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ORCIDFred H. Hamker
Person information
- affiliation: Chemnitz University of Technology, Faculty of Computer Science, Germany
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Books and Theses
- 1999
[b1]Fred Henrik Hamker:
Visuelle Aufmerksamkeit und lebenslanges Lernen im Wahrnehmungs-Handlungs-Zyklus. Technische Universität Ilmenau, Germany, 1999, pp. 1-341
Journal Articles
- 2023
- [j37]René Larisch
, Julien Vitay, Fred H. Hamker:
Detecting Anomalies in System Logs With a Compact Convolutional Transformer. IEEE Access 11: 113464-113479 (2023) - [j36]Micha Burkhardt, Julia Bergelt, Lorenz Gönner, Helge Ulo Dinkelbach, Frederik Beuth, Alex Schwarz, Andrej Bicanski, Neil Burgess, Fred H. Hamker:
A large-scale neurocomputational model of spatial cognition integrating memory with vision. Neural Networks 167: 473-488 (2023) - [j35]Javier Baladron, Julien Vitay, Torsten Fietzek, Fred H. Hamker:
The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach. PLoS Comput. Biol. 19(4) (2023) - [j34]Javier Baladron, Julien Vitay, Torsten Fietzek, Fred H. Hamker:
Correction: The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach. PLoS Comput. Biol. 19(6) (2023) - 2022
- [j33]Oliver Maith, Helge Ulo Dinkelbach, Javier Baladron, Julien Vitay, Fred H. Hamker:
BOLD Monitoring in the Neural Simulator ANNarchy. Frontiers Neuroinformatics 16: 790966 (2022) - [j32]Helge Ulo Dinkelbach, Badr-Eddine Bouhlal, Julien Vitay, Fred H. Hamker:
Auto-Selection of an Optimal Sparse Matrix Format in the Neuro-Simulator ANNarchy. Frontiers Neuroinformatics 16: 877945 (2022) - 2021
- [j31]Oliver Maith, Alex Schwarz, Fred H. Hamker:
Optimal attention tuning in a neuro-computational model of the visual cortex-basal ganglia-prefrontal cortex loop. Neural Networks 142: 534-547 (2021) - [j30]Michael Teichmann, René Larisch, Fred H. Hamker:
Performance of biologically grounded models of the early visual system on standard object recognition tasks. Neural Networks 144: 210-228 (2021) - [j29]René Larisch, Lorenz Gönner, Michael Teichmann, Fred H. Hamker:
Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity. PLoS Comput. Biol. 17(11) (2021) - 2020
- [j28]John Nassour, Tran Duy Hoa, Payam Atoofi, Fred H. Hamker:
Concrete Action Representation Model: From Neuroscience to Robotics. IEEE Trans. Cogn. Dev. Syst. 12(2): 272-284 (2020) - [j27]Tran Duy Hoa, Fred H. Hamker, John Nassour:
A Humanoid Robot Learns to Recover Perturbation During Swinging Motion. IEEE Trans. Syst. Man Cybern. Syst. 50(10): 3701-3712 (2020) - 2018
- [j26]Payam Atoofi, Fred H. Hamker, John Nassour:
Learning of Central Pattern Generator Coordination in Robot Drawing. Frontiers Neurorobotics 12: 44 (2018) - 2017
- [j25]Lorenz Gönner, Julien Vitay, Fred H. Hamker:
Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model. Frontiers Comput. Neurosci. 11: 84 (2017) - 2016
- [j24]Julia Bergelt, Fred H. Hamker:
Suppression of displacement detection in the presence and absence of eye movements: a neuro-computational perspective. Biol. Cybern. 110(1): 81-89 (2016) - 2015
- [j23]Arash Kermani Kolankeh, Michael Teichmann, Fred H. Hamker:
Competition improves robustness against loss of information. Frontiers Comput. Neurosci. 9: 35 (2015) - [j22]Julien Vitay, Helge U. Dinkelbach, Fred H. Hamker:
ANNarchy: a code generation approach to neural simulations on parallel hardware. Frontiers Neuroinformatics 9: 19 (2015) - [j21]Benny Liebold, René Richter, Michael Teichmann, Fred H. Hamker, Peter Ohler:
Human Capacities for Emotion Recognition and their Implications for Computer Vision. i-com 14(2): 126-137 (2015) - [j20]Fred H. Hamker:
Spatial Cognition of Humans and Brain-inspired Artificial Agents. Künstliche Intell. 29(1): 83-88 (2015) - [j19]Henning Schroll, Andreas Horn, Christine Gröschel, Christof Brücke, Götz Lütjens, Gerd-Helge Schneider, Joachim K. Krauss, Andrea A. Kühn, Fred H. Hamker:
Differential contributions of the globus pallidus and ventral thalamus to stimulus-response learning in humans. NeuroImage 122: 233-245 (2015) - [j18]Javier Baladron, Fred H. Hamker:
A spiking neural network based on the basal ganglia functional anatomy. Neural Networks 67: 1-13 (2015) - 2014
- [j17]Arnold Ziesche, Fred H. Hamker:
Brain circuits underlying visual stability across eye movements - converging evidence for a neuro-computational model of area LIP. Frontiers Comput. Neurosci. 8: 25 (2014) - [j16]Julien Vitay, Fred H. Hamker:
Timing and expectation of reward: a neuro-computational model of the afferents to the ventral tegmental area. Frontiers Neurorobotics 8: 4 (2014) - [j15]Marco Antonelli, Agostino Gibaldi, Frederik Beuth, Angel Juan Duran, Andrea Canessa, Manuela Chessa, Fabio Solari, Angel P. del Pobil, Fred H. Hamker, Eris Chinellato, Silvio P. Sabatini:
A Hierarchical System for a Distributed Representation of the Peripersonal Space of a Humanoid Robot. IEEE Trans. Auton. Ment. Dev. 6(4): 259-273 (2014) - 2012
- [j14]Fred H. Hamker:
Neural Learning of Cognitive Control. Künstliche Intell. 26(4): 397-401 (2012) - [j13]Michael Teichmann, Jan Wiltschut, Fred H. Hamker:
Learning Invariance from Natural Images Inspired by Observations in the Primary Visual Cortex. Neural Comput. 24(5): 1271-1296 (2012) - [j12]Henning Schroll, Julien Vitay, Fred H. Hamker:
Working memory and response selection: A computational account of interactions among cortico-basalganglio-thalamic loops. Neural Networks 26: 59-74 (2012) - 2011
- [j11]Julien Vitay, Fred H. Hamker:
A Neuroscientific View on the Role of Emotions in Behaving Cognitive Agents. Künstliche Intell. 25(3): 235-244 (2011) - 2010
- [j10]Julien Vitay, Fred H. Hamker:
A computational model of basal ganglia and its role in memory retrieval in rewarded visual memory tasks. Frontiers Comput. Neurosci. 4: 13 (2010) - 2009
- [j9]Julien Vitay, Jérémy Fix, Fred H. Hamker, Henning Schroll, Frederik Beuth:
Biological Models of Reinforcement Learning. Künstliche Intell. 23(3): 12-18 (2009) - 2008
- [j8]Julien Vitay, Fred H. Hamker:
Sustained Activities and Retrieval in a Computational Model of the Perirhinal Cortex. J. Cogn. Neurosci. 20(11): 1993-2005 (2008) - [j7]Fred H. Hamker, Marc Zirnsak, Dirk Calow, Markus Lappe:
The Peri-Saccadic Perception of Objects and Space. PLoS Comput. Biol. 4(2) (2008) - 2006
- [j6]Fred H. Hamker, Marc Zirnsak:
V4 receptive field dynamics as predicted by a systems-level model of visual attention using feedback from the frontal eye field. Neural Networks 19(9): 1371-1382 (2006) - 2005
- [j5]Fred H. Hamker:
The emergence of attention by population-based inference and its role in distributed processing and cognitive control of vision. Comput. Vis. Image Underst. 100(1-2): 64-106 (2005) - 2004
- [j4]Fred H. Hamker:
Predictions of a model of spatial attention using sum- and max-pooling functions. Neurocomputing 56: 329-343 (2004) - 2001
- [j3]Fred H. Hamker:
Life-long learning Cell Structures--continuously learning without catastrophic interference. Neural Networks 14(4-5): 551-573 (2001) - 2000
- [j2]Fred H. Hamker:
Visuelle Aufmerksamkeit und lebenslanges Lernen im Wahrnehmungs-Handlungs-Zyklus. Künstliche Intell. 14(4): 68-70 (2000) - 1998
- [j1]Dietmar Heinke, Fred H. Hamker:
Comparing neural networks: a benchmark on growing neural gas, growing cell structures, and fuzzy ARTMAP. IEEE Trans. Neural Networks 9(6): 1279-1291 (1998)
Conference and Workshop Papers
