Inhalt Hotkeys



2016   2015   2014   2013   2012   2011   2010   2009   2008   2007   2006   2005   2004   2003   2001   1998   1996  


Vitay, J. (2016)
Robust timing and motor patterns by taming chaos in recurrent neural networks
ReScience 2(1), doi:10.5281/zenodo.159545.

Bergelt, J., Hamker, F. H. (2016)
Suppression of displacement detection in the presence and absence of eye movements: A neuro-computational perspective.
Biological Cybernetics, 110:81-89.

Schroll, H., Hamker, F.H. (2016)
Basal ganglia dysfunctions in movement disorders: What can be learned from computational simulations
Journal Movement Disorders, Jul 9. doi:10.1002/mds.26719.


Beuth, F., Hamker, F. H. (2015)
A mechanistic cortical microcircuit of attention for amplification, normalization and suppression
Vision Research, 116, 241-257. doi:10.1016/j.visres.2015.04.004.
Supplementary Material

Schroll, H., Horn, A., Gröschel, C., Brücke, C., Lütjens, G., Schneider, G.-H., Krauss, J. K., Kühn, A. A., Hamker, F. H. (2015)
Differential contributions of the globus pallidus and ventral thalamus to stimulus-response learning in humans
NeuroImage, 122, 233-245. doi:10.1016/j.neuroimage.2015.07.061.

Dinkelbach, H.Ü., Schuster, J., Hamker, F.H. (2015)
Reinforcement Learning zur Planung von Arbeitsprozessen
Industrie Management, 1, 9-12.

Liebold, B., Richter, R., Teichmann, M., Hamker, F.H., Ohler, P. (2015)
Human Capacities for Emotion Recognition and their Implications for Computer Vision
i-com, 14(2):126-137. doi:10.1515/icom-2015-0032.

Hamker, F.H. (2015)
Spatial Cognition of humans and brain-like artificial agents
Künstliche Intelligenz, 29, 83-88. doi:10.1007/s13218-014-0338-8.

Schroll, H., Beste, C., Hamker, F.H. (2015)
Combined lesions of direct and indirect basal ganglia pathways but not changes in dopamine levels explain learning deficits in patients with Huntington's disease
European Journal of Neuroscience, 41:1227-1244. doi:10.1111/ejn.12868.

Ebner, C., Schroll, H., Winther, G., Niedeggen, M., Hamker, F.H. (2015)
Open and closed cortico-subcortical loops: A neuro-computational account of access to consciousness in the distractor-induced blindness paradigm
Consciousness and Cognition, Vol. 35, pp. 295-307. doi:10.1016/j.concog.2015.02.007.

Baladron, J., Hamker, F.H. (2015)
A spiking neural network based on the basal ganglia functional anatomy
Neural Networks, 67:1-13. doi:10.1016/j.neunet.2015.03.002.

Vitay, J., Dinkelbach, H.Ü., Hamker F.H. (2015)
ANNarchy: a code generation approach to neural simulations on parallel hardware
Frontiers in Neuroinformatics, 9, 19. doi:10.3389/fninf.2015.00019.

Lappe, M., Hamker, F.H. (2015)
Peri-saccadic compression to two locations in a two-target choice saccade task
Frontiers in Systems Neuroscience, 9:135. doi:10.3389/fnsys.2015.00135.

Kermani Kolankeh, A., Teichmann, M., Hamker, F.H. (2015)
Competition improves robustness against loss of information
Frontiers in Computational Neuroscience, 9:35. doi:10.3389/fncom.2015.00035.


Antonelli, M., Gibaldi, A., Beuth, F., Duran, A. J., Canessa, A., Chessa, M., Solari, F., del Pobil, P., Hamker, F., Chinellato, E., Sabatini, S. P. (2014)
A Hierarchical System for a Distributed Representation of the Peripersonal Space of a Humanoid Robot
IEEE Transactions on Autonomous Mental Development, 6(4), 259-273. doi:10.1109/TAMD.2014.2332875.

Schroll, H., Vitay, J., Hamker, F.H. (2014)
Dysfunctional and Compensatory Synaptic Plasticity in Parkinson's Disease.
European Journal of Neuroscience, Vol. 39, pp. 688-702. doi:10.1111/ejn.12434.

Verleger, R., Koerbs, A., Graf, J., Smigasiewicz, K., Schroll, H., Hamker, F.H. (2014)
Patients with Parkinson's disease are less affected than healthy persons by relevant response-unrelated features in visual search
Neuropsychologia, 62, 38-47. doi:10.1016/j.neuropsychologia.2014.07.004.

