

2025

  • Appetitive and aversive classical conditioning: Self-reports and physiological responses
    Petrenko, M., Coenen, L., Doubliez, A., Ernst, T. M., Nio, E., Diekmann, N., et al.
    Behavioural Brain Research, 484, 115509
  • Inverting Transformer-based Vision Models
    Rathjens, J., Reyhanian, S., Kappel, D., & Wiskott, L.
    arXiv e-print
  • A Taxonomy of Spatial Navigation in Mammals: Insights from Computational Modeling
    Vijayabaskaran, S., Zeng, X., Ghazinouri, B., Wiskott, L., & Cheng, S.
    Center for Open Science
    link
  • Remembering without (representational) memory: A neuro-computational study on regaining categoricity and compositionality from minimal traces
    Fayyaz, Z., Righetti, F., Wiskott, L., & Werning, M.
    Center for Open Science
    link
  • What is the nature of cache memory in Parids? A comment on Chettih et al. 2024
    Smulders, T. V., & Cheng, S.
    Animal Cognition, 28(1)
  • The Cost of Behavioral Flexibility: Reversal Learning Driven by a Spiking Neural Network
    Ghazinouri, B., & Cheng, S.
    In O. Brock & Krichmar, J. (Eds.), From Animals to Animats 17 (pp. 39–50) Cham: Springer Nature Switzerland
  • Deep Reinforcement Learning Based Navigation with Macro Actions and Topological Maps
    Hakenes, S., & Glasmachers, T.
    arXiv, Accepted for LOD 2025
  • Global remapping emerges as the mechanism for renewal of context-dependent behavior in a reinforcement learning model
    Kappel, D., & Cheng, S.
    Frontiers in Computational Neuroscience, 18
  • SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process
    SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process
  • Human cerebellum and ventral tegmental area interact during extinction of learned fear
    Nio, E., Pereira, P. P., Diekmann, N., Petrenko, M., Doubliez, A., Ernst, T. M., et al.

2024

  • Beyond Focal Lesions: Dynamical Network Effects of White Matter Hyperintensities
    Leone, R., Geysen, S., Deco, G., & Kobeleva, X.
    Human Brain Mapping, 45(17)
  • Exploring the limits of hierarchical world models in reinforcement learning
    Schiewer, R., Subramoney, A., & Wiskott, L.
    Scientific Reports, 14(1)
    Link to paper
  • Gain control of sensory input across polysynaptic circuitries in mouse visual cortex by a single G protein-coupled receptor type (5-HT2A)
    Barzan, R., Bozkurt, B., Nejad, M. M., Süß, S. T., Surdin, T., Böke, H., et al.
    Nature Communications, 15(1)
  • Distinct mechanisms and functions of episodic memory
    Cheng, S.
    Philosophical Transactions of the Royal Society B: Biological Sciences, 379(1913)
  • Analysis of a Generative Model of Episodic Memory Based on Hierarchical VQ-VAE and Transformer
    Reyhanian, S., Fayyaz, Z., & Wiskott, L.
    In Proceedings of the 33rd International Conference on Artificial Neural Networks (ICANN 2024), Lugano, Switzerland Springer Nature Switzerland
    link
  • Neural Dynamic Principles for an Intentional Embodied Agent
    Tekülve, J., & Schöner, G.
    Cognitive Science, 48(9)
    reprint
  • Hebbian Descent: A Unified View on Log-Likelihood Learning
    Melchior, J., Schiewer, R., & Wiskott, L.
    Neural Computation, 36(9), 1669–1712
    link
  • Next-generation phenotyping integrated in a national framework for patients with ultrarare disorders improves genetic diagnostics and yields new molecular findings
    Schmidt, A., Danyel, M., Grundmann, K., Brunet, T., Klinkhammer, H., Hsieh, T. -C., et al.
    Nature Genetics, 56(8), 1644–1653
  • Inverting a model of neuromuscular control to estimate descending activation patterns that generate fast-reaching movements
    Hummert, C., Zhang, L., & Schöner, G.
    Journal of Neurophysiology, 131(6), 1271–1285
    reprint
  • A neural network model for online one-shot storage of pattern sequences
    Melchior, J., Altamimi, A., Bayati, M., Cheng, S., & Wiskott, L.
    PLOS ONE, 19(6), 1–28
    link
  • *Best Paper Nominee* Gaining Insights into Course Difficulty Variations Using Item Response Theory
    Baucks, F., Schmucker, R., & Wiskott, L.
    In LAK24: 14th International Learning Analytics and Knowledge Conference (pp. 450–461) New York, NY, USA: Association for Computing Machinery
    pdf
  • Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies
    Maus, T., Zengeler, N., Sänger, D., & Glasmachers, T.
    MDPI Sensors, 24(7), 2114
    Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies
  • Layer-wise linear mode connectivity
    Adilova, L., Andriushchenko, M., Kamp, M., Fischer, A., & Jaggi, M.
    In The Twelfth International Conference on Learning Representations
  • Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning
    Baucks, F., Schmucker, R., Borchers, C., Pardos, Z. A., & Wiskott, L.
    In Proceedings of the Eleventh ACM Conference on Learning @ Scale (pp. 165–176) Atlanta, GA, USA: Association for Computing Machinery
    link link
  • Empowering Advisors: Designing a Dashboard for University Student Guidance
    Baucks, F., & Wiskott, L.
    In P. Salden & Leschke, J. (Eds.), Learning Analytics und Künstliche Intelligenz in Studium und Lehre: Erfahrungen und Schlussfolgerungen aus einer hochschulweiten Erprobung (pp. 27–44) Wiesbaden: Springer Fachmedien Wiesbaden
    link
  • tachAId—An interactive tool supporting the design of human-centered AI solutions
    Bauroth, M., Rath-Manakidis, P., Langholf, V., Wiskott, L., & Glasmachers, T.
    Frontiers in Artificial Intelligence, 7
    link
  • Visual Computing for Autonomous Driving
    Chen, S., Gou, L., Kamp, M., & Sun, D.
    IEEE Computer Graphics and Applications, 44(3), 11–13
  • Ökolopoly: Case Study on Large Action Spaces in Reinforcement Learning
    Engelhardt, R. C., Raycheva, R., Lange, M., Wiskott, L., & Konen, W.
    In G. Nicosia, Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P. M., & Umeton, R. (Eds.), Machine Learning, Optimization, and Data Science (pp. 109–123) Cham: Springer Nature Switzerland
  • A Potential Potential Function for a Variable-Metric Evolution Strategy
    Frank, S., & Glasmachers, T.
    In Parallel Problem Solving from Nature – PPSN XVIII (pp. 221–235) Springer Nature Switzerland
    pdf
  • Navigation and the efficiency of spatial coding: insights from closed-loop simulations
    Ghazinouri, B., Nejad, M. M., & Cheng, S.
    Brain Structure and Function, 229(3), 577–592
    Navigation and the efficiency of spatial coding: insights from closed-loop simulations
  • ROBOVERINE: A human-inspired neural robotic process model of active visual search and scene grammar in naturalistic environments
    Grieben, R., Sehring, S., Tekülve, J., Spencer, J. P., & Schöner, G.
    In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) IEEE
    Preprint
  • Visual selective attention: Priority is all you need
    Grieben, R., Spencer, J. P., & Schöner, G.
    In L. K. Samuelson, Frank, S. L., Toneva, M., Mackey, A., & Hazeltine, E. (Eds.), Proceedings of the 46th Annual Conference of the Cognitive Science Society
  • A Neural Process Model of Structure Mapping Accounts for Children’s Development of Analogical Mapping by Change in Inhibitory Control
    Kang, M., Sabinasz, D., & Schöner, G.
    In L. K. Samuelson, Frank, S. L., Toneva, M., Mackey, A., & Hazeltine, E. (Eds.), Proceedings of the 46th Annual Conference of the Cognitive Science Society
    pdf
  • Interaction of polarity and truth value - A neural dynamic architecture of negation processing
    Kati, L., Sabinasz, D., Schöner, G., & Kaup, B.
    In L. K. Samuelson, Frank, S. L., Toneva, M., Mackey, A., & Hazeltine, E. (Eds.), Proceedings of the 46th Annual Conference of the Cognitive Science Society
    Paper
  • Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks
    Lange, M., Engelhardt, R. C., Konen, W., & Wiskott, L.
    In eXplainable AI approaches for Deep Reinforcement Learning
    link
  • *Best Paper Award* Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-visual Environments: A Comparison
    Lange, M., Krystiniak, N., Engelhardt, R. C., Konen, W., & Wiskott, L.
    In G. Nicosia, Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P. M., & Umeton, R. (Eds.), Machine Learning, Optimization, and Data Science (pp. 177–191) Cham: Springer Nature Switzerland
    link
  • Robustness in Hopfield neural networks with biased memory patterns
    Montazeri, A., & Schmidt, R.
    bioRxiv
    link
  • Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering
    Pendyala, A., Atamna, A., & Glasmachers, T.
    In Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track (ECML-PKDD) (pp. 150–165) Cham: Springer Nature Switzerland
  • ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation
    Pendyala, A., Dettmer, J., Glasmachers, T., & Atamna, A.
    In Machine Learning, Optimization, and Data Science (pp. 78–92) Cham: Springer Nature Switzerland
  • ProtoP-OD: Explainable Object Detection with Prototypical Parts
    Rath-Manakidis, P., Strothmann, F., Glasmachers, T., & Wiskott, L.
    arXiv
    link
  • Antagonism between Classification and Reconstruction Processes in Deep Predictive Coding Networks
    Rathjens, J., & Wiskott, L.
    In ESANN 2024 proceesdings (pp. 161–166) Bruges (Belgium) and online: Ciaco - i6doc.com
    link
  • Classification and Reconstruction Processes in Deep Predictive Coding Networks: Antagonists or Allies?
    Rathjens, J., & Wiskott, L.
    arXiv
    link
  • Modularity in Nervous Systems - a Key to Efficient Adaptivity for Deep Reinforcement Learning
    Schilling, M., Hammer, B., Ohl, F. W., Ritter, H. J., & Wiskott, L.
    Cogn. Comput., 16(5), 2358–2373
    link
  • What is the relationship between Slow Feature Analysis and the Successor Representation?
    Seabrook, E., & Wiskott, L.
    arXiv
    link
  • A Neural Dynamic Model Autonomously Drives a Robot to Perform Structured Sequences of Action Intentions
    Sehring, S., Koebe, R., Aerdker, S., & Schöner, G.
    In L. K. Samuelson, Frank, S. L., Toneva, M., Mackey, A., & Hazeltine, E. (Eds.), Proceedings of the 46th Annual Conference of the CognitiveScience Society
    A Neural Dynamic Model Autonomously Drives a Robot to Perform Structured Sequences of Action Intentions
  • Landscaping Linear Mode Connectivity
    Singh, S. P., Adilova, L., Kamp, M., Fischer, A., Schölkopf, B., & Hofmann, T.
    In ICML Workshop on High-dimensional Learning Dynamics: The Emergence of Structure and Reasoning
  • Retinal ganglion cell and microvascular density loss in hereditary spastic paraplegia
    Turski, G. N., Turski, C. A., Grobe-Einsler, M., Kobeleva, X., Turski, J. S., Holz, F. G., et al.
    Restorative Neurology and Neuroscience, 41(5–6), 229–239
  • Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles
    Yang, F., Le Bodic, P., Kamp, M., & Boley, M.
    In International Conference on Artificial Intelligence and Statistics (pp. 1117–1125) PMLR

2023

  • Induction of excitatory brain state governs plastic functional changes in visual cortical topology
    Eysel, U. T., & Jancke, D.
    Brain Structure and Function
  • Whole-brain modeling of the differential influences of amyloid-beta and tau in Alzheimer’s disease
    Patow, G., Stefanovski, L., Ritter, P., Deco, G., & Kobeleva, X.
    Alzheimer’s Research & Therapy, 15(1)
  • Working memory performance is tied to stimulus complexity
    Pusch, R., Packheiser, J., Azizi, A. H., Sevincik, C. S., Rose, J., Cheng, S., et al.
    Communications Biology, 6(1)
    Working memory performance is tied to stimulus complexity
  • Response retention and apparent motion effect in visual cortex models
    Tiselko, V. S., Volgushev, M., Jancke, D., & Chizhov, A. V.
    PLOS ONE, 18(11), e0293725
  • Tunable synaptic working memory with volatile memristive devices
    Ricci, S., Kappel, D., Tetzlaff, C., Ielmini, D., & Covi, E.
    Neuromorphic Computing and Engineering, 3(4), 044004
  • Neural dynamic foundations of a theory of higher cognition: the case of grounding nested phrases
    Sabinasz, D., Richter, M., & Schöner, G.
    Cognitive Neurodynamics
    reprint
  • A Tutorial on the Spectral Theory of Markov Chains
    Seabrook, E., & Wiskott, L.
    Neural Computation, 35(11), 1713–1796
    link
  • A map of spatial navigation for neuroscience
    Parra-Barrero, E., Vijayabaskaran, S., Seabrook, E., Wiskott, L., & Cheng, S.
    Neuroscience & Biobehavioral Reviews, 152, 105200
    A map of spatial navigation for neuroscience
  • Hierarchical Transformer VQ-VAE: An investigation of attentional selection in a generative model of episodic memory
    Reyhanian, S., Fayyaz, Z., & Wiskott, L.
    Bernstein Conference
  • Von der Forschung in die Praxis: Entwicklung eines Dashboards für die Studienberatung
    Baucks, F., & Wiskott, L.
    Abstract presented at 2nd Learning AID
    pdf
  • The stabilization of visibility for sequentially presented, low-contrast objects: Experiments and neural field model
    Hock, H. S., & Schöner, G.
    Journal of Vision, 23(8), 12–12
    reprint
  • A Multisession SLAM Approach for RatSLAM
    Menezes, M., Muñoz, M., de Freitas, E. P., Cheng, S., de Almeida Neto, A., Ribeiro, P., & Oliveira, A.
    Journal of Intelligent & Robotic Systems, 108(4)
    A Multisession SLAM Approach for RatSLAM
  • Beyond Weights: Deep learning in Spiking Neural Networks with pure synaptic-delay training
    Grappolini, E. W., & Subramoney, A.
    In International Conference on Neuromorphic Systems (ICONS ′23), Santa Fe, NM, USA ACM
  • Two modes of midfrontal theta suggest a role in conflict and error processing
    Muralidharan, V., Aron, A. R., Cohen, M. X., & Schmidt, R.
    NeuroImage, 273, 120107
    preprint on biorxiv Journal version
  • Optogenetics reveals paradoxical network stabilizations in hippocampal CA1 and CA3
    de Jong, L. W., Nejad, M. M., Yoon, E., Cheng, S., & Diba, K.
    Current Biology, 33(9), 1689–1703.e5
    Optogenetics reveals paradoxical network stabilizations in hippocampal CA1 and CA3 Optogenetics reveals paradoxical network stabilizations in hippocampal CA1 and CA3- SupplementaryMaterial
  • Learning to predict future locations with internally generated theta sequences
    Parra-Barrero, E., & Cheng, S.
    PLOS Computational Biology, 19(5), e1011101
    Learning to predict future locations with internally generated theta sequences
  • Efficient Recurrent Architectures through Activity Sparsity and Sparse Back-Propagation through Time
    Subramoney, A., Nazeer, K. K., Schöne, M., Mayr, C., & Kappel, D.
    In International Conference on Learning Representations
    Openreview link arXiv version
  • Linking early-life bilingualism and cognitive advantage in older adulthood
    Ballarini, T., Kuhn, E., Röske, S., Altenstein, S., Bartels, C., Buchholz, F., et al.
    Neurobiology of Aging, 124, 18–28
  • Efficient Real Time Recurrent Learning through Combined Activity and Parameter Sparsity
    Anand Subramoney,
    In ICLR 2023 Workshop on Sparse Neural Networks
    arXiv
  • A model of hippocampal replay driven by experience and environmental structure facilitates spatial learning
    Diekmann, N., & Cheng, S.
    eLife, 12, e82301
    A model of hippocampal replay driven by experience and environmental structure facilitates spatial learning
  • CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning
    Diekmann, N., Vijayabaskaran, S., Zeng, X., Kappel, D., Menezes, M. C., & Cheng, S.
    Frontiers in Neuroinformatics, 17
    CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning
  • FAM: Relative Flatness Aware Minimization
    Adilova, L., Abourayya, A., Li, J., Dada, A., Petzka, H., Egger, J., et al.
    TAGML2023
  • Re-interpreting Rules Interpretability
    Adilova, L., Kamp, M., Andrienko, G., & Andrienko, N.
    International Journal of Data Science and Analytics
  • Ein Dashboard für die Studienberatung: Technische Infrastruktur und Studienverlaufsplanung im Projekt KI:edu.nrw
    Baucks, F., Leschke, J., Metzger, C., & Wiskott, L.
    In Workshop Proceedings of the 21th Fachtagung Bildungstechnologien (DELFI) (pp. 185–188) Bonn: Gesellschaft für Informatik e.V.
    link
  • *Best Paper Nominee* Mitigating Biases using an Additive Grade Point Model: Towards Trustworthy Curriculum Analytics Measures
    Baucks, F., & Wiskott, L.
    In 21. Fachtagung Bildungstechnologien (DELFI) (pp. 41–52) Bonn: Gesellschaft für Informatik e.V.
    link
  • Tracing Changes in University Course Difficulty Using Item-Response Theory
    Baucks*, F., Schmucker*, R., & Wiskott, L.
    AAAI Workshop on AI for Education: https://ai4ed.cc/workshops/aaai2023
    Presentation Video pdf
  • Parker: Data Fusion through Consistent Repairs using Edit Rules under Partial Keys
    Bronselaer, A., & Acosta, M.
    Information Fusion
  • Sample-Based Rule Extraction for Explainable Reinforcement Learning
    Engelhardt, R. C., Lange, M., Wiskott, L., & Konen, W.
    In G. Nicosia, Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P., Di Fatta, G., et al. (Eds.), Machine Learning, Optimization, and Data Science (pp. 330–345) Cham: Springer Nature Switzerland
  • Iterative Oblique Decision Trees Deliver Explainable RL Models
    Engelhardt, R. C., Oedingen, M., Lange, M., Wiskott, L., & Konen, W.
    Algorithms, 16(6)
    link
  • Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia
    Gaubert, M., Dell’Orco, A., Lange, C., Garnier-Crussard, A., Zimmermann, I., Dyrba, M., et al.
    Frontiers in Psychiatry, 13
  • Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation
    Hakenes, S., & Glasmachers, T.
    arXiv
    link
  • Federated Learning from Small Datasets
    Kamp, M., Fischer, J., & Vreeken, J.
    In International Conference on Learning Representations (ICLR)
    federatedlearningsmalldatasets
  • Open-source skull reconstruction with MONAI
    Li, J., Ferreira, A., Puladi, B., Alves, V., Kamp, M., Kim, M., et al.
    SoftwareX, 23, 101432
  • MedShapeNet – A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
    Li, J., Pepe, A., Gsaxner, C., Luijten, G., Jin, Y., Ambigapathy, N., et al.
  • Benchmarks for Physical Reasoning AI
    Melnik, A., Schiewer, R., Lange, M., Muresanu, A. I., Saeidi, M., Garg, A., & Ritter, H.
    Transactions on Machine Learning Research
    link
  • Nothing but Regrets - Privacy-Preserving Federated Causal Discovery
    Mian, O., Kaltenpoth, D., Kamp, M., & Vreeken, J.
    In International Conference on Artificial Intelligence and Statistics (AISTATS)
  • Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments
    Mian, O., Kamp, M., & Vreeken, J.
    In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)
    mian2023information
  • Transient oscillations as computations for cognition: Analysis, modeling and function
    Schmidt, R., Rose, J., & Muralidharan, V.
    Current Opinion in Neurobiology, 83, 102796
    article link
  • Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks
    Schwabe, T., & Acosta, M.
    arXiv
  • Solidity Meets Surprise: Cerebral and Behavioral Effects of Learning from Episodic Prediction Errors
    Siestrup, S., Jainta, B., Cheng, S., & Schubotz, R. I.
    Journal of Cognitive Neuroscience, 35(2), 291–313
    Solidity Meets Surprise: Cerebral and Behavioral Effects of Learning from Episodic Prediction Errors
  • Optogenetic activation of mGluR1 signaling in the cerebellum induces synaptic plasticity
    Surdin, T., Preissing, B., Rohr, L., Grömmke, M., Böke, H., Barcik, M., et al.
    iScience, 26(1), 105828
  • Modeling the function of episodic memory in spatial learning
    Zeng, X., Diekmann, N., Wiskott, L., & Cheng, S.
    Frontiers in Psychology, 14
    Modeling the function of episodic memory in spatial learning Modeling the function of episodic memory in spatial learning - SupplementaryMaterial

