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Journals

2012   2011   2010   2009   2008   2007   2006   2005   2004   2003   2001   1998   1996  

2012

Bröckelmann, A.-K., Junghöfer, M., Scharlau, I., Hamker, F. H. (submitted)
Reentrant processing from attentional task sets: Converging support from MEG and computational modeling.

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.
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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.
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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.
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2011

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.
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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.
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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.
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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.
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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.
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Vitay, J., Hamker, F. H. (2011)
A Neuroscientific View on the Role of Emotions in Behaving Cognitive Agents.
Künstliche Intelligenz, 25: 235-244.
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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.
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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.
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Zirnsak, M., Hamker, F. H. (2010)
Attention Alters Feature Space in Motion Processing.
Journal of Neuroscience, 30(20):6882-6890.
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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.
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2009

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.
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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.
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Vitay, J., Fix, J., Beuth, F., Schroll, H., Hamker, F. H. (2009)
Biological Models of Reinforcement Learning.
Künstliche Intelligenz, 3:12-18.
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2008

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.
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Hamker, F.H., Zirnsak, M., Calow, D., Lappe, M. (2008)
The peri-saccadic perception of objects and space.
PLOS Computational Biology, 4(2):e31.
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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.
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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.
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2007

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.
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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.
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2006

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.
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Hamker, F. H. (2006)
Modeling feature-based attention as an active top-down inference process.
BioSystems, 86: 91-99.
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2005

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.
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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.
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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.
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2004

Hamker, F. H. (2004)
Predictions of a model of spatial attention using sum- and max-pooling functions.
Neurocomputing, 56C: 329-343.
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Hamker, F. H. (2004)
A dynamic model of how feature cues guide spatial attention.
Vision Research, 44: 501-521.
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2003

Hamker, F. H. (2003)
The reentry hypothesis: linking eye movements to visual perception.
Journal of Vision, 11: 808-816.
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2001

Hamker, F. H. (2001)
Life-long learning Cell Structures - continuously learning without catastrophic interference.
Neural Networks, 14: 551-573.
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1998

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.
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1996

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.