- 2023
- [c22]René Larisch, Lucien Berger, Fred H. Hamker:
Exploring the Role of Feedback Inhibition for the Robustness Against Corruptions on Event-Based Data. ICANN (8) 2023: 197-208 - 2022
- [c21]Torsten Fietzek, Helge Ulo Dinkelbach, Fred H. Hamker:
ANNarchy - iCub: An Interface for Easy Interaction between Neural Network Models and the iCub Robot. CIVEMSA 2022: 1-6 - [c20]Aida Farahani, Julien Vitay, Fred H. Hamker:
Deep Neural Networks for Geometric Shape Deformation. KI 2022: 90-95 - 2020
- [c19]Enrico Schröder, Mirko Mählisch, Julien Vitay, Fred H. Hamker:
Monocular 3D Object Detection Using Feature Map Transformation: Towards Learning Perspective-Invariant Scene Representations. IRC 2020: 383-390 - 2019
- [c18]John Nassour, Fred H. Hamker:
Enfolded Textile Actuator for Soft Wearable Robots. CBS 2019: 60-65 - 2018
- [c17]Deepanshu Makkar, Payam Atoofi, Fred H. Hamker, John N. Nassour:
Motor Program Learning for Humanoid Robot Drawing. Humanoids 2018: 1-9 - [c16]Yuxiang Pan, Fred H. Hamker, John Nassour:
Humanoid Robot Grasping with a Soft Gripper Through a Learned Inverse Model of a Central Pattern Generator and Tactile Servoing. Humanoids 2018: 1-9 - [c15]René Larisch, Michael Teichmann, Fred H. Hamker:
A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement. ICANN (1) 2018: 253-262 - [c14]John Nassour, Vishal Ghadiya, Vincent Hugel, Fred H. Hamker:
Design of new Sensory Soft Hand: Combining air-pump actuation with superimposed curvature and pressure sensors. RoboSoft 2018: 164-169 - [c13]Sidhdharthkumar Vaghani, Yuxiang Pan, Fred H. Hamker, John Nassour:
Gait Transition Between Simple and Complex Locomotion in Humanoid Robots. SAB 2018: 114-125 - 2017
- [c12]Winfried Lötzsch, Julien Vitay, Fred H. Hamker:
Training a deep policy gradient-based neural network with asynchronous learners on a simulated robotic problem. GI-Jahrestagung 2017: 2143-2154 - 2016
- [c11]Francesc Villagrasa, Javier Baladron, Fred H. Hamker:
Fast and Slow Learning in a Neuro-Computational Model of Category Acquisition. ICANN (1) 2016: 248-255 - [c10]Amirhossein Jamalian, Frederik Beuth, Fred H. Hamker:
The Performance of a Biologically Plausible Model of Visual Attention to Localize Objects in a Virtual Reality. ICANN (2) 2016: 447-454 - 2014
- [c9]Frederik Beuth, Amirhossein Jamalian, Fred H. Hamker:
How Visual Attention and Suppression Facilitate Object Recognition? ICANN 2014: 459-466 - 2007
- [c8]Julien Vitay, Fred H. Hamker:
On the Role of Dopamine in Cognitive Vision. WAPCV 2007: 352-366 - 2005
- [c7]Fred H. Hamker:
A Population-Based Inference Framework for Feature-Based Attention in Natural Scenes. BVAI 2005: 147-156 - 2004
- [c6]Fred H. Hamker:
Modeling Attention: From Computational Neuroscience to Computer Vision. WAPCV 2004: 118-132 - 2002
- [c5]Fred H. Hamker, James Worcester:
Object Detection in Natural Scenes by Feedback. Biologically Motivated Computer Vision 2002: 398-407 - 2000
- [c4]Jürgen Paetz, Fred H. Hamker, Sven Thöne:
About the Analysis of Septic Shock Patient Data. ISMDA 2000: 130-137 - 1997
- [c3]Fred Henrik Hamker, Horst-Michael Gross:
Object Selection with Dynamic Neural Maps. ICANN 1997: 919-924 - 1996
- [c2]Fred Henrik Hamker, Horst-Michael Gross:
Region selection: segmentation, classification and task relevance in a single grouping mechanism. ICNN 1996: 1540-1545 - [c1]Fred Henrik Hamker, Horst-Michael Groß:
Task-relevant relaxation network for visuo-motory systems. ICPR 1996: 406-410
Informal and Other Publications
- 2019
- [i1]René Larisch, Michael Teichmann, Fred H. Hamker:
A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement. CoRR abs/1912.03201 (2019)
Coauthor Index
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