2022

  • Midlife occupational cognitive requirements protect cognitive function in old age by increasing cognitive reserve
    Kleineidam, L., Wolfsgruber, S., Weyrauch, A. -S., Zulka, L. E., Forstmeier, S., Roeske, S., et al.
    Frontiers in Psychology, 13
  • xRatSLAM: An Extensible RatSLAM Computational Framework
    de Souza Muñoz, M. E., Menezes, M. C., de Freitas, E. P., Cheng, S., de Almeida Ribeiro, P. R., de Almeida Neto, A., & de Oliveira, A. C. M.
    Sensors, 22(21), 8305
  • A Neural Dynamic Model Perceptually Grounds Nested Noun Phrases
    Sabinasz, D., & Schöner, G.
    Topics in Cognitive Science
    A Neural Dynamic Model Perceptually Grounds Nested Noun Phrases
  • Navigation task and action space drive the emergence of egocentric and allocentric spatial representations
    Vijayabaskaran, S., & Cheng, S.
    PLOS Computational Biology, 18(10), e1010320
  • Where was the toaster? A systematic investigation of semantic construction in a new virtual episodic memory paradigm
    Zöllner, C., Klein, N., Cheng, S., Schubotz, R. I., Axmacher, N., & Wolf, O. T.
    Quarterly Journal of Experimental Psychology, 174702182211166
  • A Model of Semantic Completion in Generative Episodic Memory
    Fayyaz, Z., Altamimi, A., Zoellner, C., Klein, N., Wolf, O. T., Cheng, S., & Wiskott, L.
    Neural Computation, 34(9), 1841–1870
  • Learning shifts the preferred theta phase of gamma oscillations in CA1
    Rayan, A., Donoso, J. R., Mendez-Couz, M., Dolón, L., Cheng, S., & Manahan-Vaughan, D.
    Hippocampus, 32(9), 695–704
  • Simulating Policy Changes in Prerequisite-Free Curricula: A Supervised Data-Driven Approach
    Baucks, F., & Wiskott, L.
    In Proceedings of the 15th International Conference on Educational Data Mining (pp. 470–476) International Educational Data Mining Society
    Presentation Video pdf
  • Transient beta modulates decision thresholds during human action-stopping
    Muralidharan, V., Aron, A. R., & Schmidt, R.
    NeuroImage, 254, 119145
  • The cerebellum contributes to context-effects during fear extinction learning: A 7T fMRI study
    Batsikadze, G., Diekmann, N., Ernst, T. M., Klein, M., Maderwald, S., Deuschl, C., et al.
    NeuroImage, 253, 119080
  • Adult‐Onset Neurodegeneration in Nucleotide Excision Repair Disorders (NERDND): Time to Move Beyond the Skin
    Cordts, I., Önder, D., Traschütz, A., Kobeleva, X., Karin, I., Minnerop, M., et al.
    Movement Disorders, 37(8), 1707–1718
  • Coherent Structural and Functional Network Changes after Thalamic Lesions in Essential Tremor
    Pohl, E. D. R., Upadhyay, N., Kobeleva, X., Purrer, V., Maurer, A., Keil, V. C., et al.
    Movement Disorders, 37(9), 1924–1929
  • Bridging the gap between single receptor type activity and whole-brain dynamics
    Jancke, D., Herlitze, S., Kringelbach, M. L., & Deco, G.
    The FEBS Journal, 289(8), 2067–2084
  • Subjective cognitive decline and stage 2 of Alzheimer disease in patients from memory centers
    Jessen, F., Wolfsgruber, S., Kleineindam, L., Spottke, A., Altenstein, S., Bartels, C., et al.
    Alzheimer’s & Dementia, 19(2), 487–497
  • Aberrant Phase Precession of Lateral Septal Cells in a Maternal Immune Activation Model of Schizophrenia Risk May Disrupt the Integration of Location with Reward
    Speers, L. J., Schmidt, R., & Bilkey, D. K.
    The Journal of Neuroscience, 42(20), 4187–4201
  • AIMHI: Protecting Sensitive Data through Federated Co-Training
    Abourayya, A., Kamp, M., Ayday, E., Kleesiek, J., Rao, K., Webb, G. I., & Rao, B.
    In FL-NeurIPS 2022)
  • Uncertainty in coupled models of cyber-physical systems
    Acosta, M., Hahner, S., Koziolek, A., Kühn, T., Mirandola, R., & Reussner, R. H.
    In Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, MODELS 2022, Montreal, Quebec, Canada, October 23-28, 2022 (pp. 569–578) ACM
  • Global linear convergence of evolution strategies on more than smooth strongly convex functions
    Akimoto, Y., Auger, A., Glasmachers, T., & Morinaga, D.
    SIAM Journal on Optimization, 32(2), 1402–1429
    arXiv version
  • Recipe for Fast Large-scale SVM Training: Polishing, Parallelism, and more RAM!
    Glasmachers, T.
    arXiv.org
    paper code
  • Convergence Analysis of the Hessian Estimation Evolution Strategy
    Glasmachers, T., & Krause, O.
    Evolutionary Computation Journal (ECJ), 30(1), 27–50
    arXiv
  • Bridging DFT and DNNs: A neural dynamic process model of scene representation, guided visual search and scene grammar in natural scenes
    Grieben, R., & Schöner, G.
    In J. Culbertson, Perfors, A., Rabagliati, H., & Ramenzoni, V. (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society
    preprint
  • Robust Query Processing for Linked Data Fragments
    Heling, L., & Acosta, M.
    Semantic Web
  • Federated SPARQL Query Processing over Heterogeneous Linked Data Fragments
    Heling, L., & Acosta, M.
    In WWW ′22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022 (pp. 1047–1057) ACM
    Federated SPARQL Query Processing over HeterogeneousLinked Data Fragments
  • Utility-aware Semantics for Alternative Service Expressions in Federated SPARQL Queries
    Heling, L., & Acosta, M.
    In 2022 IEEE International Conference on Web Services, ICWS IEEE
    pdf
  • A Perceptually Grounded Neural Dynamic Architecture Establishes Analogy Between Visual Object Pairs
    Hesse, M., Sabinasz, D., & Schöner, G.
    In J. Culbertson, Perfors, A., Rabagliati, H., & Ramenzoni, V. (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society
    A Perceptually Grounded Neural Dynamic Architecture Establishes Analogy Between Visual Object Pairs
  • Latent Representation Prediction Networks
    Hlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.
    International Journal of Pattern Recognition and Artificial Intelligence, 36(01), 2251002
  • Advancing brain network models to reconcile functional neuroimaging and clinical research
    Kobeleva, X., Varoquaux, G., Dagher, A., Adhikari, M. H., Grefkes, C., & Gilson, M.
    NeuroImage: Clinical, 36, 103262
  • Open-Source Skull Reconstruction with MONAI
    Li, J., Ferreira, A., Puladi, B., Alves, V., Kamp, M., Kim, M. -S., et al.
    arXiv preprint arXiv:2211.14051
  • Regret-based Federated Causal Discovery
    Mian, O., Kaltenpoth, D., & Kamp, M.
    In The KDD′22 Workshop on Causal Discovery (pp. 61–69) PMLR
  • Reduction of Variance-related Error through Ensembling: Deep Double Descent and Out-of-Distribution Generalization
    Rath-Manakidis, P., Hlynsson, H. D., & Wiskott, L.
    In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM, (pp. 31–40) SciTePress
    pdf
  • A Neural Dynamic Model Perceptually Grounds Nested Noun Phrases
    Sabinasz, D., & Schöner, G.
    In J. Culbertson, Perfors, A., Rabagliati, H., & Ramenzoni, V. (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society
    A Neural Dynamic Model Perceptually Grounds Nested Noun Phrases
  • Modular Networks Prevent Catastrophic Interference in Model-Based Multi-task Reinforcement Learning
    Schiewer, R., & Wiskott, L.
    In Machine Learning, Optimization, and Data Science (pp. 299–313) Springer International Publishing
  • AFRNN: Stable RNN with Top Down Feedback and Antisymmetry
    Schwabe, T., Glasmachers, T., & Acosta, M.
    In Proceedings of the 14th Asian Conference on Machine Learning (ACML). To Appear
    AFRNN: Stable RNN with Top Down Feedback and Antisymmetry
  • What Happened When? Cerebral Processing of Modified Structure and Content in Episodic Cueing
    Siestrup, S., Jainta, B., El-Sourani, N., Trempler, I., Wurm, M. F., Wolf, O. T., et al.
    Journal of Cognitive Neuroscience, 34(7), 1287–1305
  • Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification
    Stephan Johann Lehmler, T. G.
    arXiv.org
    link
  • When, Where and How does it fail? A Spatial-temporal Visual Analytics Approach for Interpretable Object Detection in Autonomous Driving
    Wang, J., Li, Y., Zhou, Z., Wang, C., Hou, Y., Zhang, L., et al.
    IEEE Transactions on Visualization and Computer Graphics
  • 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, 52(1), 508–521

2021

  • A multistage retrieval account of associative recognition ROC curves
    Hakobyan, O., & Cheng, S.
    Learning and Memory, 28(11), 400–404
  • Revealing the Relevant Spatiotemporal Scale Underlying Whole-Brain Dynamics
    Kobeleva, X., López-González, A., Kringelbach, M. L., & Deco, G.
    Frontiers in Neuroscience, 15
  • Improved Protein Function Prediction by Combining Clustering with Ensemble Classification
    Altartouri, H., & Glasmachers, T.
    Journal of Advances in Information Technology (JAIT)
  • Centering inclusivity in the design of online conferences—An OHBM–Open Science perspective
    Levitis, E., van Praag, C. D. G., Gau, R., Heunis, S., DuPre, E., Kiar, G., et al.
    GigaScience, 10(8)
  • Bilateral Intracranial Beta Activity During Forced and Spontaneous Movements in a 6-OHDA Hemi-PD Rat Model
    Mottaghi, S., Kohl, S., Biemann, D., Liebana, S., Crespo, R. E. M., Buchholz, O., et al.
    Frontiers in Neuroscience, 15
  • Hippocampal Sequencing Mechanisms Are Disrupted in a Maternal Immune Activation Model of Schizophrenia Risk
    Speers, L. J., Cheyne, K. R., Cavani, E., Hayward, T., Schmidt, R., & Bilkey, D. K.
    The Journal of Neuroscience, 41(32), 6954–6965
  • Brainhack: Developing a culture of open, inclusive, community-driven neuroscience
    Gau, R., Noble, S., Heuer, K., Bottenhorn, K. L., Bilgin, I. P., Yang, Y. -F., et al.
    Neuron, 109(11), 1769–1775
  • Brain activity is contingent on neuropsychological function in a functional magnetic resonance imaging study of verbal working memory in amyotrophic lateral sclerosis
    Kobeleva, X., Machts, J., Veit, M., Vielhaber, S., Petri, S., & Schoenfeld, M. A.
    European Journal of Neurology, 28(9), 3051–3060
  • 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, 24(6), 1279–1297
  • 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
  • Basal ganglia and cortical control of thalamic rebound spikes
    Nejad, M. M., Rotter, S., & Schmidt, R.
    European Journal of Neuroscience, 54(1), 4295–4313
    link
  • Basal ganglia and cortical control of thalamic rebound spikes
    Nejad, M. M., Rotter, S., & Schmidt, R.
    European Journal of Neuroscience, 54(1), 4295–4313
  • 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, 74(9), 1512–1524
  • How do neural processes give rise to cognition? Simultaneously predicting brain and behavior with a dynamic model of visual working memory.
    Buss, A. T., Magnotta, V. A., Penny, W., Schöner, G., Huppert, T. J., & Spencer, J. P.
    Psychological Review, 128(2), 362–395
    preprint
  • 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)
  • Artificial Neural Networks Implementation to Predict the Solution of Nonlinear ODE System for the Application to Turbulent Combustion Modeling
    Abourayya, A.
    Master’s thesis, TU Wien
    link
  • Dynamic Door Modeling for Monocular 3D Vehicle Detection
    Barowski, T., Brehme, A., Szczot, M., & Houben, S.
    In Proceedings of the IEEE Intelligent Vehicles Symposium (IV) (pp. 1359–1365)
    pdf
  • 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)
  • Clinically Applicable Quantitative Magnetic Resonance Morphologic Measurements of Grey Matter Changes in the Human Brain
    Fu, T., Kobeleva, X., Bronzlik, P., Nösel, P., Dadak, M., Lanfermann, H., et al.
    Brain Sciences, 11(1), 55
  • The (1+1)-ES Reliably Overcomes Saddle Points
    Glasmachers, T.
    arXiv.org
    link
  • 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 43rd Annual Conference of the Cognitive Science Society
    preprint
  • Reward prediction for representation learning and reward shaping
    Hlynsson, H. D., & Wiskott, L.
    arXiv
  • 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)
    Paper (pdf)
  • 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) (pp. 2972–2979)
    Paper
  • FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
    Li, X., Jiang, M., Zhang, X., Kamp, M., & Dou, Q.
    In International Conference on Learning Representations
  • 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
    Exploring Slow Feature Analysis for Extracting Generative Latent Factors
  • Non-local Optimization: Imposing Structure on Optimization Problems by Relaxation
    Müller, N., & Glasmachers, T.
    In Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA′21) Association for Computing Machinery
    link
  • Neuronal Sequences during Theta Rely on Behavior-Dependent Spatial Maps
    Parra-Barrero, E., Diba, K., & Cheng, S.
    eLife, 10, e70296
  • Relative flatness and generalization
    Petzka, H., Kamp, M., Adilova, L., Sminchisescu, C., & Boley, M.
    Advances in neural information processing systems, 34, 18420–18432
  • A Neural Dynamic Model of the Perceptual Grounding of Spatial and Movement Relations
    Richter, M., Lins, J., & Schöner, G.
    Cognitive Science, 45(10), e13045
    Paper (open publication) Link to Journal
  • Pay-as-you-go Population of an Automotive Signal Knowledge Graph
    Svetashova, Y., Heling, L., Schmid, S., & Acosta, M.
    In The Semantic Web - 18th International Conference, ESWC 2021, Virtual Event, June 6-10, 2021, Proceedings (Vol. 12731, pp. 717–735) Springer
  • Predicting Instance Type Assertions in Knowledge Graphs Using Stochastic Neural Networks
    Weller, T., & Acosta, M.
    In 30th ACM International Conference on Information and Knowledge Management (CIKM) ACM
  • Charaterizing RDF graphs through graph-based measures - framework and assessment
    Zloch, M., Maribel Acosta,, Daniel Hienert,, Stefan Conrad,, & Stefan Dietze,
    Semantic Web, 12(5), 789–812

2020

  • Improving sensory representations using episodic memory
    Görler, R., Wiskott, L., & Cheng, S.
    Hippocampus, 30(6), 638–656
    link
  • 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)
  • Occasion setters determine responses of putative DA neurons to discriminative stimuli
    Aquili, L., Bowman, E. M., & Schmidt, R.
    Neurobiology of Learning and Memory, 173, 107270
  • Globus pallidus dynamics reveal covert strategies for behavioral inhibition
    Gu, B. -M., Schmidt, R., & Berke, J. D.
    eLife, 9
  • Z2 vortices in the ground states of classical Kitaev-Heisenberg models
    Seabrook, E., Baez, M. L., & Reuther, J.
    Physical Review B, 101(17)
  • 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
    Elife, 9:e53552
  • Abundance Compensates Kinetics: Similar Effect of Dopamine Signals on D1 and D2 Receptor Populations
    Hunger, L., Kumar, A., & Schmidt, R.
    The Journal of Neuroscience, 40(14), 2868–2881
  • 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
    Motor Habituation: Theory and Experiment (pdf)
  • 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
    arXiv
  • A Versatile Combination of Classifiers for Protein Function Prediction
    Altartouri, H., & Glasmachers, T.
    The Twelfth International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies
  • SMART-KG: Hybrid Shipping for SPARQL Querying on the Web
    Azzam, A., Javier D. Fernández,, Maribel Acosta,, Martin Beno,, & Axel Polleres,
    In WWW ′20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020 (pp. 984–994) ACM / IW3C2
  • An Infrastructure for Spatial Linking of Survey Data
    Bensmann, F., Lars Heling,, Stefan Jünger,, Loren Mucha,, Maribel Acosta,, Jan Goebel,, et al.
    Data Sci. J., 19, 27
  • Global Convergence of the (1+1) Evolution Strategy
    Glasmachers, T.
    Evolutionary Computation Journal (ECJ), 28(1), 27–53
    arXiv.org pdf ECJ website
  • The Hessian Estimation Evolution Strategy
    Glasmachers, T., & Krause, O.
    In Parallel Problem Solving from Nature (PPSN XVII) Springer
    paper code
  • 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
    SpringerLink (Open Access)
  • Estimating Characteristic Sets for RDF Dataset Profiles Based on Sampling
    Heling, L., & Maribel Acosta,
    In A. Harth, Sabrina Kirrane,, Axel-Cyrille Ngonga Ngomo,, Heiko Paulheim,, Anisa Rula,, Anna Lisa Gentile,, et al. (Eds.), The Semantic Web - 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31-June 4, 2020, Proceedings (Vol. 12123, pp. 157–175) Springer
  • Cost- and Robustness-Based Query Optimization for Linked Data Fragments
    Heling, L., & Maribel Acosta,
    In The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part I (Vol. 12506, pp. 238–257) Springer
  • Latent Representation Prediction Networks
    Hlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.
    arXiv preprint arXiv:2009.09439
    Latent Representation Prediction Networks
  • 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)
    Paper Poster
  • Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing
    Oller, D., Cuccu, G., & Glasmachers, T.
    In International Conference on Autonomous Agents and Multi-Agent Systems
    pdf
  • Singular Sturm-Liouville Problems with Zero Potential (q=0) and Singular Slow Feature Analysis
    Richthofer, S., & Wiskott, L.
    CoRR e-print arXiv:2011.04765
    Singular Sturm-Liouville Problems with Zero Potential (q=0) and Singular Slow Feature Analysis
  • 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
    pdf
  • 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)
    link pdf
  • Mining Latent Features of Knowledge Graphs for Predicting Missing Relations
    Weller, T., Tobias Dillig,, Maribel Acosta,, & York Sure-Vetter,
    In Knowledge Engineering and Knowledge Management - 22nd International Conference, EKAW 2020, Bolzano, Italy, September 16-20, 2020, Proceedings (Vol. 12387, pp. 158–170) Springer

2019

  • Improved graph-based SFA: information preservation complements the slowness principle
    Escalante-B., A. N., & Wiskott, L.
    Machine Learning
    link
  • 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
    link
  • Beta Oscillations in Working Memory, Executive Control of Movement and Thought, and Sensorimotor Function
    Schmidt, R., Ruiz, M. H., Kilavik, B. E., Lundqvist, M., Starr, P. A., & Aron, A. R.
    The Journal of Neuroscience, 39(42), 8231–8238
  • Computer mouse tracking reveals motor signatures in a cognitive task of spatial language grounding
    Lins, J., & Schöner, G.
    Attention, Perception, & Psychophysics
    pdf
  • 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)
    pdf
  • 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
  • Deep reinforcement learning in a spatial navigation task: Multiple contexts and their representation
    Diekmann, N., Walther, T., Vijayabaskaran, S., & Cheng, S.
    In 2019 Conference on Cognitive Computational Neuroscience Berlin, Germany: Cognitive Computational Neuroscience
  • Challenges of Convex Quadratic Bi-objective Benchmark Problems
    Glasmachers, T.
    In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 559–567) ACM
    arxiv code
  • 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
    link pdf
  • How do memory modules differentially contribute to familiarity and recollection?
    Hakobyan, O., & Cheng, S.
    Behavioral and Brain Sciences, 42, e288
  • Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences
    Heling, L., Felix Bensmann,, Benjamin Zapilko,, Maribel Acosta,, & York Sure-Vetter,
    In Joint Proceedings of the 1st International Workshop on Knowledge Graph Building and 1st International Workshop on Large Scale RDF Analytics co-located with 16th Extended Semantic Web Conference (ESWC 2019), Portorož, Slovenia, June 3, 2019 (Vol. 2489, pp. 1–12) CEUR-WS.org
  • Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences (Extended Version)
    Heling, L., Felix Bensmann,, Benjamin Zapilko,, Maribel Acosta,, & York Sure-Vetter,
    In The Semantic Web: ESWC 2019 Satellite Events - ESWC 2019 Satellite Events, Portorož, Slovenia, June 2-6, 2019, Revised Selected Papers (Vol. 11762, pp. 285–299) Springer
  • 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
    Measuring the Data Efficiency of Deep Learning Methods Conference poster
  • 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
    link link
  • Learning gradient-based ICA by neurally estimating mutual information
    Hlynsson, H. D., & Wiskott, L.
    arXiv, arXiv–1904.09858
    pdf link
  • Measuring the Data Efficiency of Deep Learning Methods
    Hlynsson, H. D., Wiskott, L., & others,
    arXiv, arXiv–1907
    pdf
  • Park marking-based vehicle self-localization with a fisheye topview system
    Houben, S., Neuhausen, M., Michael, M., Kesten, R., Mickler, F., & Schuller, F.
    Journal of Real-Time Image Processing, 16(2), 289–304
    pdf Springer Open Access
  • A process account of the UnControlled Manifold uncontrolled manifold structure of joint space variance in pointing movements
    Martin, V., Reimann, H., & Schöner, G.
    Biological Cybernetics
    reprint
  • A Hippocampus Model for Online One-Shot Storage of Pattern Sequences
    Melchior, J., Bayati, M., Azizi, A., Cheng, S., & Wiskott, L.
    CoRR e-print arXiv:1905.12937
  • Fusing Shape-from-Silhouette and the Sparsity Driven Detector for Camera-Based 3D Multi-Object Localization with Occlusions
    Michael, M., Horn, D., & Houben, S.
    In Proceedings of the IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 1417–1424)
    pdf
  • Vehicle Shape and Color Classification Using Convolutional NeuralNetwork
    Nafzi, M., Brauckmann, M., & Glasmachers, T.
    arxiv.org
  • Dual SVM Training on a Budget
    Qaadan, S., Schüler, M., & Glasmachers, T.
    In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods SCITEPRESS - Science and Technology Publications
    link
  • Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations
    Reimann, D., Nidadavolu, K., ul Hassan, H., Vajragupta, N., Glasmachers, T., Junker, P., & Hartmaier, A.
    Frontiers in Materials, 6, 181
    link
  • SpikeDeeptector: A deep-learning based method for detection of neural spiking activity
    Saif-ur-Rehman, M., Lienkämper, R., Parpaley, Y., Wellmer, J., Liu, C., Lee, B., et al.
    Journal of Neural Engineering
    link
  • The Dynamics of Neural Populations Capture the Laws of the Mind
    Schöner, G.
    Topics in Cognitive Science, 1–15
    reprint
  • Generation of Natural Traffic Sign Images Using Domain Translation with Cycle-Consistent Generative Adversarial Networks
    Spata, D., Horn, D., & Houben, S.
    In Proceedings of the IEEE Intelligent Vehicles Symposium (IV) (pp. 622–628)
    Source Code Paper Poster
  • Autonomous Sequence Generation for a Neural Dynamic Robot: Scene Perception, Serial Order, and Object-Oriented Movement
    Tekülve, J., Fois, A., Sandamirskaya, Y., & Schöner, G.
    Frontiers in Neurorobotics, 13, 95
    Article
  • Neural dynamic concepts for intentional systems
    Tekülve, J., & Schöner, G.
    In 41th Annual Conference of the Cognitive Science Society (CogSci 2019)
    Neural dynamic concepts for intentional systems (Preprint)
  • Autonomously learning beliefs is facilitated by a neural dynamic network driving an intentional agent
    Tekülve, J., & Schöner, G.
    In Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 2019 Joint IEEE International Conference on (pp. 143–150) IEEE
    pdf cedar architecture files
  • Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
    Schüler, M., Hlynsson, H. D. ′ið, & Wiskott, L.
    In W. S. Lee & Suzuki, T. (Eds.), Proceedings of The Eleventh Asian Conference on Machine Learning (Vol. 101, pp. 316–331) Nagoya, Japan: PMLR
    link