Vitay, J., Hamker, F.H. (2014)
Timing and expectation of reward: a neuro-computational model of the afferents to the ventral tegmental area
Frontiers in Neurorobotics, 8:4. doi:10.3389/fnbot.2014.00004.

Müller, N., Truschzinski, M., Fink, V., Schuster, J., Dinkelbach, H.Ü., Heft, V., Kronfeld, Th., Rau, C., Spitzhirn, M. (2014)
The Smart Virtual Worker
Technische Sicherheit, Springer VDI Verlag. 7-8, pp. 32-35.

Ziesche, A., Hamker, F.H. (2014)
Brain circuits underlying visual stability across eye movements - converging evidence for a neuro-computational model of area LIP.
Frontiers in Computational Neuroscience, 8(25), 1-15. doi:10.3389/fncom.2014.00025.


Verleger R., Schroll H., Hamker F.H. (2013)
The Unstable Bridge from Stimulus Processing to Correct Responding in Parkinson's Disease
Neuropsychologia, 51(13):2512-2525. doi:10.1016/j.neuropsychologia.2013.09.017.

Schroll H., Hamker, F.H. (2013)
Computational models of basal-ganglia pathway functions: Focus on functional neuroanatomy
Frontiers in Systems Neuroscience, 7:122. doi:10.3389/fnsys.2013.00122.


Schroll, H, Vitay, J, Hamker, F.H. (2012)
Working memory and response selection: A computational account of interactions among cortico-basal ganglio-thalamic loops.
Neural Networks, 26: 59-74. doi:10.1016/j.neunet.2011.10.008.

Trapp, S., Schroll, H., Hamker, F.H. (2012)
Open and Closed loops: A computational approach to Attention and Consciousness
Advances in Cognitive Psychology, 8(1): 1-8. doi:10.5709/acp-0096-y.

Teichmann, M., Wiltschut, J., Hamker, F.H. (2012)
Learning invariance from natural images inspired by observations in the primary visual cortex
Neural Computation, 24(5): 1271-1296. doi:10.1162/NECO_a_00268.

Hamker, F.H. (2012)
Neural learning of cognitive control
Künstliche Intelligenz, 26: 397-401. doi: 10.1007/s13218-012-0210-7.

Dinkelbach, H., Vitay, J., Beuth, F., Hamker, F.H. (2012)
Comparison of GPU- and CPU-implementations of mean-firing rate neural networks on parallel hardware.
Network: Computation in Neural Systems., 23(4): 212-236. doi:10.3109/0954898X.2012.739292.


Ziesche, A., Hamker, F. H. (2011)
A computational Model for the Influence of Corollary Discharge and Proprioception on the Perisaccadic Mislocalization of Briefly Presented Stimuli in Complete Darkness
Journal of Neuroscience, 31(48): 17392-17405. doi:10.1523/JNEUROSCI.3407-11.2011.

Hamker, F. H., Zirnsak, M., Ziesche, A., Lappe, M. (2011)
Computational models of spatial updating in peri-saccadic perception
Phil. Trans. R. Soc. B (2011), 366: 554-571. doi:10.1098/rstb.2010.0229.

Vitay, J., Hamker, F. H. (2011)
A Neuroscientific View on the Role of Emotions in Behaving Cognitive Agents.
Künstliche Intelligenz, 25: 235-244.

Zirnsak, M., Beuth, F., Hamker, F. H. (2011)
Split of spatial attention as predicted by a systems-level model of visual attention
European Journal of Neuroscience, 33: 2035-2045.

Zirnsak, M., Gerhards, R., Kiani, R., Lappe, M., Hamker, F.H. (2011)
Anticipatory Saccade Target Processing and the Pre-saccadic Transfer of Visual Features
Journal of Neuroscience, 31(49):17887-17891.

Kiefer, M., Ansorge, U., Haynes, J.-D., Hamker, F.H., Mattler, U., Verleger, R., Niedeggen, M. (2011)
Neuro-cognitive mechanisms of conscious and unconscious visual perception: From a plethora of phenomena to general principles
Advances in Cognitive Psychology, 7: 55-67.

Kermani Kolankeh, A., Spitsyn, V.G, Hamker, F.H. (2011)
Finding parameters and removing the DC component of Gabor Filter for Image processing
Bulletin of Tomsk Polytechnic University, 318(5): 57.


