Fred H. Hamker

3.2k total citations
108 papers, 2.1k citations indexed

About

Fred H. Hamker is a scholar working on Cognitive Neuroscience, Neurology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Fred H. Hamker has authored 108 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 83 papers in Cognitive Neuroscience, 18 papers in Neurology and 16 papers in Computer Vision and Pattern Recognition. Recurrent topics in Fred H. Hamker's work include Neural dynamics and brain function (62 papers), Visual perception and processing mechanisms (45 papers) and Neurological disorders and treatments (18 papers). Fred H. Hamker is often cited by papers focused on Neural dynamics and brain function (62 papers), Visual perception and processing mechanisms (45 papers) and Neurological disorders and treatments (18 papers). Fred H. Hamker collaborates with scholars based in Germany, United States and Chile. Fred H. Hamker's co-authors include Henning Schroll, Marc Zirnsak, Julien Vitay, Markus Lappe, Javier Baladron, John Nassour, Rolf Verleger, Andrea A. Kühn, Julien Dubois and Rufin VanRullen and has published in prestigious journals such as Journal of Neuroscience, NeuroImage and Brain.

In The Last Decade

Fred H. Hamker

103 papers receiving 2.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Fred H. Hamker Germany 27 1.5k 340 323 283 184 108 2.1k
Stan Gielen Netherlands 27 1.6k 1.1× 404 1.2× 260 0.8× 402 1.4× 301 1.6× 64 3.0k
Naotaka Fujii Japan 31 2.3k 1.5× 840 2.5× 355 1.1× 48 0.2× 101 0.5× 57 2.9k
Peter Ford Dominey France 36 2.2k 1.5× 230 0.7× 273 0.8× 176 0.6× 991 5.4× 140 3.6k
Daniel Bullock United States 32 2.8k 1.8× 524 1.5× 173 0.5× 124 0.4× 365 2.0× 90 3.8k
Ueli Rutishauser United States 35 3.3k 2.2× 1.1k 3.3× 136 0.4× 958 3.4× 184 1.0× 84 4.3k
Yves Burnod France 30 2.1k 1.4× 501 1.5× 190 0.6× 83 0.3× 175 1.0× 81 3.0k
Ethan A. Solomon United States 10 1.8k 1.2× 329 1.0× 71 0.2× 441 1.6× 263 1.4× 19 2.2k
George Mather United Kingdom 28 3.0k 2.0× 559 1.6× 116 0.4× 700 2.5× 43 0.2× 103 3.7k
Kost Elisevich United States 30 1.0k 0.7× 963 2.8× 547 1.7× 181 0.6× 127 0.7× 123 2.8k
Chung‐Chuan Lo Taiwan 21 983 0.7× 472 1.4× 80 0.2× 128 0.5× 236 1.3× 76 2.4k

Countries citing papers authored by Fred H. Hamker

Since Specialization
Citations

This map shows the geographic impact of Fred H. Hamker's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fred H. Hamker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fred H. Hamker more than expected).

Fields of papers citing papers by Fred H. Hamker

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fred H. Hamker. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fred H. Hamker. The network helps show where Fred H. Hamker may publish in the future.

Co-authorship network of co-authors of Fred H. Hamker

This figure shows the co-authorship network connecting the top 25 collaborators of Fred H. Hamker. A scholar is included among the top collaborators of Fred H. Hamker based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fred H. Hamker. Fred H. Hamker is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hamker, Fred H., Javier Baladron, & Lieneke Janssen. (2025). Interacting cortico-basal ganglia-thalamocortical loops shape behavioral control through cognitive maps and shortcuts. Trends in Neurosciences. 48(11). 841–852.
2.
Rudisch, Julian, et al.. (2024). A systematic review of observational practice for adaptation of reaching movements. npj Science of Learning. 9(1). 61–61.
3.
Bičanski, Andrej, et al.. (2023). A large-scale neurocomputational model of spatial cognition integrating memory with vision. Neural Networks. 167. 473–488. 2 indexed citations
4.
Vitay, Julien, et al.. (2022). Auto-Selection of an Optimal Sparse Matrix Format in the Neuro-Simulator ANNarchy. Frontiers in Neuroinformatics. 16. 877945–877945. 2 indexed citations
5.
Hamker, Fred H., et al.. (2021). Optimal attention tuning in a neuro-computational model of the visual cortex–basal ganglia–prefrontal cortex loop. Neural Networks. 142. 534–547. 4 indexed citations
6.
Fallah, Ali, et al.. (2021). A neuro-computational model of visual attention with multiple attentional control sets. Vision Research. 189. 104–118. 2 indexed citations
7.
Baladron, Javier & Fred H. Hamker. (2020). Habit learning in hierarchical cortex–basal ganglia loops. European Journal of Neuroscience. 52(12). 4613–4638. 17 indexed citations
8.
Baladron, Javier, et al.. (2020). A computational model‐based analysis of basal ganglia pathway changes in Parkinson’s disease inferred from resting‐state fMRI. European Journal of Neuroscience. 53(7). 2278–2295. 14 indexed citations
9.
Baladron, Javier, et al.. (2020). A spiking model of basal ganglia dynamics in stopping behavior supported by arkypallidal neurons. European Journal of Neuroscience. 53(7). 2296–2321. 11 indexed citations
10.
Nassour, John, Fred H. Hamker, & Gordon Cheng. (2020). High-Performance Perpendicularly-Enfolded-Textile Actuators for Soft Wearable Robots: Design and Realization. IEEE Transactions on Medical Robotics and Bionics. 2(3). 309–319. 28 indexed citations
11.
Nassour, John & Fred H. Hamker. (2019). Enfolded Textile Actuator for Soft Wearable Robots. 60–65. 15 indexed citations
12.
Baladron, Javier, Julien Vitay, Henning Schroll, et al.. (2018). On the Role of Cortex-Basal Ganglia Interactions for Category Learning: A Neurocomputational Approach. Journal of Neuroscience. 38(44). 9551–9562. 22 indexed citations
13.
Deubel, Heiner, et al.. (2017). Pre- and post-saccadic stimulus timing in saccadic suppression of displacement – A computational model. Vision Research. 138. 1–11. 16 indexed citations
14.
Vitay, Julien, et al.. (2017). Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model. Frontiers in Computational Neuroscience. 11. 84–84. 7 indexed citations
15.
Hamker, Fred H., et al.. (2016). Suppression of displacement detection in the presence and absence of eye movements: a neuro-computational perspective. Biological Cybernetics. 110(1). 81–89. 6 indexed citations
16.
Zirnsak, Marc, et al.. (2011). Anticipatory Saccade Target Processing and the Presaccadic Transfer of Visual Features. Journal of Neuroscience. 31(49). 17887–17891. 28 indexed citations
17.
Zirnsak, Marc, et al.. (2011). Split of spatial attention as predicted by a systems‐level model of visual attention. European Journal of Neuroscience. 33(11). 2035–2045. 26 indexed citations
18.
Hamker, Fred H., et al.. (2011). Computational models of spatial updating in peri-saccadic perception. Philosophical Transactions of the Royal Society B Biological Sciences. 366(1564). 554–571. 46 indexed citations
19.
Hamker, Fred H.. (2000). Visuelle Aufmerksamkeit und lebenslanges Lernen im Wahrnehmungs-Handlungs-Zyklus.. Künstliche Intell.. 14. 68–70. 4 indexed citations
20.
Hamker, Fred H., et al.. (1998). Comparing neural networks: a benchmark on growing neural gas, growing cell structures, and fuzzy ARTMAP. IEEE Transactions on Neural Networks. 9(6). 1279–1291. 48 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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