2018

  • A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems
    Menezes, M. C., de Freitas, E. P., Cheng, S., de Oliveira, A. C. M., & de Almeida Ribeiro, P. R.
    In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) IEEE
  • Autonomous Exploration Guided by Optimisation Metaheuristic
    Santos, R. G., de Freitas, E. P., Cheng, S., de Almeida Ribeiro, P. R., & de Oliveira, A. C. M.
    In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) IEEE
  • 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
  • Utilizing Slow Feature Analysis for Lipreading
    Freiwald, J., Karbasi, M., Zeiler, S., Melchior, J., Kompella, V., Wiskott, L., & Kolossa, D.
    In Speech Communication; 13th ITG-Symposium (pp. 191–195) VDE Verlag GmbH
  • A neural dynamic model for the perceptual grounding of spatial and movement relations
    Richter, M.
    Doctoral thesis, Ruhr-Universität Bochum, Universitätsstr. 150, 44801 Bochum
    pdf
  • 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
    link
  • The Interaction between Semantic Representation and Episodic Memory
    Fang, J., Rüther, N., Bellebaum, C., Wiskott, L., & Cheng, S.
    Neural Computation, 30(2), 293–332
  • Query Processing over Graph-structured Data on the Web
    Acosta, M.
    Doctoral thesis, Karlsruhe Institute of Technology, Germany
    pdf
  • 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
    link 1802.03209.pdf
  • Fine-Grained Vehicle Representations for Autonomous Driving
    Barowski, T., Szczot, M., & Houben, S.
    In IEEE International Conference on Intelligent Transportation Systems (ITSC) (pp. 3797–3804)
  • 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)
    pdf
  • Unveiling CO adsorption on Cu surfaces: new insights from molecular orbital principles
    Gameel, K. M., Sharafeldin, I. M., Abourayya, A. U., Biby, A. H., & Allam, N. K.
    Physical Chemistry Chemical Physics, 20(40), 25892–25900
  • 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
    link
  • 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
    preprint
  • 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
    reprint
  • 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)
    Paper Poster Project Page
  • 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
  • Optical Imaging With Voltage Sensors—Capturing TMS-Induced Neuronal Signals Using Light
    Jancke, D.
    In Handbook of Behavioral Neuroscience (pp. 223–234) Elsevier
  • 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)
    preprint
  • Large Scale Black-box Optimization by Limited-Memory Matrix Adaptation
    Loshchilov, I., Glasmachers, T., & Beyer, H. -G.
    IEEE Transactions on Evolutionary Computation, 99
    link link
  • Challenges in High-dimensional Controller Design with Evolution Strategies
    Müller, N., & Glasmachers, T.
    In Parallel Problem Solving from Nature (PPSN XVI) Springer
    arXiv software
  • 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.
    13th WiML Workshop, Co-located with NeurIPS, Montreal, QC, Canada
  • Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training
    Qaadan, S., & Glasmachers, T.
    arXiv.org
    link
  • Multi-Merge Budget Maintenance for Stochastic Coordinate Ascent SVM Training
    Qaadan, S., & Glasmachers, T.
    Artificial Intelligence International Conference – A2IC 2018
  • User-Centered Development of a Pedestrian Assistance System Using End-to-End Learning
    Qureshi, H. S., Glasmachers, T., & Wiczorek, R.
    In 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 808–813) IEEE
    link
  • Global Navigation Using Predictable and Slow Feature Analysis in Multiroom Environments, Path Planning and Other Control Tasks
    Richthofer, S., & Wiskott, L.
    CoRR e-print arXiv:1805.08565
  • Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
    Schüler, M., Hlynsson, H. D., & Wiskott, L.
    CoRR e-print arXiv:1808.08833
  • Unsupervised Acquisition of Human Body Models
    Walther, T., & Würtz, R. P.
    Cognitive Systems Research, 47, 68–84
    Article Additional material
  • 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, e34
  • 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

  • PFAx: Predictable Feature Analysis to Perform Control
    Richthofer, S., & Wiskott, L.
    CoRR e-print arXiv:1712.00634
    PFAx: Predictable Feature Analysis to Perform Control
  • 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
    reprint
  • Gaussian-binary restricted Boltzmann machines for modeling natural image statistics
    Melchior, J., Wang, N., & Wiskott, L.
    PLOS ONE, 12(2), 1–24
  • Theoretical and DFT Analysis of the CO Adsorption Mechanism Late Transition Metal Surfaces
    Abourayya, A., Gameel, K. M., Sharafeldin, I. M., Biby, A. H., & Allam, N. K.
    NanoWorld Conference, Bosten, USA
  • 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
  • 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
    PhD Thesis
  • A Fast Incremental BSP Tree Archive for Non-dominated Points
    Glasmachers, T.
    In Evolutionary Multi-Criterion Optimization (EMO) Springer
    pdf code
  • Limits of End-to-End Learning
    Glasmachers, T.
    In Proceedings of the 9th Asian Conference on Machine Learning (ACML)
    pdf code proceedings
  • 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)
    pdf
  • 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
    pdf
  • 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
    pdf
  • 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
    reprint
  • 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
    preprint
  • A neural dynamic model generates descriptions of object-oriented actions
    Richter, M., Lins, J., & Schöner, G.
    Topics in Cognitive Science, 9(1), 35–47
    A neural dynamic model generates descriptions of object-oriented actions (PDF)
  • 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
    preprint
  • Dynamic Neural Fields with Intrinsic Plasticity
    Strub, C., Schöner, G., Wörgötter, F., & Sandamirskaya, Y.
    Frontiers in Computational Neuroscience, 11(August), 74
    reprint
  • 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)
    Paper Poster Project Page
  • Graph-based predictable feature analysis
    Weghenkel, B., Fischer, A., & Wiskott, L.
    Machine Learning, 1–22
    preprint
  • Taxonomy and Unity of Memory
    Werning, M., & Cheng, S.
    In S. Bernecker & Michaelian, K. (Eds.), The Routledge Handbook of Philosophy of Memory (pp. 7–20) New York: Routledge

2016

  • Melanopsin Variants as Intrinsic Optogenetic On and Off Switches for Transient versus Sustained Activation of G Protein Pathways
    Spoida, K., Eickelbeck, D., Karapinar, R., Eckhardt, T., Mark, M. D., Jancke, D., et al.
    Current Biology, 26(9), 1206–1212
  • Temporal Asymmetry in Dark–Bright Processing Initiates Propagating Activity across Primary Visual Cortex
    Rekauzke, S., Nortmann, N., Staadt, R., Hock, H. S., Schöner, G., & Jancke, D.
    The Journal of Neuroscience, 36(6), 1902–1913
    pdf
  • Graph-based Predictable Feature Analysis
    Weghenkel, B., Fischer, A., & Wiskott, L.
    e-print arXiv:1602.00554v1
  • 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
    pdf 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
    pdf jmlr.org
  • 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
    Theoretical analysis of the optimal free responses of graph-based SFA for the design of training graphs
  • Improved graph-based SFA: Information preservation complements the slowness principle
    Escalante-B., A. N., & Wiskott, L.
    e-print arXiv:1601.03945
    Improved graph-based SFA: Information preservation complements the slowness principle
  • 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 Training of Kernel Machines on a Budget
    Glasmachers, T.
    In NIPS workshop on Optimization for Machine Learning
    pdf code
  • Small Stochastic Average Gradient Steps
    Glasmachers, T.
    In NIPS workshop on Optimizing the Optimizers
    pdf
  • Nonlinear dynamics in the perceptual grouping of connected surfaces
    Hock, H. S., & Schöner, G.
    Vision Research, 126, 80–96
    reprint
  • A Comparative Study on Large Scale Kernelized Support Vector Machines
    Horn, D., Demircioğlu, A., Bischl, B., Glasmachers, T., & Weihs, C.
    Advances in Data Analysis and Classification (ADAC), 1–17
    springer.com
  • Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite
    Krause, O., Glasmachers, T., Hansen, N., & Igel, C.
    In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)
    pdf
  • Multi-objective Optimization with Unbounded Solution Sets
    Krause, O., Glasmachers, T., & Igel, C.
    In NIPS workshop on Bayesian Optimization
    pdf
  • Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar
    Lomp, O., Richter, M., Zibner, S. K. U., & Schöner, G.
    Frontiers in Neurorobotics, 10(November), 14
    reprint
  • Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES (HMO-CMA-ES)
    Loshchilov, I., & Glasmachers, T.
    In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)
    pdf
  • How to Center Deep Boltzmann Machines
    Melchior, J., Fischer, A., & Wiskott, L.
    Journal of Machine Learning Research, 17(99), 1–61
    Journal article page
  • Fast Change Detection for Camera-based Surveillance Systems
    Michael, M., Feist, C., Schuller, F., & Tschentscher, M.
    In Proceedings of the IEEE International Conference on Intelligent Transportation Systems (Vol. 19, pp. 1–8)
    Fast Change Detection for Camera-based Surveillance Systems
  • Coordination of muscle torques stabilizes upright standing posture: an UCM analysis
    Park, E., Reimann, H., & Schöner, G.
    Experimental Brain Research, 234(6), 1757–1767
    preprint
  • Separating Timing, Movement Conditions and Individual Differences in the Analysis of Human Movement
    Raket, L. L., Grimme, B., Schöner, G., Igel, C., & Markussen, B.
    PLoS Computational Biology, 12(9), 1–27
    reprint
  • A neural dynamic model parses object-oriented actions
    Richter, M., Lins, J., & Schöner, G.
    In A. Papafragou, Grodner, D., Mirman, D., & Trueswell, J. C. (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 1931–1936) Austin, TX: Cognitive Science Society
    pdf
  • A computational model of spatial encoding in the hippocampus
    Schönfeld, F.
    Doctoral thesis, Ruhr-Universität Bochum
  • A neural process model of learning to sequentially organize and activate pre-reaches
    Tekülve, J., Zibner, S. K. U., & Schöner, G.
    In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2016 Joint IEEE International Conferences on
    A neural process model of learning to sequentially organize and activate pre-reaches
  • "Self-Improving System Integration" – Preface for the SISSY′16 Workshop
    Tomforde, S., Rudolph, S., Bellman, K., & Würtz, R. P.
    In Proc. ICAC (p. 275)
    pdf
  • An Organic Computing Perspective on Self-Improving System Interweaving at Runtime
    Tomforde, S., Rudolph, S., Bellman, K., & Würtz, R. P.
    In Proc. ICAC (pp. 276–284)
    pdf
  • Supervised Classification
    Weihs, C., & Glasmachers, T.
    In C. Weihs, Jannach, D., Vatolkin, I., & Rudolph, G. (Eds.), Music Data Analysis: Foundations and Applications

2015

  • Phase based detection of JPEG counter forensics
    Fahmy, G., Alqallaf, A., & Wurtz, R.
    In 2015 IEEE International Conference on Electronics, Circuits, and Systems (ICECS) (pp. 37–40)
    pdf
  • Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input Statistics
    Neher, T., Cheng, S., & Wiskott, L.
    PLOS Computational Biology, 11(5), 1–25
  • Using SFA and PFA to solve navigation tasks in multi room environments
    Richthofer, S.
    Weekly Seminar talk, Institut für Neuroinformatik, Ruhr-Universität Bochum, Apr 1st, 2015, Bochum, Germany
    Talk slides: Using SFA and PFA to solve navigation tasks in multi room environments
  • Visual homeostatic processing in V1: when probability meets dynamics
    Nortmann, N., Rekauzke, S., Azimi, Z., Onat, S., König, P., & Jancke, D.
    Frontiers in Systems Neuroscience, 9
  • Self-organization of synchronous activity propagation in neuronal networks driven by local excitation
    Bayati, M., Valizadeh, A., Abbassian, A., & Cheng, S.
    Frontiers in Computational Neuroscience, 9, 69
  • A smartphone-controlled autonomous robot
    Bodenstein, C., Tremer, M., Overhoff, J., & Würtz, R. P.
    In Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery, Zhangjiajie, China, Aug. 15-17 (pp. 2360–2367)
    pdf
  • Evoking plasticity through sensory stimulation: Implications for learning and rehabilitation
    Dinse, H. R., & Tegenthoff, M.
    e-Neuroforum, 1(1),
    pdf
  • Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs
    Escalante-B., A. N., & Wiskott, L.
    e-print arXiv:1509.08329 (Accepted in Journal of Machine Learning Research)
  • Reconstruction of Images from Gabor Graphs with Applications in Facial Image Processing
    Günther, M., Böhringer, S., Wieczorek, D., & Würtz, R. P.
    International Journal of Wavelets, Multiresolution and Information Processing, 13(4), 1550019-1–25
    pdf
  • Resting BOLD fluctuations in the primary somatosensory cortex correlate with tactile acuity
    Haag, L. M., Heba, S., Lenz, M., Glaubitz, B., Höffken, O., Kalisch, T., et al.
    Cortex, 64, 20–28
    pdf
  • Carry-over coarticulation in joint angles
    Hansen, E., Grimme, B., Reimann, H., & Schöner, G.
    Experimental Brain Research, 233(9), 2555–2569
    reprint
  • Video-based Parking Space Detection: Localisation of Vehicles and Development of an Infrastructure for a Routeing System
    Horn, D., & Brüggenthies, M.
    In Proceedings of the Forum Bauinformatik (pp. 175–182)
    Paper
  • Topview stereo: combining vehicle-mounted wide-angle cameras to a distance sensor array
    Houben, S.
    In Video Surveillance and Transportation Imaging Applications International Society for Optics and Photonics
    pdf
  • Arbitrary object localization and tracking via multiple-camera surveillance system embedded in a parking garage
    Ibisch, A., Houben, S., Michael, M., Kesten, R., & Schuller, F.
    In SPIE/IS&T Electronic Imaging International Society for Optics and Photonics
  • Continual curiosity-driven skill acquisition from high-dimensional video inputs for humanoid robots
    Kompella, V. R., Stollenga, M., Luciw, M., & Schmidhuber, J.
    Artificial Intelligence
  • Automatic face classification in Cushing′s syndrome and acromegaly: Review, current results and future perspectives
    Kosilek, R. P., Frohner, R., Würtz, R. P., Berr, C. M., Schopohl, J., Reincke, M., & Schneider, H. J.
    European Journal of Endocrinology, 173(4), M39–M44
  • A CMA-ES with Multiplicative Covariance Matrix Updates
    Krause, O., & Glasmachers, T.
    In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)
    pdf code
  • Parsing of action sequences: A neural dynamics approach
    Lobato, D., Sandamirskaya, Y., Richter, M., & Schöner, G.
    Paladyn, Journal of Behavioral Robotics, 6(1), 119–135
    reprint
  • Learning the condition of satisfaction of an elementary behavior in dynamic field theory
    Luciw, M., Kazerounian, S., Lahkman, K., Richter, M., & Sandamirskaya, Y.
    Paladyn, Journal of Behavioral Robotics, 6(1), 180–190
    pdf
  • Task-specific stability of abundant systems: Structure of variance and motor equivalence
    Mattos, D., Schöner, G., Zatsiorsky, V. M., & Latash, M. L.
    Neuroscience, 310, 600–615
    reprint
  • Motor equivalence during multi-finger accurate force production
    Mattos, D., Schöner, G., Zatsiorsky, V. M., & Latash, M. L.
    Experimental Brain Research, 233, 487–502
    pdf
  • Extending Traffic Light Recognition: Efficient Classification of Phase and Pictogram
    Michael, M., & Schlipsing, M.
    In Proceedings of the IEEE International Joint Conference on Neural Networks
    Paper
  • Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input Statistics
    Neher, T., Cheng, S., & Wiskott, L.
    PLoS Computational Biology, 11(5), e1004250
    Journal's article page
  • The Dynamics of Neural Activation Variables
    Reimann, H., Lins, J., & Schöner, G.
    Paladyn, Journal of Behavioral Robotics, 6(1), 57–70
    pdf
  • Predictable Feature Analysis
    Richthofer, S., & Wiskott, L.
    In 14th IEEE International Conference on Machine Learning and Applications, ICMLA 2015, Miami, FL, USA, December 9-11, 2015 (pp. 190–196)
    Poster - Predictable Feature Analysis - 2015
  • Predictable Feature Analysis
    Richthofer, S., & Wiskott, L.
    In Workshop New Challenges in Neural Computation 2015 (NC2) (pp. 68–75)
  • Predictable Feature Analysis.
    Richthofer, S., & Wiskott, L.
    In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (pp. 190–196)
  • State-dependencies of learning across brain scales
    Ritter, P., Born, J., Brecht, M., Dinse, H. R., Heinemann, U., Pleger, B., et al.
    Frontiers in Computational Neuroscience, 9
    pdf
  • Artificial Neural Networks — Methods and Applications in Bio-/Neuroinformatics
    Sandamirskaya, Y., & Storck, T.
    In P. Koprinkova-Hristova, Mladenov, V., & Kasabov, N. K. (Eds.) (Vol. 4) Springer
    pdf
  • Modeling place field activity with hierarchical slow feature analysis
    Schoenfeld, F., & Wiskott, L.
    Frontiers in Computational Neuroscience, 9(51)
    Journal's article page
  • Modeling place field activity with hierarchical slow feature analysis
    Schönfeld, F., & Wiskott, L.
    frontiers in Computational Neuroscience, 9(51)
  • Scalable real-time parking lot classification: An evaluation of image features and supervised learning algorithms
    Tschentscher, M., Koch, C., König, M., Salmen, J., & Schlipsing, M.
    In Proceedings of the IEEE International Joint Conference on Neural Networks
    Paper Poster
  • FELICITY: A flexible video similarity search framework using the earth mover’s distance
    Uysal, M. S., Beecks, C., Sabinasz, D., & Seidl, T.
    In International Conference on Similarity Search and Applications (pp. 347–350) Springer
  • The Neural Dynamics of Goal-Directed Arm Movements: A Developmental Perspective
    Zibner, S. K. U., Tekülve, J., & Schöner, G.
    In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2015 Joint IEEE International Conferences on (pp. 154–161)
    model simulation pdf
  • The Sequential Organization of Movement is Critical to the Development of Reaching: A Neural Dynamics Account
    Zibner, S. K. U., Tekülve, J., & Schöner, G.
    In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2015 Joint IEEE International Conferences on (pp. 39–46)
    pdf