Vitay, J., Hamker, F. H. (2010)
A computational model of basal ganglia and its role in memory retrieval in rewarded visual memory tasks.
Frontiers in Computational Neuroscience, 4(13): 1-18.

Zirnsak, M., Hamker, F. H. (2010)
Attention Alters Feature Space in Motion Processing.
Journal of Neuroscience, 30(20):6882-6890. doi:10.1523/JNEUROSCI.3543-09.2010.

Zirnsak, M., Lappe, M., Hamker, F. H. (2010)
The spatial distribution of receptive field changes in a model of peri-saccadic perception: predictive remapping and shifts towards the saccade target.
Vision Research, 50:1328-1337.


Wiltschut, J., Hamker, F.H. (2009)
Efficient Coding correlates with spatial frequency tuning in a model of V1 receptive field organization.
Visual Neuroscience, 26:21-34.

Dubois, J., Hamker, F.H., VanRullen, R. (2009)
Attentional selection of noncontiguous locations: The spotlight is only transiently split.
Journal of Vision, 9(5):3, 1-11.

Vitay, J., Fix, J., Beuth, F., Schroll, H., Hamker, F. H. (2009)
Biological Models of Reinforcement Learning.
Künstliche Intelligenz, 3:12-18.


Hamker, F.H., Zirnsak, M., Lappe, M. (2008)
About the influence of post-saccadic mechanisms for visual stability on peri-saccadic compression of object location.
Journal of Vision, 8(14):1, 1-13.

Vitay, J., Hamker, F.H. (2008)
Sustained activities and retrival in a computational model of perirhinal cortex.
Journal of Cognitive Neuroscience, 20(11): 1993-2005.

Hamker, F.H., Zirnsak, M., Calow, D., Lappe, M. (2008)
The peri-saccadic perception of objects and space.
PLOS Computational Biology, 4(2):e31.

Georg, K., Hamker, F. H., and Lappe, M. (2008)
Influence of adaptation state and stimulus luminance on peri-saccadic localization.
J. Vision, 8(1):15, 1-11.


Hamker, F.H., Wiltschut, J. (2007)
Hebbian learning in a model with dynamic rate-coded neurons: an alternative to the generative model approach for learning receptive fields from natural scenes.
Network, Computation in Neural Systems, 18: 249-266.

Hamker, F. H. (2007)
The mechanisms of feature inheritance as predicted by a systems-level model of visual attention and decision making.
Advances in Cognitive Psychology, 3: 111-123.


Hamker, F. H., Zirnsak, M. (2006)
V4 receptive field dynamics as predicted by a systems-level model of visual attention using feedback from the frontal eye field.
Neural Networks, 19: 1371-1382.

Hamker, F. H. (2006)
Modeling feature-based attention as an active top-down inference process.
BioSystems, 86: 91-99.


Hamker, F. H. (2005)
The Reentry Hypothesis: The Putative Interaction of the Frontal Eye Field, Ventrolateral Prefrontal Cortex, and Areas V4, IT for Attention and Eye Movement.
Cerebral Cortex, 15: 431-447.

Hamker, F. H. (2005)
The emergence of attention by population-based inference and its role in distributed processing and cognitive control of vision.
Journal for Computer Vision and Image Understanding, Special Issue on Attention and Performance in Computer Vision, 100: 64-106.

Hamker, F. H. (2005)
A computational model of visual stability and change detection during eye movements in real world scenes.
Visual Cognition, 12: 1161-1176.


Hamker, F. H. (2004)
Predictions of a model of spatial attention using sum- and max-pooling functions.
Neurocomputing, 56C: 329-343.

Hamker, F. H. (2004)
A dynamic model of how feature cues guide spatial attention.
Vision Research, 44: 501-521.


Hamker, F. H. (2003)
The reentry hypothesis: linking eye movements to visual perception.
Journal of Vision, 11: 808-816.


Hamker, F. H. (2001)
Life-long learning Cell Structures - continuously learning without catastrophic interference.
Neural Networks, 14: 551-573.


Heinke, D., Hamker, F. H. (1998)
Comparing Neural Networks: A Benchmark on Growing Neural Gas, Growing Cell Structures, and Fuzzy ARTMAP.
IEEE Transactions on Neural Networks, 9: 1279-1291.


Hamker, F. H., Gross, H.-M. (1996)
Region Finding for Attention Control in Consideration of Subgoals.
Neural Network World. International Journal on Neural and Mass-Parallel Computing and Information Systems, 6: 305-313.