Prof. Dr. Fred Hamker

Baladron, J., & Hamker, F. H. (2023). Re-Thinking the Organization of Cortico-Basal Ganglia-Thalamo-Cortical Loops. Cognitive Computation. https://doi.org/10.1007/s12559-023-10140-9

Baladron, J., Vitay, J., Fietzek, T., & Hamker, F. H. (2023). The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach. PLOS Computational Biology, 19(4). https://doi.org/10.1371/journal.pcbi.1011024

Burkhardt, M., Bergelt, J., Gönner, L., Dinkelbach, H. Ülo, Beuth, F., Schwarz, A., Bicanski, A., Burgess, N., & Hamker, F. H. (2023). A large-scale neurocomputational model of spatial cognition integrating memory with vision. Neural Networks, 167, 473–488. https://doi.org/10.1016/j.neunet.2023.08.034

Maith, O., Baladron, J., Einhäuser, W., & Hamker, F. H. (2023). Exploration behavior after reversals is predicted by STN-GPe synaptic plasticity in a basal ganglia model. IScience, 26(5). https://doi.org/10.1016/j.isci.2023.106599

Meyer, B., Kanoun, O., Sanseverino, G., Müller, M., Pentzold, C., Bischof, A., & Hamker, F. (2023). (in press). Hybrid societies: Concepts, challenges, and research agenda. In B. Meyer, U. Thomas, & O. Kanoun (Eds.), Hybrid Societies - Humans Interacting with Embodied Technologies (Vol. 1). Springer.

Teichmann, M., Ragni, M., Vitay, J., Gaedke, M., & Hamker, F. H. (2023). (in press). Human-Machine Teaming Agents: A Future Perspective. In B. Meyer, U. Thomas, & O. Kanoun (Eds.), Hybrid Societies - Humans Interacting with Embodied Technologies (Vol. 1). Springer.

Voelcker-Rehage, C., Hamker, F. H., Baladron, J., Rudisch, J., Fietzek, T., & Vitay, J. (2023). (in press). Observational Learning in Humans and Machines. In B. Meyer, U. Thomas, & O. Kanoun (Eds.), Hybrid Societies - Humans Interacting with Embodied Technologies (Vol. 1). Springer.

Farahani, A., Vitay, J., & Hamker, F. H. (2022). Deep Neural Networks for Geometric Shape Deformation. In R. Bergmann, L. Malburg, S. C. Rodermund, & I. J. Timm (Eds.), KI 2022: Advances in Artificial Intelligence. Lecture Notes in Computer Science (Vols. 13404, pp. 90-95). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-031-15791-2_9

Fietzek, T., Dinkelbach, H. Ü., & Hamker, F. H. (2022). ANNarchy - iCub: An Interface for Easy Interaction between Neural Network Models and the iCub Robot. 2022 IEEE 9th International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). https://doi.org/10.1109/CIVEMSA53371.2022.9853699

Krumm, D., Kuske, N., Neubert, M., Buder, J., Hamker, F., & Odenwald, S. (2021). Determining push-off forces in speed skating imitation drills. Sports Engineering Volume, 24, 25. https://doi.org/10.1007/s12283-021-00362-1

Larisch, R., Gönner, L., Teichmann, M., & Hamker, F. (2021). Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity. PLoS Computational Biology, 17(11). https://doi.org/10.1371/journal.pcbi.1009566

Maith, O., Schwarz, A., & Hamker, F. (2021). Optimal attention tuning in a neuro-computational model of the visual cortex–basal ganglia–prefrontal cortex loop. Neural Networks, 142, 534-547. https://doi.org/10.1016/j.neunet.2021.07.008

Novin, S., Fallaha, A., Rashidib, S., Beuth, F., & Hamker, F. (2021). A neuro-computational model of visual attention with multiple attentional control sets. Vision Research, 189, 104-118. https://doi.org/10.1016/j.visres.2021.08.009

Teichmann, M., Larisch, R., & Hamker, F. (2021). Performance of biologically grounded models of the early visual system on standard object recognition tasks. Neural Networks, 144, 210-228. https://doi.org/10.1016/j.neunet.2021.08.009