2014

  • Modeling the Dynamics of Disease States in Depression
    Demic, S., & Cheng, S.
    PLOS ONE, 9(10), 1–14
  • Voltage-sensitive dye imaging of transcranial magnetic stimulation-induced intracortical dynamics
    Kozyrev, V., Eysel, U. T., & Jancke, D.
    Proceedings of the National Academy of Sciences, 111(37), 13553–13558
  • Touch improvement at the hand transfers to the face
    Muret, D., Dinse, H. R., Macchione, S., Urquizar, C., Farne, A., & Reilly, K. T.
    Curr. Biol., 24(16), R736–737
    pdf
  • The transformation from grid cells to place cells is robust to noise in the grid pattern
    Azizi, A. H., Schieferstein, N., & Cheng, S.
    Hippocampus, 24(8), 912–919
  • Classification and visualization based on derived image features: application to genetic syndromes
    Balliu, B., Würtz, R. P., Horsthemke, B., Wieczorek, D., & Böhringer, S.
    PLOS One, 9(11), e109033
    pdf
  • Learning to Look: a Dynamic Neural Fields Architecture for Gaze Shift Generation
    Bell, C., Storck, T., & Sandamirskaya, Y.
    In International Conference for Artificial Neural Networks, ICANN Hamburg, Germany
    pdf
  • "Self-Improving System Integration" – Preface for the SISSY14 Workshop
    Bellman, K., Tomforde, S., & Würtz, R. P.
    In Proc. SASO, London (p. 122) IEEE
    pdf
  • Interwoven Systems: Self-improving Systems Integration
    Bellman, K., Tomforde, S., & Würtz, R. P.
    In Proc. SASO, London (pp. 123–127) IEEE
    pdf
  • Tactile Acuity Charts: A Reliable Measure of Spatial Acuity
    Bruns, P., Camargo, C. J., Campanella, H., Esteve, J., Dinse, H. R., & Röder, B.
    PloS one, 9(2), e87384
    pdf
  • Optogenetic Assessment of Horizontal Interactions in Primary Visual Cortex (pg 4976, 2014)
    Chavane, F., Sharon, D., Jancke, D., Marre, O., Fregnac, Y., & Grinvald, A.
    J Neurosci, 34(26), 8930–8930
  • Slow Feature Analysis on Retinal Waves Leads to V1 Complex Cells.
    Dähne, S., Wilbert, N., & Wiskott, L.
    PLoS Comput Biol, 10(5), e1003564
  • Testing Hypotheses by Regularized Maximum Mean Discrepancy
    Danafar, S., Rancoita, P. M. V., Glasmachers, T., Whittingstall, K., & Schmidhuber, J.
    International Journal of Computer and Information Technology (IJCIT), 3(2)
    pdf arXiv.org
  • Handling Sharp Ridges with Local Supremum Transformations
    Glasmachers, T.
    In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)
    pdf
  • Optimized Approximation Sets for Low-dimensional Benchmark Pareto Fronts
    Glasmachers, T.
    In Parallel Problem Solving from Nature (PPSN) Springer
    pdf code
  • Start Small, Grow Big - Saving Multiobjective Function Evaluations
    Glasmachers, T., Naujoks, B., & Rudolph, G.
    In Parallel Problem Solving from Nature (PPSN) Springer
    pdf
  • A comparison between reactive potential fields and Attractor Dynamics
    Hernandes, A. C., Guerrero, H. B., Becker, M., Jokeit, J. -S., & Schöner, G.
    Circuits and Systems (CWCAS), 2014 IEEE 5th Colombian Workshop on
    reprint
  • Towards the intrinsic self-calibration of a vehicle-mounted omni-directional radially symmetric camera
    Houben, S.
    In Proceedings of the IEEE Intelligent Vehicles Symposium Proceedings (pp. 878–883)
    pdf
  • Towards highly automated driving in a parking garage: General object localization and tracking using an environment-embedded camera system
    Ibisch, A., Houben, S., Schlipsing, M., Kesten, R., Reimche, P., Schuller, F., & Altinger, H.
    In Proceedings of the IEEE Intelligent Vehicles Symposium Proceedings (pp. 426–431)
  • Tanz im Alter: Fitness für Gehirn, Geist und Körper
    Kattenstroth, J. C., Kalisch, T., Tegenthoff, M., & Dinse, H. R.
    In C. Behrens & Rosenberg, C. (Eds.), TanzZeit – LebensZeit, Tanzforschung 2014 (pp. 115–135) Leipzig: Henschel Verlag
  • A neural dynamics architecture for grasping that integrates perception and movement generation and enables on-line updating
    Knips, G., Zibner, S. K. U., Reimann, H., Popova, I., & Schöner, G.
    In International Conference on Intelligent Robots and Systems (IROS) (pp. 646–653)
  • Reaching and grasping novel objects: Using neural dynamics to integrate and organize scene and object perception with movement generation
    Knips, G., Zibner, S. K. U., Reimann, H., Popova, I., & Schöner, G.
    In International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EPIROB) (pp. 416–423)
  • Slow Feature Analysis for Curiosity-Driven Agents, 2014 IEEE WCCI Tutorial
  • Slowness Learning for Curiosity-Driven Agents
    Kompella, V. R.
    Doctoral thesis, Università della svizzera italiana (USI)
  • An Anti-hebbian Learning Rule to Represent Drive Motivations for Reinforcement Learning
    Kompella, V. R., Kazerounian, S., & Schmidhuber, J.
    In From Animals to Animats 13 (pp. 176–187) Springer International Publishing
  • Explore to See, Learn to Perceive, Get the Actions for Free: SKILLABILITY
    Kompella, V. R., Stollenga, M. F., Luciw, M. D., & Schmidhuber, J.
    In Proceedings of IEEE Joint Conference of Neural Networks (IJCNN)
  • Spiking network simulations
    Krause, T. U., Schrör, P. Y., & Würtz, R. P.
    In B. Hammer, Martinetz, T., & Villmann, T. (Eds.), Proceedings of New Challenges in Neural Computation, Münster (pp. 14–15)
    pdf
  • Effects of combining 2 weeks of passive sensory stimulation with active hand motor training in healthy adults
    Ladda, A. M., Pfannmoeller, J. P., Kalisch, T., Roschka, S., Platz, T., Dinse, H. R., & Lotze, M.
    PloS one, 9(1), e84402
    pdf
  • Learning of invariant object recognition from temporal correlation in a hierarchical network
    Lessmann, M., & Würtz, R. P.
    Neural Networks, 54, 70–84
    pdf
  • Online Learning of Invariant Object Recognition in a Hierarchical Neural Network
    Lessmann, M., & Würtz, R. P.
    In S. Wermter, Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., et al. (Eds.), Proc. ICANN (pp. 427–434) Springer
    pdf
  • Neural Fields
    Lins, J., & Schöner, G.
    In S. Coombes, beim Graben, P., Potthast, R., & , J. W. (Eds.) (pp. 319–339) Springer Berlin Heidelberg
    pdf
  • Opposing effects of dopamine antagonism in a motor sequence task—tiapride increases cortical excitability and impairs motor learning
    Lissek, S., Vallana, G. S., Schlaffke, L., Lenz, M., Dinse, H. R., & Tegenthoff, M.
    Frontiers in behavioral neuroscience, 8
    pdf
  • Instance-based object recognition with simultaneous pose estimation using keypoint maps and neural dynamics
    Lomp, O., Terzić, K., Faubel, C., du Buf, J. M. H., & Schöner, G.
    In International Conference on Artificial Neural Networks (pp. 451–458) Springer
  • Reinforcement-Driven Shaping of Sequence Learning in Neural Dynamics
    Luciw, M., Kazerounian, S., Sandamirskaya, Y., Schöner, G., & Schmidhuber, J.
    In Simulation of Adaptive Behavior, SAB
    pdf
  • Change occurs when body meets environment: A review of the embodied nature of development
    Maruyama, S., Dineva, E., Spencer, J. P., & Schöner, G.
    Japanese Psychological Research, 56, 385–401
    pdf
  • Contrasting accounts of direction and shape perception in short-range motion: Counterchange compared with motion energy detection.
    Norman, J., Hock, H., & Schoner, G.
    Attention, perception & psychophysics, 76, 1350–70
    pdf
  • A neural dynamics to organize timed movement : Demonstration in a robot ball bouncing task
    Oubbati, F., Richter, M., & Schöner, G.
    In 4th International Conference on Development and Learning and on Epigenetic Robotics (pp. 291–298) Palazzo Ducale, Genoa, Italy
    reprint
  • Pattern Association and Consolidation Emerges from Connectivity Properties between Cortex and Hippocampus
    Pyka, M., & Cheng, S.
    PLOS ONE, 9(1), 1–14
  • Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections
    Pyka, M., Klatt, S., & Cheng, S.
    Frontiers in Neuroanatomy, 8, 91
  • Autonomous Neural Dynamics to Test Hypotheses in a Model of Spatial Language
    Richter, M., Lins, J., Schneegans, S., Sandamirskaya, Y., & Schöner, G.
    In P. Bello, Guarini, M., McShane, M., & Scassellati, B. (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 2847–2852) Austin, TX: Cognitive Science Society
    pdf
  • A neural dynamic architecture resolves phrases about spatial relations in visual scenes
    Richter, M., Lins, J., Schneegans, S., & Schöner, G.
    In 24th International Conference on Artificial Neural Networks (ICANN) (pp. 201–208) Heidelberg, Germany: Springer
    pdf
  • Neural-Dynamic Architecture for Looking: Shift from Visual to Motor Target Representation for Memory Saccade
    Sandamirskaya, Y., & Storck, T.
    In IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL EPIROB 2014)
    pdf
  • Videobasierte Leistungserfassung im Fußball
    Schlipsing, M.
    Doctoral thesis, Ruhr-Universität Bochum
    Kurzfassung der Dissertation
  • Adaptive pattern recognition in real-time video-based soccer analysis
    Schlipsing, M., Salmen, J., Tschentscher, M., & Igel, C.
    Journal of Real-Time Image Processing, 1–17
    Paper
  • Dynamic interactions between visual working memory and saccade target selection
    Schneegans, S., Spencer, J. P., Schöner, G., Hwang, S., & Hollingworth, A.
    Journal of vision, 14(11), 9
    supplementary material pdf
  • Use of the Uncontrolled Manifold (UCM) Approach to Understand MotorVariability, Motor Equivalence, and Self-motion
    Scholz, J. P., & Schöner, G.
    In M. F. Levin (Ed.), Progress in Motor Control (Vol. 826, p. Chapter 7) Springer International Publishing
    preprint
  • Embodied Cognition, Neural Field Models of
    Schöner, G.
    In Encyclopedia of Computational Neuroscience (pp. 1084–1092) Springer Berlin Heidelberg
    preprint
  • Dynamical Systems Thinking: From Metaphor to Neural Theory
    Schöner, G.
    In P. C. M. Molenaar, Lerner, R. M., & Newell, K. M. (Eds.), Handbook of Developmental Systems Theory and Methodology (pp. 188–219) New York, New York, USA: Guilford Publications
    preprint
  • Coordination Dynamics
    Schöner, G., & Nowak, E.
    In D. Jaeger & Jung, R. (Eds.), Encyclopedia of Computational Neuroscience (pp. 1–3) New York, NY: Springer New York
    preprint
  • Synergistic effects of noradrenergic modulation with atomoxetine and 10 Hz repetitive transcranial magnetic stimulation on motor learning in healthy humans
    Sczesny-Kaiser, M., Bauknecht, A., Höffken, O., Tegenthoff, M., Dinse, H. R., Jancke, D., et al.
    BMC neuroscience, 15(1), 46
  • Synergistic effects of noradrenergic modulation with atomoxetine and 10 Hz repetitive transcranial magnetic stimulation on motor learning in healthy humans
    Sczesny-Kaiser, M., Bauknecht, A., Hoffken, O., Tegenthoff, M., Dinse, H. R., Jancke, D., et al.
    BMC Neurosci, 15, 46
    pdf
  • The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models
    Sigala, R., Haufe, S., Roy, D., Dinse, H. R., & Ritter, P.
    Front Comput Neurosci, 8, 36
    pdf
  • An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation
    Sprekeler, H., Zito, T., & Wiskott, L.
    Journal of Machine Learning Research, 15, 921–947
  • Correcting Pose Estimates during Tactile Exploration of Object Shape: a Neuro-robotic Study
    Strub, C., Wörgötter, F., Ritter, H., & Sandamirskaya, Y.
    In Development and Learning and Epirobotics (ICDL-Epirob), IEEE International Conference on
    pdf
  • Using Haptics to Extract Object Shape from Rotational Manipulations
    Strub, C., Wörgötter, F., Ritter, H., & Sandamirskaya, Y.
    In Intelligent Robots and Systems (IROS), IEEE/RSJ International Conference on IEEE
    pdf
  • Learning natural image statistics with variants of restricted Boltzmann machines
    Wang, N.
    Doctoral thesis, International Graduate School of Neuroscience, Ruhr-Universität Bochum
  • Modeling correlations in spontaneous activity of visual cortex with Gaussian-binary deep Boltzmann machines
    Wang, N., Jancke, D., & Wiskott, L.
    In Proc. Bernstein Conference for Computational Neuroscience, Sep 3–5,Göttingen, Germany (pp. 263–264) BFNT Göttingen
  • Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines
    Wang, N., Jancke, D., & Wiskott, L.
    In Proc. International Conference of Learning Representations (ICLR′14, workshop), Apr 14–16,Banff, Alberta, Canada
  • Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image statistics
    Wang, N., Melchior, J., & Wiskott, L.
    (Vol. 1401.5900) arXiv.org e-Print archive
    Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image statistics
  • Learning predictive partitions for continuous feature spaces
    Weghenkel, B., & Wiskott, L.
    In Proc. 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 23-25, Bruges, Belgium (pp. 577–582)
    pdf
  • Is Episodic Memory a Natural Kind?-A Defense of the Sequence Analysis
    Werning, M., & Cheng, S.
    In P. Bello, Guarini, M., McShane, M., & Scassellati, B. (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (Vol. 2, pp. 964–69) Austin, TX: Cognitive Science Society
    Is Episodic Memory a Natural Kind? - A Defense of the Sequence Analysis
  • Natural Evolution Strategies
    Wierstra, D., Schaul, T., Glasmachers, T., Sun, Y., Peters, J., & Schmidhuber, J.
    Journal of Machine Learning Research, 15, 949–980
    pdf jmlr.org
  • Elastic Bunch Graph Matching
    Wiskott, L., Würtz, R. P., & Westphal, G.
    Scholarpedia, 9, 10587
    http://www.scholarpedia.org/article/Elastic_Bunch_Graph_Matching
  • Spatial representations of place cells in darkness are supported by path integration and border information
    Zhang, S., Schoenfeld, F., Wiskott, L., & Manahan-Vaughan, D.
    Frontiers in Behavioral Neuroscience, 8(222)
    Spatial representations of place cells in darkness are supported by path integration and border information

2013

  • Primary Visual Cortex Represents the Difference Between Past and Present
    Nortmann, N., Rekauzke, S., Onat, S., König, P., & Jancke, D.
    Cerebral Cortex, 25(6), 1427–1440
  • Composition and replay of mnemonic sequences: The contributions of REM and slow-wave sleep to episodic memory
    Cheng, S., & Werning, M.
    Behavioral and Brain Sciences, 36(06), 610–611
  • Quantitative assessment of joint position sense recovery in subacute stroke patients: a pilot study
    Kattenstroth, J. C., Kalisch, T., Kowalewski, R., Tegenthoff, M., & Dinse, H. R.
    J Rehabil Med, 45(10), 1004–1009
    pdf
  • Effects of aging on properties of the local circuit in rat primary somatosensory cortex (S1) in vitro
    Hickmott, P., & Dinse, H.
    Cereb. Cortex, 23(10), 2500–2513
    pdf
  • Effects of aging on properties of the local circuit in rat primary somatosensory cortex (S1) in vitro
    Hickmott, P., & Dinse, H.
    Cereb. Cortex, 23(10), 2500–2513
  • Autonomous robot hitting task using dynamical system approach
    Oubbati, F., Richter, M., & Schöner, G.
    In IEEE International Conference on Systems, Man, and Cybernetics (pp. 4042–4047) IEEE
    pdf
  • Cortical long-range interactions embed statistical knowledge of natural sensory input: a voltage-sensitive dye imaging study
    Onat, S., Jancke, D., & König, P.
    F1000Research, 2, 51
  • Learning without training
    Beste, C., & Dinse, H. R.
    Curr. Biol., 23(11), R489–499
    pdf
  • Influence of stimulation intensity on paired-pulse suppression of human median nerve somatosensory evoked potentials
    Gatica Tossi, M. A., Lillemeier, A. S., & Dinse, H. R.
    Neuroreport, 24(9), 451–456
    pdf
  • Phosphene thresholds correlate with paired-pulse suppression of visually evoked potentials
    Hoffken, O., Lenz, M., Sczesny-Kaiser, M., Dinse, H. R., & Tegenthoff, M.
    Brain Stimul, 6(2), 118–121
    pdf
  • State-dependent perceptual learning
    Freyer, F., Becker, R., Dinse, H. R., & Ritter, P.
    J. Neurosci., 33(7), 2900–2907
    pdf
  • A computational model for preplay in the hippocampus
    Azizi, A. H., Wiskott, L., & Cheng, S.
    Frontiers in Computational Neuroscience, 7, 161
  • Video-based trailer detection and articulation estimation
    Caup, L., Salmen, J., Muharemovic, I., & Houben, S.
    In Proceedings of the IEEE Intelligent Vehicles Symposium (IV) (pp. 1179–1184)
    pdf
  • The CRISP theory of hippocampal function in episodic memory
    Cheng, S.
    Frontiers in Neural Circuits, 7, 88
  • How to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis
    Escalante-B., A. -N., & Wiskott, L.
    Cognitive Sciences EPrint Archive (CogPrints)
  • How to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis
    Escalante-B., A. N., & Wiskott, L.
    Journal of Machine Learning Research, 14, 3683–3719
    lHow to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis
  • Optimierung der Gesichtsklassifikation bei der Erkennung von Akromegalie
    Frohner, R., Würtz, R. P., Kosilek, R. P., & Schneider, H. J.
    Austrian Journal of Clinical Endocrinology and Metabolism, 6(3), 20–24
    pdf
  • A Natural Evolution Strategy with Asynchronous Strategy Updates
    Glasmachers, T.
    In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)
    pdf code
  • The Planning-ahead SMO Algorithm
    Glasmachers, T.
    arxiv.org
    pdf
  • Accelerated Coordinate Descent with Adaptive Coordinate Frequencies
    Glasmachers, T., & Doğan, Ü.
    In Proceedings of the fifth Asian Conference on Machine Learning (ACML)
    pdf jmlr.org code (LASSO) code (LibLinear)
  • Identification of two forebrain structures that mediate execution of memorized sequences in the pigeon
    Helduser, S., Cheng, S., & Güntürkün, O.
    Journal of Neurophysiology, 109(4), 958–968
  • On-vehicle video-based parking lot recognition with fisheye optics
    Houben, S., Komar, M., Hohm, A., Luke, S., Neuhausen, M., & Schlipsing, M.
    In 16th International IEEE Conference on Intelligent Transportation Systems (ITSC) (pp. 7–12)
    pdf
  • Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark
    Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., & Igel, C.
    In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN) (pp. 1–8)
    pdf
  • Autonomous Driving in a Parking Garage: Vehicle Localization and Tracking Using Environment-embedded LIDAR Sensors
    Ibisch, A., Stümper, S., Altinger, H., Neuhausen, M., Tschentscher, M., Schlipsing, M., et al.
    In Proceedings of the IEEE Intelligent Vehicles Symposium (pp. 829–934)
    Paper
  • Six months of dance intervention enhances postural, sensorimotor, and cognitive performance in elderly without affecting cardio-respiratory functions
    Kattenstroth, J. C., Kalisch, T., Holt, S., Tegenthoff, M., & Dinse, H. R.
    Front Aging Neurosci, 5, 5
    pdf
  • Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics
    Kazerounian, S., Luciw, M., Richter, M., & Sandamirskaya, Y.
    In International Joint Conference on Neural Networks (IJCNN)
    pdf
  • Automatic face classification of Cushing’s syndrome in women - A novel screening approach
    Kosilek, R. P., Schopohl, J., Grünke, M., Dimopoulou, C., Stalla, G. K., Lammert, A., et al.
    Experimental and Clinical Endocrinology and Diabetes
    pdf
  • Approximation properties of DBNs with binary hidden units and real-valued visible units
    Krause, O., Fischer, A., Glasmachers, T., & Igel, C.
    In Proceedings of the International Conference on Machine Learning (ICML)
    pdf
  • Deep Hierarchies in the Primate Visual Cortex: What Can We Learn For Computer Vision?
    Krüger, N., Janssen, P., Kalkan, S., Lappe, M., Leonardis, A., Piater, J., et al.
    IEEE Trans. on Pattern Analysis and Machine Intelligence, 35(8), 1847–1871
  • Brain activation in motor sequence learning is related to the level of native cortical excitability
    Lissek, S., Vallana, G. S., Gunturkun, O., Dinse, H., & Tegenthoff, M.
    PLoS ONE, 8(4), e61863
    pdf
  • A software framework for cognition, embodiment, dynamics, and autonomy in robotics: cedar
    Lomp, O., Zibner, S. K. U., Richter, M., Ranó, I., & Schöner, G.
    In International Conference on Artificial Neural Networks (pp. 475–482) Springer
    pdf
  • Learning the Perceptual Conditions of Satisfaction of Elementary Behaviors
    Luciw, M., Kazerounian, S., Lakhmann, K., Richter, M., & Sandamirskaya, Y.
    In Robotics: Science and Systems (RSS), Workshop "Active Learning in Robotics: Exploration, Curiosity, and Interaction"
    pdf
  • An intrinsic value system for developing multiple invariant representations with incremental slowness learning
    Luciw*, M., Kompella*, V., Kazerounian, S., & Schmidhuber, J.
    Frontiers in neurorobotics, 7, *Joint first authors
  • How to Center Binary Restricted Boltzmann Machines
    Melchior, J., Fischer, A., Wang, N., & Wiskott, L.
    (Vol. 1311.1354) arXiv.org e-Print archive
    How to Center Binary Restricted Boltzmann Machines
  • Real-Time Stereo Vision: Optimizing Semi-Global Matching
    Michael, M., Salmen, J., Stallkamp, J., & Schlipsing, M.
    In Proceedings of the IEEE Intelligent Vehicles Symposium (pp. 1197–1202)
    Paper
  • Learning invariant face recognition from examples
    Müller, M. K., Tremer, M., Bodenstein, C., & Würtz, R. P.
    Neural Networks, 41, 137–146
    pdf
  • Are memories really stored in the hippocampal CA3 region?
    Neher, T., Cheng, S., & Wiskott, L.
    BoNeuroMed
  • Are memories really stored in the hippocampal CA3 region?
    Neher, T., Cheng, S., & Wiskott, L.
    In Proc. 10th Göttinger Meeting of the German Neuroscience Society, Mar 13-16, Göttingen, Germany (p. 104)
  • Predictable Feature Analysis
    Richthofer, S., & Wiskott, L.
    CoRR e-print arXiv:1311.2503
    Predictable Feature Analysis - 2013
  • Eine Systemarchitektur für effiziente videobasierte Fahrerassistenzsysteme
    Salmen, J.
    Doctoral thesis, Ruhr-Universität Bochum
    pdf
  • Dynamic Neural Fields as a Step Towards Cognitive Neuromorphic Architectures
    Sandamirskaya, Y.
    Frontiers in Neuroscience, 7, 276
    pdf
  • Increasing Autonomy of Learning SensorimotorTransformations with Dynamic Neural Fields
    Sandamirskaya, Y., & Conradt, J.
    In International Conference on Robotics and Automation (ICRA), Workshop "Autonomous Learning"
    pdf
  • Learning Sensorimotor Transformations with Dynamic Neural Fields
    Sandamirskaya, Y., & Conradt, J.
    In International Conference on Artificial Neural Networks (ICANN)
    pdf
  • Using Dynamic Field Theory to Extend the Embodiment Stance toward Higher Cognition
    Sandamirskaya, Y., Zibner, S. K. U., Schneegans, S., & Schöner, G.
    New Ideas in Psychology, 31(3), 322–339
    pdf
  • Echtzeit-Videoanalyse im Fußball
    Schlipsing, M., Salmen, J., & Igel, C.
    KI - Künstliche Intelligenz, 27(3), 235–240
    Paper
  • RatLab: An easy to use tool for place code simulations
    Schoenfeld, F., & Wiskott, L.
    Frontiers in Computational Neuroscience, 7(104)
    RatLab: an easy to use tool for place code simulations
  • Behavioural and neurophysiological markers reveal differential sensitivity to homeostatic interactions between centrally and peripherally applied passive stimulation
    Tossi, G., Stude, P., Schwenkreis, P., Tegenthoff, M., & Dinse, H. R.
    European Journal of Neuroscience, 38(6), 2893–2901
    pdf
  • Comparing Image Features and Machine Learning Algorithms for Real-time Parking-space Classifiaction
    Tschentscher, M., Neuhausen, M., Koch, C., König, M., Salmen, J., & Schlipsing, M.
    In Proceedings of the ASCE International Workshop on Computing in Civil Engineering (pp. 363–370)
    Paper
  • Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines
    Wang, N., Jancke, D., & Wiskott, L.
    arXiv preprint arXiv:1312.6108
    pdf

