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Baladron, J., Hamker, F.H. (in press)
Habit learning in hierarchical cortex - basal ganglia loops
European Journal of Neuroscience,

Nassour, J., Tran, D.H., Atoofi, P., Hamker, F.H. (in press)
Concrete Action Representation Model: from Neuroscience to Robotics
IEEE Transactions on Cognitive and Developmental Systems, doi:10.1109/TCDS.2019.2896300.


Kübler, D., Schroll, H., Hamker, F.H., Joutsa, J., Kühn, A.A. (2019)
The effect of dopamine on response inhibition in Parkinson's disease relates to age-dependent patterns of nigrostriatal degeneration
Parkinsonism and Related Disorders, 63:185-190. doi:10.1016/j.parkreldis.2019.02.003.

Baladron, J., Nambu, A., Hamker, F.H. (2019)
The subthalamic nucleus - external globus pallidus loop biases exploratory decisions towards known alternatives: A neuro-computational study
European Journal of Neuroscience, 49:754-767. doi:10.1111/ejn.13666.

Bergelt, J., Hamker, F.H. (2019)
Spatial updating of attention across eye movements: A neuro-computational approach
Journal of Vision, 19(7):10, 1-23. doi:10.1167/19.7.10.
Supplementary Material

Nassour, J. (2019)
Marionette-based programming of a soft textile inflatable actuator
Sensors and Actuators A: Physical, 291:93-98. doi:10.1016/j.sna.2019.03.017.

Abadi, A. K., Yahya, K., Amini, M., Friston, K., Heinke, D. (2019)
Excitatory versus inhibitory feedback in Bayesian formulations of scene construction
Journal of The Royal Society, 16(154). doi:10.1098/rsif.2018.0344.

Larisch, R. (2019)
[Re] Connectivity reflects coding a model of voltage-based STDP with homeostasis
ReScience C, 5(3). doi:10.5281/zenodo.3538217.


Schroll, H., Horn, A., Runge, J., Lipp, A., Schneider, G.-H., Krauss, J., Hamker, F.H., Kühn, A. (2018)
Reinforcement magnitudes modulate subthalamic beta band activity in patients with Parkinson's disease
Scientific Reports, 8(1):8621. doi:10.1038/s41598-018-26887-3.

Neumann, W.-J., Schroll, H., de Almeida Marcelino, A.L., Horn, A., Ewert, S., Irmen, F., Krause, P., Schneider, G.-H., Hamker, F.H., Kühn, A. (2018)
Functional segregation of basal ganglia pathways in Parkinson's disease
Brain, 141(9):2655-2669. doi:10.1093/brain/awy206.

Atoofi, P., Hamker, F.H., Nassour, J. (2018)
Learning of Central Pattern Generator Coordination in Robot Drawing
Frontiers in Neurorobotics, 12:44. doi:10.3389/fnbot.2018.00044.

Villagrasa, F., Baladron, J., Vitay, J., Schroll, H., Antzoulatos, E. G., Miller, Earl K., Hamker, F.H. (2018)
On the role of cortex-basal ganglia interactions for category learning: A neuro-computational approach
Journal of Neuroscience, 38(44):9551-9562. doi:10.1523/JNEUROSCI.0874-18.2018.

Duy Hoa, T., Hamker, F.H., Nassour, J. (2018)
A Humanoid Robot Learns to Recover Perturbation During Swinging Motion
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1-12. doi:10.1109/tsmc.2018.2884619.


Hartmann, T.S., Zirnsak, M., Marquis, M., Hamker, F.H., Moore, T. (2017)
Two types of receptive field dynamics in area V4 at the time of eye movements?
Frontiers in Systems Neuroscience, 11:13. doi:10.3389/fnsys.2017.00013 .

Ziesche, A., Bergelt, J., Deubel, H., Hamker, F.H. (2017)
Pre- and post-saccadic stimulus timing in Saccadic Suppression of Displacement - a computational model
Vision Research, 138:1-11. doi:10.1016/j.visres.2017.06.007.

Irmen, F., Huebl, J., Schroll, H., Brücke, Ch., Schneider, G.-H., Hamker, F.H., Kühn, A. (2017)
Subthalamic nucleus stimulation impairs emotional conflict monitoring in Parkinson's Disease
Social Cognitive and Affective Neuroscience, 12(10):1594-1604. doi:10.1093/scan/nsx090.

Gönner, L., Vitay, J., Hamker, F.H. (2017)
Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model
Frontiers in Computational Neuroscience, 11:84. doi:10.3389/fncom.2017.00084.


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. doi:10.1007/s00422-015-0677-z.

Schroll, H., Hamker, F.H. (2016)
Basal ganglia dysfunctions in movement disorders: What can be learned from computational simulations
Movement Disorders, 31:1591-1601. doi:10.1002/mds.26719.

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


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.

Dinkelbach, H.Ü., Schuster, J., Hamker, F.H. (2015)
Reinforcement Learning zur Planung von Arbeitsprozessen
Industrie 4.0 Management, 31(1), 9-12.

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.

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.

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, 35:295-307. doi:10.1016/j.concog.2015.02.007.

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.

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.

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.

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.

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.


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, 39:688-702. doi:10.1111/ejn.12434.

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.

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.

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.

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, 32-35.


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.

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.


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.

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.

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.


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.

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.

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.

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.

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.

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.

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


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., 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. doi:10.1016/j.visres.2010.02.002.

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.


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

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.

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.


Hamker, F.H., Zirnsak, M., Calow, D., Lappe, M. (2008)
The peri-saccadic perception of objects and space.
PLOS Computational Biology, 4(2):e31. doi:10.1371/journal.pcbi.0040031.

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., 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. (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., 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., 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. doi:10.1016/j.neunet.2006.08.006.

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


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. (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. (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.

Press Articles