2012

  • Autonomous reinforcement of behavioral sequences in neural dynamics
    Kazerounian, S., Luciw, M., Sandamirskaya, Y., Richter, M., Schmidhuber, J., & Schöner, G.
    In IEEE International Conference on Development and Learning and Epigenetic Robotics (Vol. 1, pp. 1–2) Ieee
    pdf
  • Faster perceptual learning through excitotoxic neurodegeneration
    Beste, C., Wascher, E., Dinse, H. R., & Saft, C.
    Curr. Biol., 22(20), 1914–1917
    pdf
  • Naturalistic arm movements during obstacle avoidance in 3D and the identification of movement primitives.
    Grimme, B., Lipinski, J., & Schöner, G.
    Experimental brain research, 222(3), 185–200
    pdf
  • Perceptual improvement following repetitive sensory stimulation depends monotonically on stimulation intensity
    Schlieper, S., & Dinse, H. R.
    Brain Stimul, 5(4), 647–651
    pdf
  • Unsupervised Learning of Face Detection Models from Unlabeled Image Streams
    Walther, T., & Würtz, R. P.
    In A. Brömme & Busch, C. (Eds.), Proceedings of the 11th International Conference of the Biometrics Special Interest Group (Vol. P-196, pp. 221–231) Bonn: Köllen
    pdf
  • Noradrenergic modulation of human visual cortex excitability assessed by paired-pulse visual-evoked potentials
    Hoffken, O., Lenz, M., Hockelmann, N., Dinse, H. R., & Tegenthoff, M.
    Neuroreport, 23(12), 707–711
    pdf
  • Effect of synaptic plasticity on the structure and dynamics of disordered networks of coupled neurons
    Bayati, M., & Valizadeh, A.
    Phys. Rev. E, 86(1), 011925
  • Constraints on the synchronization of entorhinal cortex stellate cells
    Crotty, P., Lasker, E., & Cheng, S.
    Phys. Rev. E, 86(1), 011908
  • Choosing to improve or to impair
    Dinse, H. R.
    Clin Neurophysiol, 123(6), 1063–1064
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  • Increased excitability of somatosensory cortex in aged humans is associated with impaired tactile acuity
    Lenz, M., Tegenthoff, M., Kohlhaas, K., Stude, P., Hoffken, O., Gatica Tossi, M. A., et al.
    J. Neurosci., 32(5), 1811–1816
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  • Cortical topography of intracortical inhibition influences the speed of decision making
    Wilimzig, C., Ragert, P., & Dinse, H. R.
    Proc. Natl. Acad. Sci. U.S.A., 109(8), 3107–3112
    pdf
  • Plasticity of adult sensorimotor system
    Canu, M. H., Coq, J. O., Barbe, M. F., & Dinse, H. R.
    Neural Plast., 2012, 768259
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  • Turning Binary Large-margin Bounds into Multi-class Bounds
    Doğan, Ü., Glasmachers, T., & Igel, C.
    In ICML workshop on RKHS and kernel-based methods
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  • A Note on Extending Generalization Bounds for Binary Large-margin Classifiers to Multiple Classes
    Doğan, Ü., Glasmachers, T., & Igel, C.
    In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD)
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  • Neural Dynamics of Hierarchically Organized Sequences: a Robotic Implementation
    Duran,, & Sandamirskaya, Y.
    In Proceedings of 2012 IEEE-RAS International Conference on Humanoid Robots (Humanoids)
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  • A Dynamic Field Architecture for the Generation of Hierarchically Organized Sequences
    Duran, B., Sandamirskaya, Y., & Schöner, G.
    In A. E. P. Villa, Duch, W., Érdi, P., Masulli, F., & Palm, G. (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2012 (Vol. 7552, pp. 25–32) Springer Berlin Heidelberg
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  • Slow Feature Analysis: Perspectives for Technical Applications of a Versatile Learning Algorithm
    Escalante-B., A. N., & Wiskott, L.
    Künstliche Intelligenz [Artificial Intelligence], 26(4), 341–348
    http://link.springer.com/article/10.1007%2Fs13218-012-0190-7
  • Repetitive tactile stimulation changes resting-state functional connectivity-implications for treatment of sensorimotor decline
    Freyer, F., Reinacher, M., Nolte, G., Dinse, H. R., & Ritter, P.
    Front Hum Neurosci, 6, 144
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  • Convergence of the IGO-Flow of Isotropic Gaussian Distributions on Convex Quadratic Problems
    Glasmachers, T.
    In C. C. Coello, Cutello, V., Deb, K., Forrest, S., Nicosia, G., & Pavone, M. (Eds.), Parallel Problem Solving from Nature (PPSN) Springer
    pdf
  • Kernel Representations for Evolving Continuous Functions
    Glasmachers, T., Koutník, J., & Schmidhuber, J.
    Journal of Evolutionary Intelligence, 5(3), 171–187
    pdf
  • Orientation selective or not?–Measuring significance of tuning to a circular parameter
    Grabska-Barwińska, A., Ng, B. S. W., & Jancke, D.
    Journal of neuroscience methods, 203(1), 1–9
  • Age-related changes in the joint position sense of the human hand
    Kalisch, T., Kattenstroth, J. C., Kowalewski, R., Tegenthoff, M., & Dinse, H. R.
    Clin Interv Aging, 7, 499–507
    pdf
  • Cognitive and tactile factors affecting human haptic performance in later life
    Kalisch, T., Kattenstroth, J. C., Kowalewski, R., Tegenthoff, M., & Dinse, H. R.
    PLoS ONE, 7(1), e30420
    pdf
  • Long-term sensory stimulation therapy improves hand function and restores cortical responsiveness in patients with chronic cerebral lesions. Three single case studies
    Kattenstroth, J. C., Kalisch, T., Peters, S., Tegenthoff, M., & Dinse, H. R.
    Front Hum Neurosci, 6, 244
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  • Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making
    Klaes, C., Schneegans, S., Schöner, G., & Gail, A.
    PLoS computational biology, 8(11), e1002774
    supplementary material pdf
  • Incremental slow feature analysis: Adaptive low-complexity slow feature updating from high-dimensional input streams
    Kompella, V. R., Luciw, M., & Schmidhuber, J.
    Neural Computation, 24(11), 2994–3024
  • Autonomous learning of abstractions using curiosity-driven modular incremental slow feature analysis
    Kompella, V. R., Luciw, M., Stollenga, M., Pape, L., & Schmidhuber, J.
    In Development and Learning and Epigenetic Robotics (ICDL), 2012 IEEE International Conference on (pp. 1–8) IEEE
  • Collective-reward based approach for detection of semi-transparent objects in single images
    Kompella, V. R., & Sturm, P.
    Computer Vision and Image Understanding, 116(4), 484–499
  • Improved acuity and dexterity but unchanged touch and pain thresholds following repetitive sensory stimulation of the fingers
    Kowalewski, R., Kattenstroth, J. C., Kalisch, T., & Dinse, H. R.
    Neural Plast., 2012, 974504
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  • The Function and Fallibility of Visual Feature Integration: A Dynamic Neural Field Model of Illusory Conjunctions
    Lins, J., Schneegans, S., Spencer, J., & Schöner, G.
    Frontiers in Computational Neuroscience, (128)
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  • A Neuro-Behavioral Model of Flexible Spatial Language Behaviors
    Lipinski, J., Schneegans, S., Sandamirskaya, Y., Spencer, J. P., & Schöner, G.
    Journal of Experimental Psychology: Learning, Memory and Cognition., 38(6), 1490–1511
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  • Hierarchical incremental slow feature analysis
    Luciw, M., Kompella, V. R., & Schmidhuber, J.
    Workshop on Deep Hierarchies in Vision
  • Lernen situationsunabhängiger Personenerkennung
    Müller, M. K., Tremer, M., Bodenstein, C., & Würtz, R. P.
    Informatikspektrum, 35(2), 112–118
    pdf
  • Functional synergies underlying control of upright posture during changes in head orientation
    Park, E., Schöner, G., & Scholz, J. P.
    PLoS ONE, 7(8), 1–12
    pdf
  • A robotic architecture for action selection and behavioral organization inspired by human cognition
    Richter, M., Sandamirskaya, Y., & Schöner, G.
    In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
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  • Predictable Feature Analysis
    Richthofer, S., Weghenkel, B., & Wiskott, L.
    In Frontiers in Computational Neuroscience
    Poster - Predictable Feature Analysis - 2012
  • Google street view images support the development of vision-based driver assistance systems
    Salmen, J., Houben, S., & Schlipsing, M.
    In Proceedings of the Intelligent Vehicles Symposium (IV), 2012 IEEE (pp. 891–895) IEEE
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  • Roll Angle Estimation for Motorcycles: Comparing Video and Inertial Sensor Approaches
    Schlipsing, M., Salmen, J., Lattke, B., Schröter, K., & Winner, H.
    In Proceedings of the IEEE Intelligent Vehicles Symposium (pp. 500–505)
    Paper Poster
  • A neural mechanism for coordinate transformation predicts pre-saccadic remapping
    Schneegans, S., & Schöner, G.
    Biological cybernetics, 106(2), 89–109
    pdf
  • How visual information links to multijoint coordination during quiet standing
    Scholz, J. P., Park, E., Jeka, J. J., Schöner, G., & Kiemel, T.
    Experimental Brain Research, 222, 229–239
    pdf
  • Sensory integration of place and head-direction cells in a virtual environment
    Schönfeld, F., & Wiskott, L.
    Poster at NeuroVisionen 8, 26. Oct 2012, Aachen, Germany
  • Sensory integration of place and head-direction cells in a virtual environment
    Schönfeld, F., & Wiskott, L.
    Poster at the 8th FENS Forum of Neuroscience, Jul 14–18, Barcelona, Spain
  • Man vs. Computer: Benchmarking Machine Learning Algorithms for Traffic Sign Recognition
    Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C.
    Neural Networks, 32, 323–332
    Paper
  • Evaluation von Monocular Inverse Depth SLAM zur Stützung von Fahrzeugodometrie (Masterarbeit)
  • Video-based Parking-space Detection
    Tschentscher, M., & Neuhausen, M.
    In Proceedings of the Forum Bauinformatik (pp. 159–166)
    Paper
  • Hydroacoustic Signal Classification Using Support Vector Machines
    Tuma, M., Igel, C., & Prior, M.
    In C. Chen (Ed.), Signal and Image Processing for Remote Sensing, 2nd ed. (pp. 37–56) CRC Press
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  • A neural-dynamic architecture for flexible spatial language: intrinsic frames, the term “between”, and autonomy
    van Hengel, U., Sandamirskaya, Y., Schneegans, S., & Schöner, G.
    In 21st IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man) 2012 (pp. 150–157)
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  • An Analysis of Gaussian-Binary Restricted Boltzmann Machines for Natural Images
    Wang, N., Melchior, J., & Wiskott, L.
    In Proc. 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 25–27, Bruges, Belgium (pp. 287–292)
    An Analysis of Gaussian-Binary Restricted Boltzmann Machines for Natural Images

2011

  • An rTMS study into self-face recognition using video-morphing technique
    Heinisch, C., Dinse, H. R., Tegenthoff, M., Juckel, G., & Brune, M.
    Soc Cogn Affect Neurosci, 6(4), 442–449
  • Improvement and impairment of visually guided behavior through LTP- and LTD-like exposure-based visual learning
    Beste, C., Wascher, E., Gunturkun, O., & Dinse, H. R.
    Curr. Biol., 21(10), 876–882
    pdf
  • The temporal dynamics of global-to-local feedback in the formation of hierarchical motion patterns: psychophysics and computational simulations.
    Hock, H. S., Schöner, G., Brownlow, S., & Taler, D.
    Attention, perception & psychophysics, 73(4), 1171–94
    pdf
  • Slow feature analysis
    Wiskott, L., Berkes, P., Franzius, M., Sprekeler, H., & Wilbert, N.
    Scholarpedia, 6(4), 5282
    Publisher's document page
  • Genetic determination of the human facial shape: links between cleft-lips and normal variation
    Böhringer, S., van der Lijn, F., Liu, F., Sinigerova, S., Birnbaum, S., Mangold, E., et al.
    European Journal of Human Genetics, 19, 1192–1197
    pdf
  • Reactivation, Replay, and Preplay: How It Might All Fit Together
    Buhry, L., Azizi, A. H., & Cheng, S.
    Neural Plasticity, 2011, 1–11
  • Lateral spread of orientation selectivity in V1 is controlled by intracortical cooperativity
    Chavane, F., Sharon, D., Jancke, D., Marre, O., Frégnac, Y., & Grinvald, A.
    Frontiers in Systems Neuroscience, 5
  • The structure of networks that produce the transformation from grid cells to place cells
    Cheng, S., & Frank, L. M.
    Neuroscience , 197, 293–306
  • Novelty Restarts for Evolution Strategies
    Cuccu, G., Gomez, F., & Glasmachers, T.
    In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) IEEE
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  • Brain Plasticity and Touch
    Dinse, H. R.
    In M. J. Hertenstein & Weiss, S. J. (Eds.), The Handbook of Touch: neuroscience, behavioral, and health perspectives (pp. 85–120) New York : Springer
  • Sensory stimulation for augmenting perception, sensorimotor behaviour and cognition
    Dinse, H. R., Kattenstroth, J. C., Gatica Tossi, M. A., Tegenthoff, M., & Kalisch, T.
    In H. Markram & Segev, I. (Eds.), Augmenting Cognition (pp. 11–39) Lausanne : EPFL Press
  • Plastizität, motorisches Lernen und sensible Stimulation
    Dinse, H. R., Kattenstroth, J. C., Tegenthoff, M., & Kalisch, T.
    In C. Dettmers & Stephan, K. M. (Eds.), Motorische Therapie nach Schlaganfall (pp. 17–43) : Hippocampus
  • Heuristic Evaluation of Expansions for Non-Linear Hierarchical Slow Feature Analysis.
    Escalante, A., & Wiskott, L.
    In Proc. The 10th Intl. Conf. on Machine Learning and Applications (ICMLA′11), Dec 18–21, Honolulu, Hawaii (pp. 133–138) IEEE Computer Society
    http://www.computer.org/csdl/proceedings/icmla/2011/4607/01/4607a133-abs.html
  • Human umbilical cord blood cells restore brain damage induced changes in rat somatosensory cortex
    Geissler, M., Dinse, H. R., Neuhoff, S., Kreikemeier, K., & Meier, C.
    PLoS ONE, 6(6), e20194
    pdf
  • Optimal Direct Policy Search
    Glasmachers, T., & Schmidhuber, J.
    In Proceedings of the 4th Conference on Artificial General Intelligence (AGI)
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  • Striatal functional connectivity networks are modulated by fMRI resting state conditions
    Gopinath, K., Ringe, W., Goyal, A., Carter, K., Dinse, H. R., Haley, R., & Briggs, R.
    Neuroimage, 54(1), 380–388
  • Artificial Curiosity for Autonomous Space Exploration
    Graziano, V., Glasmachers, T., Schaul, T., Pape, L., Cuccu, G., Leitner, J., & Schmidhuber, J.
    ACTA FUTURA
    pdf
  • Limb versus speech motor control: a conceptual review.
    Grimme, B., Fuchs, S., Perrier, P., & Schöner, G.
    Motor control, 15(1), 5–33
    pdf
  • A single target voting scheme for traffic sign detection
    Houben, S.
    In Proceedings of the Intelligent Vehicles Symposium (IV) (pp. 124–129)
    pdf
  • Rapid assessment of age-related differences in standing balance
    Kalisch, T., Kattenstroth, J. C., Noth, S., Tegenthoff, M., & Dinse, H. R.
    J Aging Res, 2011, 160490
    pdf
  • Questionnaire-based evaluation of everyday competence in older adults
    Kalisch, T., Richter, J., Lenz, M., Kattenstroth, J. C., Kolankowska, I., Tegenthoff, M., & Dinse, H. R.
    Clin Interv Aging, 6, 37–46
    pdf
  • Balance, sensorimotor, and cognitive performance in long-year expert senior ballroom dancers
    Kattenstroth, J. C., Kalisch, T., Kolankowska, I., & Dinse, H. R.
    J Aging Res, 2011, 176709
    pdf
  • Incremental Slow Feature Analysis.
    Kompella, V. R., Luciw, M. D., & Schmidhuber, J.
    IJCAI, 11, 1354–1359
  • Autoincsfa and vision-based developmental learning for humanoid robots
    Kompella, V. R., Pape, L., Masci, J., Frank, M., & Schmidhuber, J.
    In Humanoid Robots (Humanoids), 2011 11th IEEE-RAS International Conference on (pp. 622–629) IEEE
  • Detection and avoidance of semi-transparent obstacles using a collective-reward based approach
    Kompella, V. R., & Sturm, P.
    In Robotics and Automation (ICRA), 2011 IEEE International Conference on (pp. 3469–3474) IEEE
  • Natural scene evoked population dynamics across cat primary visual cortex captured with voltage-sensitive dye imaging
    Onat, S., König, P., & Jancke, D.
    Cerebral cortex, 21(11), 2542–2554
  • Independent encoding of grating motion across stationary feature maps in primary visual cortex visualized with voltage-sensitive dye imaging
    Onat, S., Nortmann, N., Rekauzke, S., König, P., & Jancke, D.
    Neuroimage, 55(4), 1763–1770
  • Autonomous movement generation for manipulators with multiple simultaneous constraints using the attractor dynamics approach
    Reimann, H., Iossifidis, I., & Schöner, G.
    In 2011 IEEE International Conference on Robotics and Automation, ICRA2011
  • Nasal chemosensory-stimulation evoked activity patterns in the rat trigeminal ganglion visualized by in vivo voltage-sensitive dye imaging
    Rothermel, M., Ng, B. S. W., Grabska-Barwińska, A., Hatt, H., & Jancke, D.
    PloS one, 6(10), e26158
  • Real-time estimation of optical flow based on optimized Haar wavelet features
    Salmen, J., Caup, L., & Igel, C.
    In Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization (pp. 448–461)
    Paper
  • A neural-dynamic architecture for behavioral organization of an embodied agent
    Sandamirskaya, Y., Richter, M., & Schöner, G.
    In IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL EPIROB 2011) (pp. 1–7)
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  • High Dimensions and Heavy Tails for Natural Evolution Strategies
    Schaul, T., Glasmachers, T., & Schmidhuber, J.
    In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)
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  • Coherence Progress: A Measure of Interestingness Based on Fixed Compressors
    Schaul, T., Pape, L., Glasmachers, T., Graziano, V., & Schmidhuber, J.
    In Proceedings of the 4th Conference on Artificial General Intelligence (AGI)
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  • Video-Based Roll Angle Estimation for Two-Wheeled Vehicles
    Schlipsing, M., Schepanek, J., & Salmen, J.
    In Proceedings of the IEEE Intelligent Vehicles Symposium (pp. 876–881)
    Paper Poster
  • A novel approach for the detection of acromegaly: accuracy of diagnosis by automatic face classification
    Schneider, H. J., Kosilek, R. P., Günther, M., Schopohl, J., Römmler, J., Stalla, G. K., et al.
    Journal of Clinical Endocrinology and Metabolism, 96(7), 2074–2080
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  • Motor equivalence and self-motion induced by different movement speeds
    Scholz, J. P., Dwight-Higgin, T., Lynch, J. E., Tseng, Y. -W., Martin, V., & Schöner, G.
    Experimental Brain Research, 209(3), 319–332
    pdf
  • The German Traffic Sign Recognition Benchmark: A multi-class classification competition
    Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C.
    In Proceedings of the IEEE International Joint Conference on Neural Networks (pp. 1453–1460)
    Paper
  • Improved working set selection for LaRank
    Tuma, M., & Igel, C.
    In A. Berciano, Díaz-Pernil, D., Kropatsch, W., Molina-Abril, H., & Real, P. (Eds.), Proceedings of the 14th International Conference on Computer Analysis of Images and Patterns (CAIP) (Vol. 6854, pp. 327–334) Springer Press
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  • Learning to Look at Humans
    Walther, T., & Würtz, R. P.
    In C. Müller-Schloer, Schmeck, H., & Ungerer, T. (Eds.), Organic Computing - a Paradigm Shift for Complex Systems (pp. 309–322) Springer
  • Dynamic Neural Fields as Building Blocks of a Cortex-Inspired Architecture for Robotic Scene Representation
    Zibner, S. K. U., Faubel, C., Iossifidis, I., & Schöner, G.
    IEEE Transactions on Autonomous Mental Development, 3(1), 74–91
    pdf
  • Making a robotic scene representation accessible to feature and label queries
    Zibner, S. K. U., Faubel, C., & Schöner, G.
    In Proceedings of the First Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL EPIROB 2011)

2010

  • 3-SAT on CUDA: Towards a massively parallel SAT solver
    Meyer, Q., Schönfeld, F., Stamminger, M., & Wanka, R.
    In 2010 International Conference on High Performance Computing Simulation (pp. 306–313)
  • Effects of aging on paired-pulse behavior of rat somatosensory cortical neurons
    David-Jurgens, M., & Dinse, H. R.
    Cereb. Cortex, 20(5), 1208–1216
  • Evolutionary Optimization of Growing Neural Gas Parameters for Object Categorization and Recognition
    Donatti, G. S., Lomp, O., & Würtz, R. P.
    In Proc. IJCNN (pp. 1862–1869) IEEE Computer Society, Los Alamitos, CA
    pdf
  • Building a Side Channel Based Disassembler
    Eisenbarth, T., Paar, C., & Weghenkel, B.
    In M. L. Gavrilova, Tan, C. J. K., & Moreno, E. D. (Eds.), Transactions on Computational Science X: Special Issue on Security in Computing, Part I (pp. 78–99) Berlin, Heidelberg: Springer Berlin Heidelberg
  • Gender and Age Estimation from Synthetic Face Images with Hierarchical Slow Feature Analysis.
    Escalante, A., & Wiskott, L.
    In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU′10), Jun 28 – Jul 2, Dortmund
  • A neuro-dynamic object recognition architecture enhanced by foveal vision and a gaze control mechanism
    Faubel, C., & Zibner, S. K. U.
    In Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on (pp. 1171–1176) IEEE
  • Motor Abundance Contributes to Resolving Multiple Kinematic Task Constraints
    Gera, G., Freitas, S., Latash, M., Monahan, K., Schöner, G., & Scholz, J.
    Motor Control, 14, 83–115
    pdf
  • Universal Consistency of Multi-Class Support Vector Classification
    Glasmachers, T.
    In Advances in Neural Information Processing Systems (NIPS)
    pdf
  • Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters
    Glasmachers, T., & Igel, C.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1522–1528
    pdf
  • A Natural Evolution Strategy for Multi-Objective Optimization
    Glasmachers, T., Schaul, T., & Schmidhuber, J.
    In Parallel Problem Solving from Nature (PPSN) Springer
    pdf
  • Exponential Natural Evolution Strategies
    Glasmachers, T., Schaul, T., Sun, Y., Wierstra, D., & Schmidhuber, J.
    In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)
    pdf
  • Two kinds of statistics for better face recognition
    Günther, M., Müller, M. K., & Würtz, R. P.
    In T. E. Simos, Psihoyios, G., & Tsitouras, C. (Eds.), Numerical Analysis and Applied Mathematics, International Conference (pp. 1901–1904) American Institute of Physics
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  • Measuring Perceptual Hysteresis with the Modified Method of Limits: Dynamics at the Threshold
    Hock, H. S., & Schöner, G.
    Seeing and Perceiving, 23, 173–195
    pdf
  • Stimulus Localization by Neuronal Populations in Early Visual Cortex: Linking Functional Architecture to Perception
    Jancke, D., Chavane, F., & Grinvald, A.
    In Dynamics of Visual Motion Processing (pp. 95–116) Springer
    pdf
  • Bridging the Gap: A Model of Common Neural Mechanisms Underlying the Fröhlich Effect, the Flash-Lag Effect, and the Representational Momentum Effect
    Jancke, D., & Erlhagen, W.
    In Space and Time in Perception and Action (pp. 422–440) Cambridge: Cambridge University Press
    pdf
  • Repetitive electric stimulation elicits enduring improvement of sensorimotor performance in seniors
    Kalisch, T., Tegenthoff, M., & Dinse, H. R.
    Neural Plast., 2010, 690531
  • Superior sensory, motor, and cognitive performance in elderly individuals with multi-year dancing activities
    Kattenstroth, J. C., Kolankowska, I., Kalisch, T., & Dinse, H. R.
    Front Aging Neurosci, 2,
  • Motor control theories and their applications
    Latash, M., Levin, M. F., Scholz, J. P., & Schöner, G.
    Medicina (Kaunas), 29(6), 997–1003
    pdf
  • A dynamic neural field model of mesoscopic cortical activity captured with voltage-sensitive dye imaging
    Markounikau, V., Igel, C., Grinvald, A., & Jancke, D.
    PLoS computational biology, 6(9), e1000919
  • Dominant vertical orientation processing without clustered maps: early visual brain dynamics imaged with voltage-sensitive dye in the pigeon visual Wulst
    Ng, B. S. W., Grabska-Barwińska, A., Güntürkün, O., & Jancke, D.
    J Neurosci, 30(19), 6713–6725
  • Efficient Update of the Covariance Matrix Inverse in Iterated Linear Discriminant Analysis
    Salmen, J., Schlipsing, M., & Igel, C.
    Pattern Recognition Letters, 31, 1903–1907
    Paper
  • Natural human-robot interaction through spatial language: a dynamic neural fields approach
    Sandamirskaya, Y., Lipinski, J., Iossifidis, I., & Schöner, G.
    In 19th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN (pp. 600–607) Viareggio, Italy
    pdf
  • An embodied account of serial order: how instabilities drive sequence generation
    Sandamirskaya, Y., & Schöner, G.
    Neural Networks, 23(10), 1164–1179
    pdf
  • Serial order in an acting system: a multidimensional dynamic neural fields implementation
    Sandamirskaya, Y., & Schöner, G.
    In Development and Learning, 2010. ICDL 2010. 9th IEEE International Conference on
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  • Frontier Search
    Sun, Y., Glasmachers, T., Schaul, T., & Schmidhuber, J.
    In Proceedings of the 3rd Conference on Artificial General Intelligence (AGI)
    pdf
  • Effiziente Berechnung von 3D-Flußvektoren (Scene Flow) (Bachelorarbeit)
  • Hydroacoustic Signal Classification Using Kernel Functions for Variable Feature Sets
    Tuma, M., Igel, C., & Prior, M.
    In Proc. of the 20th International Conference on Pattern Recognition (ICPR) (pp. 1011–1014)
    pdf
  • Learning Generic Human Body Models
    Walther, T., & Würtz, R. P.
    In F. J. Perales & Fisher, R. B. (Eds.), Proc. Sixth Conference on Articulated Motion and Deformable Objects (pp. 98–107) Springer
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  • Editorial: Special Issue on Organic Computing
    Würtz, R. P., Bellman, K. L., Schmeck, H., & Igel, C.
    ACM Transactions on Autonomous and Adaptive Systems, 5(3), 1–3
    pdf
  • Scene Representation for Anthropomorphic Robots: A Dynamic Neural Field Approach
    Zibner, S. K. U., Faubel, C., Iossifidis, I., & Schöner, G.
    In ISR / ROBOTIK 2010 Munich, Germany
  • Scenes and tracking with dynamic neural fields: How to update a robotic scene representation
    Zibner, S. K. U., Faubel, C., Iossifidis, I., Schöner, G., & Spencer, J. P.
    In Development and Learning (ICDL), 2010 IEEE 9th International Conference on (pp. 244–250) IEEE

2009

  • Receptive field plasticity of area 17 visual cortical neurons of adult rats
    Leonhardt, R., & Dinse, H. R.
    Exp Brain Res, 199(3-4), 401–410
  • Effects of repetitive electrical stimulation to treat sensory loss in persons poststroke
    Smith, P. S., Dinse, H. R., Kalisch, T., Johnson, M., & Walker-Batson, D.
    Arch Phys Med Rehabil, 90(12), 2108–2111
  • A layered neural architecture for the consolidation, maintenance, and updating of representations in visual working memory.
    Johnson, J. S., Spencer, J. P., & Schöner, G.
    Brain research, 1299, 17–32
    pdf
  • A layered neural architecture for the consolidation, maintenance, and updating of representations in visual working memory.
    Johnson, J. S., Spencer, J. P., & Schöner, G.
    Brain research, 1299, 17–32
    pdf
  • A counterchange mechanism for the perception of motion.
    Hock, H. S., Schöner, G., & Gilroy, L.
    Acta psychologica, 132(1), 1–21
    pdf
  • Visual paired-pulse stimulation reveals enhanced visual cortex excitability in migraineurs
    Hoffken, O., Stude, P., Lenz, M., Bach, M., Dinse, H. R., & Tegenthoff, M.
    Eur. J. Neurosci., 30(4), 714–720
  • Impaired tactile acuity in old age is accompanied by enlarged hand representations in somatosensory cortex
    Kalisch, T., Ragert, P., Schwenkreis, P., Dinse, H. R., & Tegenthoff, M.
    Cereb. Cortex, 19(7), 1530–1538
  • Immobilization impairs tactile perception and shrinks somatosensory cortical maps
    Lissek, S., Wilimzig, C., Stude, P., Pleger, B., Kalisch, T., Maier, C., et al.
    Curr. Biol., 19(10), 837–842
  • Slowness learning
    Sprekeler, H.
    Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I
    University's eprint server
  • Gehirne begreifen und erfassen: Tasten – der unterschätzte Sinn
    Dinse, H. R.
    In R. Rosenzweig (Ed.), Nicht wahr?! - Sinneskanäle, Hirnwindungen und Grenzen der Wahrnehmung (pp. 133–164) : mentis Verlag
  • Ageing and Touch
    Dinse, H. R., Tegenthoff, M., Heinisch, C., & Kalisch, T.
    In B. Goldstein (Ed.), The Sage Encyclopedia of Perception (pp. 21–24) : Sage
  • Using Growing Neural Gas Networks to represent visual object knowledge
    Donatti, G. S., & Würtz, R. P.
    In Proceedings of the 21st IEEE International Conference on Tools with Artificial Intelligence (pp. 54–58) IEEE Computer Society, Newark, NJ
    pdf
  • Contrast independence of cardinal preference: stable oblique effect in orientation maps of ferret visual cortex
    Grabska-Barwińska, A., Distler, C., Hoffmann, K. -P., & Jancke, D.
    European Journal of Neuroscience, 29(6), 1258–1270
  • Face Detection and Recognition Using Maximum Likelihood Classifiers on Gabor Graphs
    Günther, M., & Würtz, R. P.
    International Journal of Pattern Recognition and Artificial Intelligence, 23(3), 433–461
    pdf
  • A map of periodicity orthogonal to frequency representation in the cat auditory cortex
    Langner, G., Dinse, H. R., & Godde, B.
    Front Integr Neurosci, 3, 27
  • Swing it to the Left, Swing it to the Right: Enacting Flexible Spatial Language Using a Neurodynamic Framework
    Lipinski, J., Sandamirskaya, Y., & Schöner, G.
    Cognitive Neurodynamics, 3(4)
    pdf
  • An Integrative Framework for Spatial Language and Color: Robotic Demonstrations Using the Dynamic Field Theory.
    Lipinski, J., Sandamirskaya, Y., & Schöner, G.
    In 31th Annual Meeting of the Cognitive Science Society, CogSci 2009 Amstredam, NL
    pdf
  • Behaviorally Flexible Spatial Communication: Robotic Demonstrations of a Neurodynamic Framework
    Lipinski, J., Sandamirskaya, Y., & Schöner, G.
    In B. Mertsching, Hund, M., & Z., A. (Eds.), KI 2009, Lecture Notes in Artificial Intelligence (Vol. 5803, pp. 257–264) Berlin: Springer-Verlag
    pdf
  • A Mesoscopic Model of VSD Dynamics Observed in Visual Cortex Induced by Flashed and Moving Stimuli
    Markounikau, V., Igel, C., & Jancke, D.
    In Frontiers in Computational Neuroscience (p. 64) Frontiers
  • Redundancy, self-motion and motor control
    Martin, V., Scholz, J. P., & Schöner, G.
    Neural Computation, 21(5), 1371–1414
    pdf
  • Learning from Examples to Generalize over Pose and Illumination
    Müller, M. K., & Würtz, R. P.
    In C. Alippi, Polycarpou, M., Panayiotou, C., & Ellinas, G. (Eds.), Artificial Neural Networks - ICANN 2009 (Vol. 5769, pp. 643–652) Springer
    pdf
  • Strengthening of lateral activation in adult rat visual cortex after retinal lesions captured with voltage-sensitive dye imaging in vivo
    Palagina, G., Eysel, U. T., & Jancke, D.
    Proceedings of the National Academy of Sciences, 106(21), 8743–8747
  • Voltage-Sensitive Dye Imaging of Odor Evoked Activity Patterns in the Trigeminal Ganglion in vivo
    Rothermel, M., Ng, B., Hatt, H., & Jancke, D.
    In CHEMICAL SENSES (Vol. 34, pp. A30–A31) OXFORD UNIV PRESS GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
  • Real-Time Stereo Vision: Making more out of Dynamic Programming
    Salmen, J., Schlipsing, M., Edelbrunner, J., Hegemann, S., & Lueke, S.
    In Proceedings of the International Conference on Computer Analysis of Images and Patterns (pp. 1096–1103)
    Paper
  • Temporal stabilization of discrete movement in variable environments: an attractor dynamics approach
    Tuma, M., Iossifidis, I., & Schöner, G.
    In IEEE International Conference on Robotics and Automation (ICRA) (pp. 863–868)
    pdf
  • Unsupervised learning of human body parts from video footage
    Walther, T., & Würtz, R. P.
    In Proceedings of ICCV workshops, Kyoto (pp. 336–343) IEEE Computer Society, Los Alamitos, CA
    pdf
  • Combining Feature- and Correspondence-Based Methods for Visual Object Recognition
    Westphal, G., & Würtz, R. P.
    Neural Computation, 21(7), 1952–1989
  • Combining Feature- and Correspondence-Based Methods for Visual Object Recognition
    Westphal, G., & Würtz, R. P.
    Neural Computation, 21(7), 1952–1989
    pdf

2008

  • Excitation and inhibition jointly regulate cortical reorganization in adult rats
    Benali, A., Weiler, E., Benali, Y., Dinse, H. R., & Eysel, U. T.
    J. Neurosci., 28(47), 12284–12293
  • Homeostatic metaplasticity in the human somatosensory cortex
    Bliem, B., Muller-Dahlhaus, J. F., Dinse, H. R., & Ziemann, U.
    J Cogn Neurosci, 20(8), 1517–1528
  • Paired-pulse behavior of visually evoked potentials recorded in human visual cortex using patterned paired-pulse stimulation
    Hoffken, O., Grehl, T., Dinse, H. R., Tegenthoff, M., & Bach, M.
    Exp Brain Res, 188(3), 427–435
  • New Experiences Enhance Coordinated Neural Activity in the Hippocampus
    Cheng, S., & Frank, L. M.
    Neuron , 57(2), 303–313
  • Differential effects of aging on fore- and hindpaw maps of rat somatosensory cortex
    David-Jurgens, M., Churs, L., Berkefeld, T., Zepka, R. F., & Dinse, H. R.
    PLoS ONE, 3(10), e3399
  • Haptic Banknote Design
    Dinse, H. R.
    In M. Grunwald (Ed.), Human Haptic Perception - Basics and Applications (pp. 537–547) : Birkhäuser
  • Learning in Haptic Perception
    Dinse, H. R., Wilimzig, C., & Kalisch, T.
    In M. Grunwald (Ed.), Human Haptic Perception - Basics and Applications (pp. 165–182) : Birkhäuser
  • On related violating pairs for working set selection in SMO algorithms
    Glasmachers, T.
    In M. Verleysen (Ed.), Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) d-side publications
    pdf
  • Gradient Based Optimization of Support Vector Machines
    Glasmachers, T.
    Doctoral thesis, Fakultät für Mathematik, Ruhr-Universität Bochum, Germany
    pdf
  • Second-Order SMO Improves SVM Online and Active Learning
    Glasmachers, T., & Igel, C.
    Neural Computation, 20(2), 374–382
    pdf
  • Uncertainty Handling in Model Selection for Support Vector Machines
    Glasmachers, T., & Igel, C.
    In G. Rudolph, Jansen, T., Lucas, S., Poloni, C., & Beume, N. (Eds.), Parallel Problem Solving from Nature (PPSN) (pp. 185–194) Springer
    pdf
  • Shark
    Igel, C., Heidrich-Meisner, V., & Glasmachers, T.
    Journal of Machine Learning Research, 9, 993–996
    pdf jmlr.org
  • Improvement of sensorimotor functions in old age by passive sensory stimulation
    Kalisch, T., Tegenthoff, M., & Dinse, H. R.
    Clin Interv Aging, 3(4), 673–690
  • Self-organized evaluation of dynamic hand gestures for sign language recognition
    Krüger, M., von der Malsburg, C., & Würtz, R. P.
    In R. P. Würtz (Ed.), Organic Computing (pp. 321–342) Springer
    pdf
  • Image Segmentation by a Network of Cortical Macrocolumns with Learned Connection Weights
    Lessmann, M., & Würtz, R. P.
    In M. Hinchey, Pagnonia, A., Rammig, F. J., & Schmeck, H. (Eds.), Proceedings of Biologically-Inspired Collaborative Computing (BICC), Milano, Sep. 2008 (pp. 177–186) Springer
    pdf
  • Improvement of tactile perception and enhancement of cortical excitability through intermittent theta burst rTMS over human primary somatosensory cortex
    Ragert, P., Franzkowiak, S., Schwenkreis, P., Tegenthoff, M., & Dinse, H. R.
    Exp Brain Res, 184(1), 1–11
  • Differential effects of tactile high- and low-frequency stimulation on tactile discrimination in human subjects
    Ragert, P., Kalisch, T., Bliem, B., Franzkowiak, S., & Dinse, H. R.
    BMC Neurosci, 9, 9
  • Dynamic Field Theory as a framework for understanding embodied cognition
    Schneegans, S., & Schöner, G.
    In P. Calvo & Gomila, T. (Eds.), Handbook of cognitive science: An embodied approach (pp. 241–271) Amsterdam, Netherlands: Elsevier
    pdf
  • Impact of geometry and viewing angle on classification accuracy of 2D based analysis of dysmorphic faces
    Vollmar, T., Maus, B., Würtz, R. P., Gillessen-Kaesbach, G., Horsthemke, B., Wieczorek, D., & Böhringer, S.
    European Journal of Medical Genetics, 51, 44–53
    pdf
  • Feature-driven emergence of model graphs for object recognition and categorization
    Westphal, G., von der Malsburg, C., & Würtz, R. P.
    In A. Kandel, Bunke, H., & Last, M. (Eds.), Applied Pattern Recognition (pp. 155–199) Springer
    pdf
  • Organic Computing for Image Understanding and Robotics
  • Organic Computing
    Würtz_(editor),
    Springer

2007

  • Assessment of sensorimotor cortical representation asymmetries and motor skills in violin players
    Schwenkreis, P., El Tom, S., Ragert, P., Pleger, B., Tegenthoff, M., & Dinse, H. R.
    Eur. J. Neurosci., 26(11), 3291–3302
  • Sustained increase of somatosensory cortex excitability by tactile coactivation studied by paired median nerve stimulation in humans correlates with perceptual gain
    Hoffken, O., Veit, M., Knossalla, F., Lissek, S., Bliem, B., Ragert, P., et al.
    J. Physiol. (Lond.), 584(Pt 2), 463–471
  • Dopaminergic influences on changes in human tactile acuity induced by tactile coactivation
    Bliem, B., Frombach, E., Ragert, P., Knossalla, F., Woitalla, D., Tegenthoff, M., & Dinse, H. R.
    Exp Brain Res, 181(1), 131–137
  • Increased functional connectivity is crucial for learning novel muscle synergies
    McNamara, A., Tegenthoff, M., Dinse, H., Buchel, C., Binkofski, F., & Ragert, P.
    Neuroimage, 35(3), 1211–1218
  • A common framework for perceptual learning
    Seitz, A. R., & Dinse, H. R.
    Curr. Opin. Neurobiol., 17(2), 148–153
  • Calibration of Visually Guided Reaching Is Driven by Error-Corrective Learning and Internal Dynamics
    Cheng, S., & Sabes, P. N.
    Journal of Neurophysiology, 97(4), 3057–3069
    Cheng, Sabes - 2007
  • Somatosensorik
    Dinse, H. R., Ragert, P., & Tegenthoff, M.
    In H. Siebner & Ziemann, U. (Eds.), Das TMS-Buch. Transkranielle Magnetstimulation (pp. 439–448) Heidelberg : Springer
  • Gradient-Based Optimization of Kernel-Target Alignment for Sequence Kernels Applied to Bacterial Gene Start Detection
    Igel, C., Glasmachers, T., Mersch, B., Pfeifer, N., & Meinicke, P.
    IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 4(2), 216–226
    pdf
  • Differential effects of synchronous and asynchronous multifinger coactivation on human tactile performance
    Kalisch, T., Tegenthoff, M., & Dinse, H. R.
    BMC Neurosci, 8, 58
  • Fast image processing with constraints by solving linear PDEs
    Kusnezow, W., Horn, W., & Würtz, R. P.
    Electronic Letters on Computer Vision and Image Analysis, 6(2), 22–35
    pdf
  • Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts
    Mersch, B., Glasmachers, T., Meinicke, P., & Igel, C.
    International Journal of Neural Systems, 17(5), 369–381
    pdf
  • Similarity rank correlation for face recognition under unenrolled pose
    Müller, M. K., Heinrichs, A., Tewes, A. H. J., Schäfer, A., & , R. P. W.
    In S. -W. Lee & Li, S. Z. (Eds.), Advances in Biometrics (pp. 67–76) Springer
    pdf
  • Evolutionary Optimization of Wavelet Feature Sets for Real-Time Pedestrian Classification
    Salmen, J., Suttorp, T., Edelbrunner, J., & Igel, C.
    In Proceedings of the IEEE Conference on Hybrid Intelligent Systems (pp. 222–227)
    Paper
  • Echtzeitfähige und robuste Erkennung von Verkehrszeichen in Videosequenzen mit Hilfe von Haar-Merkmalen (Diplomarbeit)
  • Cortical response field dynamics in cat visual cortex
    Sharon, D., Jancke, D., Chavane, F., Na′aman, S., & Grinvald, A.
    Cerebral Cortex, 17(12), 2866–2877
  • Organic Computing for Video Analysis
    Würtz, R. P.
    In A. König, Köppen, M., Abraham, A., Igel, C., & Kasabov, N. (Eds.), Seventh International Conference on Hybrid Intelligent Systems, Kaiserslautern, Germany (pp. 6–11) IEEE Computer Society Press
    pdf

2006

  • Analytical derivation of complex cell properties from the slowness principle
    Sprekeler, H., & Wiskott, L.
    In Proc. 2nd Bernstein Symposium for Computational Neuroscience, Oct 1–3, Berlin, Germany (p. 67) Bernstein Center for Computational Neuroscience (BCCN) Berlin
  • Patterns of cortical reorganization parallel impaired tactile discrimination and pain intensity in complex regional pain syndrome
    Pleger, B., Ragert, P., Schwenkreis, P., Forster, A. F., Wilimzig, C., Dinse, H., et al.
    Neuroimage, 32(2), 503–510
  • Tactile coactivation resets age-related decline of human tactile discrimination
    Dinse, H. R., Kleibel, N., Kalisch, T., Ragert, P., Wilimzig, C., & Tegenthoff, M.
    Ann. Neurol., 60(1), 88–94
  • Analysis of Cluttered Scenes Using an Elastic Matching Approach for Stereo Images
    Eckes, C., Triesch, J., & von der Malsburg, C.
    Neural Computation, 18(6), 1441–1471
  • Modeling Sensorimotor Learning with Linear Dynamical Systems
    Cheng, S., & Sabes, P. N.
    Neural Computation, 18(4), 760–793
    Cheng, Sabes 2006
  • Spastic paresis after perinatal brain damage in rats is reduced by human cord blood mononuclear cells
    Meier, C., Middelanis, J., Wasielewski, B., Neuhoff, S., Roth-Haerer, A., Gantert, M., et al.
    Pediatr. Res., 59(2), 244–249
  • A sequence-encoding neural network for face recognition
    Barwiṅski, M., & Würtz, R. P.
    In M. Verleysen (Ed.), Proceedings of ESANN, Bruges, Belgium (pp. 635–640) d-side publications, Evere, Belgium
    pdf
  • Syndrome identification based on 2D analysis software
    Böhringer, S., Vollmar, T., Tasse, C., Würtz, R. P., Gillessen-Kaesbach, G., Horsthemke, B., & Wieczorek, D.
    European Journal of Human Genetics, 14(10), 1082–1089
    pdf
  • Cortical reorganization in the aging brain
    Dinse, H. R.
    Prog. Brain Res., 157, 57–80
  • Degeneracy in Model Selection for SVMs with Radial Gaussian Kernel
    Glasmachers, T.
    In M. Verleysen (Ed.), Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN) d-side publications
    pdf
  • Maximum-Gain Working Set Selection for Support Vector Machines
    Glasmachers, T., & Igel, C.
    Journal of Machine Learning Research, 7, 1437–1466
    pdf jmlr.org
  • Graphs with Principal Components of Gabor Wavelet Features for Improved Face Recognition
    Heinrichs, A., Müller, M. K., Tewes, A. H. J., & , R. P. W.
    In G. Cristóbal, Javidi, B., & Vallmitjana, S. (Eds.), Information Optics: 5th International Workshop on Information Optics; WIO′06 (pp. 243–252) American Institute of Physics
    pdf
  • Supplementing Bundle Adjustment with Evolutionary Algorithms
    Heyden, L., Würtz, R. P., & Peters, G.
    In Proceedings of the IET International Conference on Visual Information Engineering 2006 (VIE 2006) (pp. 533–536) Institution of Engineering and Technology
    pdf
  • Age-related attenuation of dominant hand superiority
    Kalisch, T., Wilimzig, C., Kleibel, N., Tegenthoff, M., & Dinse, H. R.
    PLoS ONE, 1, e90
  • Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts
    Mersch, B., Glasmachers, T., Meinicke, P., & Igel, C.
    In Proceedings of the 16th International Conference on Artificial Neural Networks (ICANN) Springer-Verlag
    pdf
  • A sensor for dynamic tactile information with applications in human-robot interaction and object exploration
    Schmidt, P. A., Maël, E., & Würtz, R. P.
    Robotics and Autonomous Systems, 54(12), 1005–1014
    pdf
  • Analytical derivation of complex cell properties from the slowness principle
    Sprekeler, H., & Wiskott, L.
    In Proc. Berlin Neuroscience Forum, Jun 8–10, Bad Liebenwalde, Germany (pp. 65–66) Berlin: Max-Delbrück-Centrum für Molekulare Medizin (MDC)
  • Analytical derivation of complex cell properties from the slowness principle
    Sprekeler, H., & Wiskott, L.
    In Proc. 15th Annual Computational Neuroscience Meeting (CNS′06), Jul 16–20, Edinburgh, Scotland
  • Feature-driven emergence of model graphs for object recognition and categorization
    Westphal, G., von der Malsburg, C., & Würtz, R. P.
    In K. Bellman, Hofmann, P., Müller-Schloer, C., Schmeck, H., & Würtz, R. P. (Eds.), Organic Computing – Controlled Emergence Internationales Begegnungs- und Forschungszentrum (IBFI), Schloss Dagstuhl, Germany

2005

  • Improvement of tactile discrimination performance and enlargement of cortical somatosensory maps after 5 Hz rTMS
    Tegenthoff, M., Ragert, P., Pleger, B., Schwenkreis, P., Forster, A. F., Nicolas, V., & Dinse, H. R.
    PLoS Biol., 3(11), e362
  • ARCS 2005 - System Aspects in Organic and Pervasive Computing - Workshops Procedeedings, Innsbruck Austria, March 14-17
    Brinkschulte, U., Becker, J., Fey, D., Hochberger, C., Martinetz, T., Müller-Schloer, C., et al.
    VDE Verlag, Berlin, Offenbach
  • Gradient-based Adaptation of General Gaussian Kernels
    Glasmachers, T., & Igel, C.
    Neural Computation, 17(10), 2099–2105
    pdf
  • Strategies and Benefits of Fusion of 2D and 3D face recognition
    Hüsken, M., Brauckmann, M., Gehlen, S., & von der Malsburg, C.
    In Proceedings of the IEEE workshop on Face Recognition Grand Challenge Experiments at CVPR 2005
  • A flexible object model for recognising and synthesising facial expressions
    Tewes, A., Würtz, R. P., & von der Malsburg, C.
    In T. Kanade, Ratha, N., & Jain, A. (Eds.), Proceedings of the International Conference on Audio- and Video-based Biometric Person Authentication (pp. 81–90) Springer
    pdf
  • Organic Computing methods for face recognition
    Würtz, R. P.
    it - Information Technology, 47(4), 207–211
    pdf

2004

  • Maplets for correspondence-based object recognition
    Zhu, J., & von der Malsburg, C.
    Neural Networks, 17(8-9), 1311–1326
  • Statistical and dynamic models of charge balance functions
    Cheng, S., Petriconi, S., Pratt, S., Skoby, M., Gale, C., Jeon, S., et al.
    Phys. Rev. C, 69(5), 054906
  • Rapid Processing and Unsupervised Learning in a Model of the Cortical Macrocolumn
    Lücke, J., & von der Malsburg, C.
    Neural Computation, 16(3), 501–533
  • The role of action plans and other cognitive factors in motion extrapolation: A modelling study
    Erlhagen, W., & Jancke, D.
    Visual Cognition, 11(2-3), 315–340
  • Imaging cortical correlates of illusion in early visual cortex
    Jancke, D., Chavane, F., Naaman, S., & Grinvald, A.
    Nature, 428(6981), 423–426
  • Shorter latencies for motion trajectories than for flashes in population responses of cat primary visual cortex
    Jancke, D., Erlhagen, W., Schöner, G., & Dinse, H. R.
    The Journal of Physiology, 556(3), 971–982
  • Communicating agents architecture with applications in multimodal human computer interaction
    Krüger, M., Schäfer, A., Tewes, A., & Würtz, R. P.
    In P. Dadam & Reichert, M. (Eds.), Informatik 2004 (Vol. 2, pp. 641–645) Gesellschaft für Informatik
    pdf
  • Face Authentication Test on the BANCA Database
    Messer, K., Kittler, J., Sadeghi, M., Hamouz, M., Kostin, A., Cardinaux, F., et al.
    In Proceedings of ICPR 2004, Cambridge (Vol. 4, pp. 523–532)
    pdf
  • Aktuelles Schlagwort: Organic Computing
    Müller-Schloer, C., von der Malsburg, C., & Würtz, R. P.
    Informatik Spektrum, 27(4), 332–336
    pdf
  • Superior tactile performance and learning in professional pianists: evidence for meta-plasticity in musicians
    Ragert, P., Schmidt, A., Altenmuller, E., & Dinse, H. R.
    Eur. J. Neurosci., 19(2), 473–478
  • Self-Organization: The Unfinished Revolution
    von der Malsburg, C.
    In W. Greiner & Reinhardt, J. (Eds.), Idea-Finding Symposium Frankfurt Institute for Advanced Studies (pp. 127–138) EP Systema, Debrecen, Hungary
  • Vision as an exercise in Organic Computing
    von der Malsburg, C.
    In P. Dadam & Reichert, M. (Eds.), Informatik 2004 (Vol. 2, pp. 631–635)
  • Fast object and pose recognition through minimum entropy coding
    Westphal, G., & Würtz, R. P.
    In 17th International Conference on Pattern Recognition (ICPR 2004), Cambridge (Vol. 3, pp. 53–56) IEEE Press
    pdf
  • Image Representation by Complex Cell Responses
    Wundrich, I. J., von der Malsburg, C., & Würtz, R. P.
    Neural Computation, 16(12), 2563–2575
    pdf
  • Organic Computing for face and object recognition
    Würtz, R. P.
    In P. Dadam & Reichert, M. (Eds.), Informatik 2004 (Vol. 2, pp. 636–640) Gesellschaft für Informatik
    pdf

2003

  • Functional imaging of perceptual learning in human primary and secondary somatosensory cortex
    Pleger, B., Foerster, A. F., Ragert, P., Dinse, H. R., Schwenkreis, P., Malin, J. P., et al.
    Neuron, 40(3), 643–653
  • Pharmacological modulation of perceptual learning and associated cortical reorganization
    Dinse, H. R., Ragert, P., Pleger, B., Schwenkreis, P., & Tegenthoff, M.
    Science, 301(5629), 91–94
  • Removing distortions from charge balance functions
    Pratt, S., & Cheng, S.
    Phys. Rev. C, 68(1), 014907
  • Isospin fluctuations from a thermally equilibrated hadron gas
    Cheng, S., & Pratt, S.
    Phys. Rev. C, 67(4), 044904
  • Image processing and behavior planning for intelligent vehicles
    Bucher, T., Curio, C., Edelbrunner, J., Igel, C., Kastrup, D., Leefken, I., et al.
    IEEE Transactions on Industrial electronics, 50(1), 62–75
  • Computer-based recognition of dysmorphic faces
    Loos, H. S., Wieczorek, D., Würtz, R. P., von der Malsburg, C., & Horsthemke, B.
    European Journal of Human Genetics, 11, 555–560
    pdf
  • Extraction and Matching of Symbolic Contour Graphs
    Lourens, T., & Würtz, R. P.
    International Journal of Pattern Recognition and Artificial Intelligence, 17(7), 1279–1302
    pdf
  • Learning the Gestalt Rule of Collinearity from Object Motion
    Prodöhl, C., Würtz, R. P., & von der Malsburg, C.
    Neural Computation, 15(8), 1865–1896
    pdf
  • European Patent Number: EP 1 142 118 B1 Tastsensor
    Schmidt, P., Maël, E., & Würtz, R. P.
    Europäisches Patentamt, München
    pdf
  • US Patent Number: 6 593 756 Tactile sensor
    Schmidt, P., Maël, E., & Würtz, R. P.
    United States Patent and Trademark Office
    pdf
  • Conference Report: 4th Workshop on Dynamic Perception
    Würtz, R. P.
    KI - Künstliche Intelligenz, 17(2), 38
    pdf

2002

  • Classification of hand postures against complex backgrounds using elastic graph matching
    Triesch, J., & von der Malsburg, C.
    Image and Vision Computing, 20(13-14), 937–943
  • Age-related changes in primary somatosensory cortex of rats: evidence for parallel degenerative and plastic-adaptive processes
    Godde, B., Berkefeld, T., David-Jurgens, M., & Dinse, H. R.
    Neurosci Biobehav Rev, 26(7), 743–752
  • Aging of the brain, sensorimotor, and cognitive processes
    Li, S. C., & Dinse, H. R.
    Neurosci Biobehav Rev, 26(7), 729–732
  • Dynamic Perception
    Würtz, R. P., & Lappe_(eds.), M.
    infix Verlag/IOS press, Berlin, Amsterdam
    pdf
  • The role of complex cells in object recognition
    Shams, L., & von der Malsburg, C.
    Vision Research, 42(22), 2547–2554
  • Acquisition of visual shape primitives
    Shams, L., & von der Malsburg, C.
    Vision Research, 42(17), 2105–2122
  • Plasticity of orientation preference maps in the visual cortex of adult cats
    Godde, B., Leonhardt, R., Cords, S. M., & Dinse, H. R.
    Proc. Natl. Acad. Sci. U.S.A., 99(9), 6352–6357
  • How to measure the pose robustness of object views
    Peters, G., Zitova, B., & von der Malsburg, C.
    Image and Vision Computing, 20(5-6), 341–348
  • Application-specific biometric templates
    Braithwaite, M., von Seelen, U. C., Cambier, J., Daugman, J., Glass, R., Moore, R., & Scott, I.
    AutoID02, 167–171
  • Statistical physics in a finite volume with absolute conservation laws
    Statistical Physics in a Finite Volume with Absolute Conservation Laws
  • Modeling Relativistic Heavy Ion Collisions
    MODELING RELATIVISTIC HEAVY ION COLLISIONS
  • Effect of finite-range interactions in classical transport theory
    Cheng, S., Pratt, S., Csizmadia, P., Nara, Y., Molnár, D., Gyulassy, M., et al.
    Phys. Rev. C, 65(2), 024901
  • Plastic-adaptive properties of cortical areas
    Dinse, H. R., & Böhmer, G.
    In A. Schüz & Miller, R. (Eds.), Cortical Areas: Unity and Diversity: Conceptual Advances in Brain Research (pp. 311–348) London : Taylor & Francis
  • Adaptation of Inputs in the Somatosensory System
    Dinse, H. R., & Merzenich, M. M.
    In M. Fahle & Poggio, T. (Eds.), Perceptual Learning (pp. 19–42) : MIT Press
  • Do primary sensory areas play homologous roles in different sensory modalities?
    Dinse, H. R., & Schreiner, C. E.
    In A. Schüz & Miller, R. (Eds.), Cortical Areas: Unity and Diversity: Conceptual Advances in Brain Research (pp. 273–310) London : Taylor & Francis
  • Statistical Learning of the Detection of Faces in Natural Images
    Heinrichs, A., Eckes, C., Würtz, R. P., & von der Malsburg, C.
    In R. P. Würtz & Lappe, M. (Eds.), Dynamic Perception (pp. 265–270) infix Verlag/IOS press
    pdf
  • Evolving field models for inhibition effects in early vision
    Igel, C., von Seelen, W., Erlhagen, W., & Jancke, D.
    Neurocomputing, 44, 467–472
  • 1-Click Learning of Object Models for Recognition
    Loos, H. S., & von der Malsburg, C.
    In H. H. Bülthoff, Lee, S. -W., Poggio, T. A., & Wallraven, C. (Eds.), Biologically Motivated Computer Vision (pp. 377–386) Springer
  • Macrocolumns as decision units
    Lücke, J., von der Malsburg, C., & Würtz, R. P.
    In International Conference on Artificial Neural Networks (pp. 57–62) Springer
    pdf
  • Timing, Clocks, and Dynamical Systems
    Schöner, G.
    Brain and Cognition, 48, 31–51
    reprint
  • Acquisition of visual shape primitives
    Shams, L., & von der Malsburg, C.
    Vision Research, 42(17), 2105–2122
  • Dynamic link architecture
    von der Malsburg, C.
    In M. A. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks (2.nd ed., pp. 365–367) MIT Press
  • Self-organization in the brain
    von der Malsburg, C.
    In M. A. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks (2.nd ed., pp. 1002–1005) MIT Press
  • How are Neural Signals Related to Each Other and to the World
    von der Malsburg, C.
    Journal of Consciousness Studies, 9(1), 47–60
  • Gabor-based Feature Point Tracking with Automatically Learned Constraints
    Wieghardt, J., Würtz, R. P., & von der Malsburg, C.
    In R. P. Würtz & Lappe, M. (Eds.), Dynamic Perception (pp. 121–126) infix Verlag/IOS press
    pdf
  • Learning the Topology of Object Views
    Wieghardt, J., Würtz, R. P., & von der Malsburg, C.
    In A. Heyden, Sparr, G., Nielsen, M., & Johansen, P. (Eds.), Computer Vision - ECCV 2002 (p. IV-747-IV-760) Springer
    pdf
  • Image Reconstruction from Gabor Magnitudes
    Wundrich, I. J., von der Malsburg, C., & Würtz, R. P.
    In H. H. Bülthoff, Lee, S. -W., Poggio, T. A., & Wallraven, C. (Eds.), Biologically Motivated Computer Vision (pp. 117–126) Springer
    pdf
  • Face Recognition, Neurophysiology, and Neural technology
    Würtz, R. P.
    In M. A. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks (2.nd ed., pp. 434–437) MIT Press
    pdf
  • Technik und Leistungsfähigkeit automatischer Gesichtserkennung
    Würtz, R. P.
    FIfF-Kommunikation, 19(1), 27–30
    pdf
  • Vision and Touch for Grasping
    Würtz, R. P.
    In G. D. Hager, Christensen, H. I., Bunke, H., & Klein, R. (Eds.), Sensor Based Intelligent Robots (pp. 74–86) Springer
    pdf

2001

  • Shifts in cortical representations predict human discrimination improvement
    Pleger, B., Dinse, H. R., Ragert, P., Schwenkreis, P., Malin, J. P., & Tegenthoff, M.
    Proc. Natl. Acad. Sci. U.S.A., 98(21), 12255–12260
  • View Reconstruction by Linear Combination of Sample Views
    Peters, G., & von der Malsburg, C.
    In British Machine Vision Conference 2001 (BMVC:2001) Manchester, UK
  • Democratic Integration: Self-Organized Integration of Adaptive Cues
    Triesch, J., & von der Malsburg, C.
    Neural Computation, 13(9), 2049–2074
  • Quantum corrections for pion correlations involving resonance decays
    Cheng, S., & Pratt, S.
    Phys. Rev. C, 63(5), 054904
  • Interpolation of Novel Object Views from Sample Views
    Peters, G., & von der Malsburg, C.
    In M. F. Sebaaly (Ed.), Proceedings of the International NAISO Congress on Information Science Innovations (ISI′2001) (pp. 800–805) Dubai, U.A.E.: International Computing Science Conventions (ICSC), ICSC Academic Press, Canada/The Netherlands
  • Chapter 10: Comparative population analysis of cortical representations in parametric spaces of visual field and skin : a unifying role for nonlinear interactions as a basis for active information processing across modalities
    Dinse, H. R., & Jancke, D.
    In M. A. L. Nicolelis (Ed.), Advances in neural population coding (pp. 155–173) Elsevier
    pdf
  • Comparative population analysis of cortical representations in parametric spaces of visual field and skin: A unifying role for nonlinear interactions as a basis for active information processing across modalities
    Dinse, H. R., & Jancke, D.
    Progress in brain research, 130, 155–173
  • Time-variant processing in V1: From microscopic (single cell) to mesoscopic (population) levels
    Dinse, H. R., & Jancke, D.
    Trends in neurosciences, 24(4), 203–205
  • Optimization of dynamic neural fields
    Igel, C., Erlhagen, W., & Jancke, D.
    Neurocomputing, 36(1), 225–233
  • Efficient evaluation of serial sections by iterative Gabor matching
    König, P., Kayser, C., Bonin, V., & Würtz, R. P.
    Journal of Neuroscience Methods, 111(2), 141–150
    pdf
  • Analysis and synthesis of human faces with pose variations by a parametric piecewise linear subspace method
    Okada, K., & von der Malsburg, C.
    In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 IEEE Comput. Soc
  • A system for person-independent hand posture recognition against complex backgrounds
    Triesch, J., & von der Malsburg, C.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(12), 1449–1453
  • Gabor phase space molecules for image understanding
    Würtz, R. P.
    In I. Wundrich (Ed.), The Mathematical, Computational and Biological Study of Vision (p. 23)
    pdf

2000

  • Target representation on an autonomous vehicle with low-level sensors
    Bicho, E., Mallet, P., & Schöner, G.
    The International Journal of Robotics Research, 19, 424–447
    reprint
  • Walking pedestrian recognition
    Curio, C., Edelbrunner, J., Kalinke, T., Tzomakas, C., & von Seelen, W.
    IEEE Transactions on Intelligent Transportation Systems, 1(3), 155–163
  • Orientation formed by a spot’s trajectory: A two-dimensional population approach in primary visual cortex
    Jancke, D.
    J Neurosci, 20(14), U13–U18
  • An iris biometric system for public and personal use
    Negin, M., Chmielewski, T. A., Salganicoff, M., von Seelen, W., Venetainer, P. L., & Zhang, G. G.
    Computer, 33(2), 70–75
  • Pose-Independent Object Representation by 2-D Views
    Wieghardt, J., & von der Malsburg, C.
    In IEEE International Workshop on Biologically Motivated Computer Vision, May 15-17, Seoul
  • Gossiping Nets
    Würtz, R. P.
    Artificial Intelligence, 119(1-2), 295–299
    pdf
  • Corner detection in color images through a multiscale combination of end-stopped cortical cells
    Würtz, R. P., & Lourens, T.
    Image and Vision Computing, 18(6-7), 531–541
    pdf

1999

  • Two Methods for Comparing Different Views of the Same Object
    Peters, G., Zitova, B., & von der Malsburg, C.
    In T.~Pridmore & D.~Elliman, (Eds.), Proceedings of the 10th British Machine Vision Conference (BMVC′99) (Vol. 2, pp. 493–502) Nottingham, UK: University of Nottingham
  • The What and Why of Binding
    von der Malsburg, C.
    Neuron, 24(1), 95–104
  • Automatic Video Indexing with Incremental Gallery Creation: Integration of Recognition and Knowledge Acquisition
    Okada, K., & von der Malsburg, C.
    In Proceedings of Third International Conference on Knowledge-Based Intelligent Information Engineering Systems (pp. 431–434) Adelaide
  • GripSee: A Gesture-controlled Robot for Object Perception and Manipulation
    Becker, M., Kefalea, E., Maël, E., von der Malsburg, C., Pagel, M., Triesch, J., et al.
    Autonomous Robots, 6(2), 203–221
    pdf
  • Complex behavior by means of dynamical systems for an anthropomorphic robot
    Bergener, T., Bruckhoff, C., Dahm, P., Janßen, H., Joublin, F., Menzner, R., et al.
    Neural Networks, 12(7-8), 1087–1099
  • The distribution of neuronal population activation (DPA) as a tool to study interaction and integration in cortical representations
    Erlhagen, W., Bastian, A., Jancke, D., Riehle, A., & Schöner, G.
    Journal of Neuroscience Methods, 94(1), 53–66
  • Parametric population representation of retinal location: Neuronal interaction dynamics in cat primary visual cortex
    Jancke, D., Erlhagen, W., Dinse, H. R., Akhavan, A. C., Giese, M., Steinhage, A., & Schöner, G.
    J Neurosci, 19(20), 9016–9028
  • On generating FC/sup 3/ fuzzy rule systems from data using evolution strategies
    Jin, Y., von Seelen, W., & Sendhoff, B.
    IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 29(6), 829–845
  • An Integrated Object Representation for Recognition and Grasping
    Kefalea, E., Maël, E., & Würtz, R. P.
    In L. C. Jain (Ed.), Proceedings of the Third International Conference on Knowledge-based Intelligent Information Engineering Systems, Adelaide, Australia (pp. 423–426) IEEE Press
    pdf
  • Neural networks as a model for visual perception: what is lacking?
    Würtz, R. P.
    Cognitive Systems, 5(2), 103–112
    pdf
  • On the performance of neuronal matching algorithms
    Würtz, R. P., Konen, W., & Behrmann, K. -O.
    Neural Networks, 12(1), 127–134
    pdf
  • Robust Detection of Significant Points in Multiframe Images
    Zitova, B., Kautsky, J., Peters, G., & Flusser, J.
    Pattern Recognition Letters, 20(2), 199–206

1998

  • GripSee: A Robot for Visually-Guided Grasping
    Becker, M., Kefalea, E., Maël, E., von der Malsburg, C., Pagel, M., Triesch, J., et al.
    In L. Niklasson, Bodén, M., & Ziemke, T. (Eds.), Proceedings of the ICANN′98, International Conference on Artificial Neural Networks (pp. 431–436) Springer
  • Using attractor dynamics to control autonomous vehicle motion
    Bicho, E., Mallet, P., & Schöner, G.
    In Proceedings of IECON′98 (pp. 1176–1181) IEEE Industrial Electronics Society
    reprint
  • Computer vision for driver assistance systems
    Handmann, U., Kalinke, T., Tzomakas, C., Werner, M., & von Seelen, W.
    In Enhanced and Synthetic Vision 1998 (Vol. 3364, pp. 136–148) International Society for Optics and Photonics
  • Fusion of different sensors and algorithms for segmentation
    Handmann, U., Lorenz, G., Schnitger, T., & von Seelen, W.
    IV, 98, 499–504
  • Fusion of different sensors and algorithms for segmentation
    Handmann, U., Lorenz, G., Schnitger, T., & von Seelen, W.
    IV, 98, 499–504
  • Fusion von Basisalgorithmen zur Segmentierung von Straßenverkehrsszenen
    Handmann, U., Lorenz, G., & von Seelen, W.
    In Mustererkennung 1998 (pp. 101–108) Springer
  • An approach to rule-based knowledge extraction
    Jin, Y., von Seelen, W., & Sendhoff, B.
    In Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on (Vol. 2, pp. 1188–1193) IEEE
  • A texture-based object detection and an adaptive model-based classification
    Kalinke, T., Tzomakas, C., & von Seelen, W.
    In Procs. IEEE Intelligent Vehicles Symposium (Vol. 98, pp. 341–346) Citeseer
  • Positionsvorhersage von bewegten Objekten in großformatigen Bildsequenzen
    Loos, H. S., Fritzke, B., & von der Malsburg, C.
    In S. Posch & Ritter, H. (Eds.), Proceedings in Artificial Intelligence: Dynamische Perzeption, Juni 18–19, Bielefeld, Germany (pp. 31–38) Infix Verlag
  • Object Recognition by matching symbolic edge graphs
    Lourens, T., & Würtz, R. P.
    In R. Chin & Pong, T. -C. (Eds.), Computer Vision – ACCV′98 (Vol. 1352, p. II-193 - II-200) Springer Verlag
    pdf
  • The Bochum/USC Face Recognition System and how it Fared in the FERET Phase III test
    Okada, K., Steffens, J., Maurer, T., Hong, H., Elagin, E., Neven, H., & von der Malsburg, C.
    In H. Wechsler, Phillips, P. J., Bruce, V., Fogelman-Soulie, F., & Huang, T. S. (Eds.), Face Recognition: From Theory to Applications (pp. 186–205) Springer-Verlag
  • Self Calibration of the Fixation Movement of a Stereo Camera Head
    Pagel, M., Maël, E., & von der Malsburg, C.
    Machine Learning, 31(1/3), 169–186
  • Self Calibration of the Fixation Movement of a Stereo Camera Head
    Pagel, M., Maël, E., & von der Malsburg, C.
    Autonomous Robots, 5(3/4), 355–367
  • A Gesture Interface for Robotics
    Triesch, J., & von der Malsburg, C.
    In FG′98, the Third International Conference on Automatic Face and Gesture Recognition (pp. 546–551) IEEE Computer Society Press
  • Biologically Inspired Methods for Model Matching and Object Recognition
    Würtz, R. P.
    In Y. Sawada (Ed.), Proceedings of the 2nd R.I.E.C. International Symposium on Design and Architecture of Information Processing Systems Based on the Brain Information Principles, in Sendai, Japan, March 16-18, 1998 (pp. 101–106)
    pdf

1997

  • A condition for the genotype-phenotype mapping: Causality
    Sendhoff, B., Kreutz, M., & von Seelen, W.
    Advances in Astrophysics
  • The dynamic approach to autonomous robotics demonstrated on a low-level vehicle platform
    Bicho, E., & Schöner, G.
    Robotics and autonomous systems, 21, 23–35
    reprint
  • Object Classification Based on Contours with Elastic Graph Matching
    Kefalea, E., Rehse, O., & von der Malsburg, C.
    In C. Arcelli, Cordella, L. P., & di Baja, G. S. (Eds.), Proc. 3rd Int. Workshop on Visual Form, Capri, Italy (pp. 287–297) World Scientific
  • Tracking Facial Feature Points with Gabor Wavelets and Shape Models
    McKenna, S. J., Gong, S., Würtz, R. P., Tanner, J., & Banin, D.
    In J. Bigün, Chollet, G., & Borgefors, G. (Eds.), Proceedings of the First International Conference on Audio- and Video-based Biometric Person Authentication (Vol. 1206, pp. 35–42) Springer
    pdf
  • Development of Shape Primitives from Images of Composite Objects Represented by Complex Cells
    Shams, L., & von der Malsburg, C.
    In W. Gerstner, Germond, A., Hasler, M., & Nicoud, J. -D. (Eds.), Artificial Neural Networks – ICANN ′97 (Vol. 1327, pp. 895–900) Springer Verlag
  • The Coherence Definition of Consciousness
    von der Malsburg, C.
    In M. Ito, Miyashita, Y., & E.T.Rolls, (Eds.), Cognition, Computation and Consciousness (pp. 193–204) Oxford University Press
  • Context dependent feature groups, a proposal for object representation
    Würtz, R. P.
    Behavioral and Brain Sciences, 20(4), 702–703
    pdf
  • Neuronal theories and technical systems for face recognition
    Würtz, R. P.
    In M. Verleysen (Ed.), Proceedings of the Fifth European Symposium On Artificial Neural Networks, Bruges (Belgium), 16-18 April 1997 (pp. 73–78) D facto, Brussels
    pdf
  • Object recognition robust under translations, deformations and changes in background
    Würtz, R. P.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 769–775
    pdf
  • Corner detection in color images by multiscale combination of end-stopped cortical cells
    Würtz, R. P., & Lourens, T.
    In W. Gerstner, Germond, A., Hasler, M., & Nicoud, J. -D. (Eds.), Artificial Neural Networks - ICANN ′97 (Vol. 1327, pp. 901–906) Springer Verlag
    pdf

1996

  • Artificial Neural Networks – ICANN 96
    von der Malsburg, C., Vorbrüggen, J. C., von Seelen, W., & Sendhoff (Eds.), B.
    (C. von der Malsburg, Vorbrüggen, J. C., von Seelen, W., & Sendhoff, B., Eds.), Lecture Notes in Computer Science (Vol. 1112) Springer Verlag
  • Population coding in cat visual cortex reveals nonlinear interactions as predicted by a neural field model
    Jancke, D., Akhavan, A. C., Erlhagen, W., Giese, M., Steinhage, A., Schöner, G., & Dinse, H. R.
    In Artificial Neural Networks — ICANN 96 (pp. 641–648) Springer Berlin Heidelberg
  • Entropie als Maß des lokalen Informationsgehalts in Bildern zur Realisierung einer Aufmerksamkeitssteuerung
    Kalinke, T., & von Seelen, W.
    In Informatik aktuell (pp. 627–634) Springer Berlin Heidelberg
  • Tracking and Learning Graphs and Pose on Image Sequences of Faces
    Maurer, T., & von der Malsburg, C.
    In Proceedings of the 2nd International Conference on Automatic Face- and Gesture- Recognition (pp. 176–181) Killington, Vermont, USA
  • Improving Object Recognition by Transforming Gabor Filter responses
    Pötzsch, M., Krüger, N., & von der Malsburg, C.
    Network: Computation in Neural Systems, 7(2), 341–347
  • Edge Information: A Confidence Based Algorithm Emphazising Continuous Curves
    Rehse, O., Pötzsch, M., & von der Malsburg, C.
    In C.~v.d.~Malsburg, W.~v.~Seelen,, Vorbrüggen, J. C., & Sendhoff, B. (Eds.), Proc. ICANN 96 (pp. 845–850) Springer Verlag, Berlin, Heidelberg, New York
  • Robust Classification of Hand Postures against Complex Backgrounds
    Triesch, J., & von der Malsburg, C.
    In Proceedings of the Second International Conference on Automatic Face and Gesture Recognition (pp. 170–175) IEEE Computer Society Press
  • How fast can neuronal algorithms match patterns?
    Würtz, R. P., Konen, W., & Behrmann, K. -O.
    In C. von der Malsburg, Vorbrüggen, J. C., von Seelen, W., & Sendhoff, B. (Eds.), Artificial Neural Networks - ICANN 96 (Vol. 1112, pp. 145–150) Springer Verlag
    pdf

1995

  • Patent Nummer 4406020: Verfahren zur automatisierten Erkennung von Objekten
    Konen, W., Vorbrüggen, J. C., & Würtz, R. P.
    Deutsches Patentamt, München
  • Single-View Based Recognition of Faces Rotated in Depth
    Maurer, T., & von der Malsburg, C.
    In M. Bichsel (Ed.), Proceedings of IWAFGR95, Zürich, June 1995 (pp. 248–253)
  • Dynamics of behavior: Theory and applications for autonomous robot architectures
    Schöner, G., Dose, M., & Engels, C.
    Robotics and Autonomous Systems, 16, 213–245
    SchonerDoseEngels95.pdf
  • Binding in models of perception and brain function
    von der Malsburg, C.
    Current Opinion in Neurobiology, 5, 520–526
  • Pose Invariant Object Recognition in a Neural System
    von der Malsburg, C., & Reiser, K.
    In F. Fogelmann-Soulié, Rault, J. C., Gallinari, P., & Dreyfus, G. (Eds.), Proceedings of the International Conference on Artificial Neural Networks ICANN ′95 (pp. 127–132) EC2 & Cie, Paris
  • Multilayer Dynamic Link Networks for Establishing Image Point Correspondences and Visual Object Recognition
    Würtz, R. P.
    (p. 155) Thun, Frankfurt am Main: Verlag Harri Deutsch
    pdf
  • Building Visual Correspondence Maps—From Neuronal Dynamics To A Face Recognition System
    Würtz, R. P.
    In In Proceedings of the International Conference on Brain Processes, Theories and Models
    pdf

1994

  • Image Point Correspondences from a Wavelet Representation and a Hierarchical Dynamic Link Network
    Würtz, R. P., & von der Malsburg, C.
    In T. Smithers & Moreno, A. (Eds.), The Role of Dynamics and Representation in Adaptive Behavior and Cognition (pp. 200–202)
    pdf
  • A Fast Dynamic Link Matching Algorithm for Invariant Pattern Recognition
    Konen, W., Maurer, T., & von der Malsburg, C.
    Neural Networks, 7(6/7), 1019–1030

1993

  • Learning to generalize from single examples in the dynamic link architecture
    Konen, W., & von der Malsburg, C.
    Neural Computation, 5, 719–735
  • Distortion Invariant Object Recognition in the Dynamic Link Architecture
    Lades, M., Vorbrüggen, J. C., Buhmann, J., Lange, J., von der Malsburg, C., Würtz, R. P., & Konen, W.
    IEEE Transactions on Computers, 42(3), 300–311
    pdf (remastered with errata)

1992

  • Gesichtserkennung mit dynamischen neuronalen Netzen
    Würtz, R. P.
    Spektrum der Wissenschaft, 18–22
  • Object Recognition with Gabor Functions in the Dynamic Link Architecture – Parallel Implementation on a Transputer Network
    Buhmann, J., Lange, J., von der Malsburg, C., Vorbrüggen, J. C., & Würtz, R. P.
    In B. Kosko (Ed.), Neural Networks for Signal Processing (pp. 121–159) Prentice Hall, Englewood Cliffs, NJ
  • Neural Mechanisms of Elastic Pattern Matching
    Doursat, R., Konen, W., Lades, M., von der Malsburg, C., Vorbrüggen, J., Wiskott, L., & Würtz, R.
    In M. van der Meer (Ed.), Statusseminar des BMFT: Neuroinformatik (pp. 71–82) Projektträger Informationstechnik/DLR, Berlin
    pdf
  • Unsupervised symmetry detection: A network which learns from single examples
    Konen, W., & von der Malsburg, C.
    In I. Aleksander (Ed.), Proceedings of the International Conference on Artificial Neural Networks (pp. 121–125) North-Holland, Amsterdam
  • Vision-based car-following: detection, tracking, and identification
    Schwarzinger, M., Zielke, T., Noll, D., Brauckmann, M., & von Seelen, W.
    In Intelligent Vehicles′ 92 Symposium., Proceedings of the (pp. 24–29) IEEE
  • Sensory Segmentation with Coupled Neural Oscillators
    von der Malsburg, C., & Buhmann, J.
    Biological Cybernetics, 67, 233–242
  • Cartrack: computer vision-based car following
    Zielke, T., Brauchkmann, M., & von Seelen, W.
    In Applications of Computer Vision, Proceedings, 1992., IEEE Workshop on (pp. 156–163) IEEE
  • Intensity and edge-based symmetry detection applied to car-following
    Zielke, T., Brauckmann, M., & von Seelen, W.
    In European Conference on Computer Vision (pp. 865–873) Springer

1991

  • Recursive tracking of image points using labelled graph matching
    Chandrashekhar, S., von der Malsburg, C., & Chellappa, R.
    In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics; Charlottesville, Virginia
  • Sensory Segmentation by Neural Oscillators
    Buhmann, J., & von der Malsburg, C.
    In Proceedings IJCNN, Seattle
  • Künstliche Intelligenz-Perspektiven einer wissenschaftlichen Disziplin und Realisierungsmöglichkeiten
    Barth, G., Christaller, T., Cremers, A. B., Neumann, B., Radermacher, F. J., Radig, B., et al.
    Informatik Spektrum: Vol. 14, No. 4
  • Recognizing Faces with a Transputer farm
    Lades, M., Vorbrüggen, J. C., & Würtz, R. P.
    In T. S. Durrani, Sandham, W. A., Soraghan, J. J., & Forbes, S. M. (Eds.), Applications of Transputers~3 (pp. 148–153) IOS Press; Amsterdam, Oxford, Washington, Tokio
    pdf
  • Topographic mapping for stereo and motion processing
    Mallot, H. A., Zielke, T., Storjohann, K., & von Seelen, W.
    In Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods (Vol. 1382, pp. 397–409) International Society for Optics and Photonics
  • Bilderkennung mit dynamischen Neuronennetzen
    von der Malsburg, C., Würtz, R. P., & Vorbrüggen, J. C.
    In W. Brauer & Hernández, D. (Eds.), Verteilte Künstliche Intelligenz und kooperatives Arbeiten (pp. 519–529) Springer
  • A Transputer-based Neural Object Recognition System
    Würtz, R. P., Vorbrüggen, J. C., von der Malsburg, C., & Lange, J.
    In H. Burkhardt, Neuvo, Y., & Simon, J. C. (Eds.), From Pixels to Features II - Parallelism in Image Processing (pp. 275–294) North Holland, Amsterdam

1990

  • Size and Distortion Invariant Object Recognition by Hierarchical Graph Matching
    Buhmann, J., Lades, M., & von der Malsburg, C.
    In Proceedings of the IJCNN International Joint Conference on Neural Networks (pp. II 411–416) San Diego: IEEE
  • Neural mapping and space-variant image processing
    Mallot, H. A., von Seelen, W., & Giannakopoulos, F.
    Neural Networks, 3(3), 245–263
  • Tissue type imaging—an approach to clinical use
    Meindl, S., Jungke, M., Bielke, G., Grigat, M., von Seelen, W., Pedrosa, P., & Higer, H. P.
    In Tissue Characterization in MR Imaging (pp. 174–182) Springer
  • Considerations for a Visual Architecture
    von der Malsburg, C.
    In R. Eckmiller (Ed.), Advanced Neural Computers (pp. 303–312) Amsterdam: North-Holland
  • A neural architecture for the representation of scenes
    von der Malsburg, C.
    In J. L. McGaugh, Weinberger, N. M., & Lynch, G. (Eds.), Brain Organization and Memory: Cells, Systems and Circuits (pp. 356–372) New York: Oxford University Press
  • Network Self-Organization
    von der Malsburg, C.
    In S. F. Zornetzer, Davis, J., & Lau, C. (Eds.), An Introduction to Neural and Electronic Networks (1stst ed., pp. 421–432) Academic Press
  • Pattern Segmentation in Associative Memory
    Wang, D. L., Buhmann, J., & von der Malsburg, C.
    Neural Computation, 2, 94–106
  • A Transputer System for the Recognition of Human Faces by Labeled Graph Matching
    Würtz, R. P., Vorbrüggen, J. C., & von der Malsburg, C.
    In R. Eckmiller, Hartmann, G., & Hauske, G. (Eds.), Parallel Processing in Neural Systems and Computers (pp. 37–41) North Holland, Amsterdam
  • Adapting computer vision systems to the visual environment: topographic mapping
    Zielke, T., Storjohann, K., Mallot, H. A., & von Seelen, W.
    In European Conference on Computer Vision (pp. 613–615) Springer

1986

  • A stochastic theory of phase transitions in human hand movement
    Schöner, G., Haken, H., & Kelso, J. A. S.
    Biological Cybernetics, 53, 247–257
    pdf bib

About the INI

The Institut für Neuroinformatik (INI) is a research unit of the Faculty of Computer Science at the Ruhr-Universität Bochum. Its scientific goal is to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory and effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental psychology and neurophysiology as well as machine learning, neural artificial intelligence, computer vision, and robotics.

Contact

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210

sekretariat@ini.rub.de
www.ini.rub.de