Thomas Trappenberg

3.2k total citations
91 papers, 1.7k citations indexed

About

Thomas Trappenberg is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Cellular and Molecular Neuroscience. According to data from OpenAlex, Thomas Trappenberg has authored 91 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Cognitive Neuroscience, 25 papers in Artificial Intelligence and 13 papers in Cellular and Molecular Neuroscience. Recurrent topics in Thomas Trappenberg's work include Neural dynamics and brain function (37 papers), Neural Networks and Applications (14 papers) and Visual perception and processing mechanisms (12 papers). Thomas Trappenberg is often cited by papers focused on Neural dynamics and brain function (37 papers), Neural Networks and Applications (14 papers) and Visual perception and processing mechanisms (12 papers). Thomas Trappenberg collaborates with scholars based in Canada, United Kingdom and Germany. Thomas Trappenberg's co-authors include Simon M. Stringer, Edmund T. Rolls, Raymond M. Klein, Douglas P. Munoz, Michael C. Dorris, Ivan E. de Araújo, Andrew D. Back, Dominic Standage, Jason Satel and Zhiguo Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Nuclear Physics B.

In The Last Decade

Thomas Trappenberg

87 papers receiving 1.7k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Thomas Trappenberg 1.1k 363 263 166 116 91 1.7k
Kay A. Robbins 1.6k 1.5× 363 1.0× 152 0.6× 70 0.4× 126 1.1× 96 2.8k
Ariel Rokem 2.0k 1.9× 544 1.5× 106 0.4× 161 1.0× 150 1.3× 93 4.1k
Lionel Barnett 2.0k 1.9× 314 0.9× 450 1.7× 47 0.3× 78 0.7× 32 3.5k
Anthony N. Burkitt 1.8k 1.7× 1.3k 3.5× 106 0.4× 34 0.2× 689 5.9× 168 2.6k
Zbigniew R. Struzik 549 0.5× 63 0.2× 134 0.5× 62 0.4× 49 0.4× 97 2.3k
Christian Rummel 1.3k 1.2× 239 0.7× 85 0.3× 65 0.4× 52 0.4× 94 1.9k
E. Harth 2.2k 2.0× 483 1.3× 497 1.9× 70 0.4× 169 1.5× 49 2.9k
M. Stetter 653 0.6× 128 0.4× 222 0.8× 70 0.4× 312 2.7× 89 1.8k
Valeri A. Makarov 1.5k 1.4× 619 1.7× 284 1.1× 113 0.7× 306 2.6× 94 2.5k
Luiz Antonio Baccalá 2.8k 2.6× 733 2.0× 266 1.0× 34 0.2× 99 0.9× 63 3.5k

Countries citing papers authored by Thomas Trappenberg

Since Specialization
Citations

This map shows the geographic impact of Thomas Trappenberg'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 Thomas Trappenberg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Trappenberg more than expected).

Fields of papers citing papers by Thomas Trappenberg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas Trappenberg. 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 Thomas Trappenberg. The network helps show where Thomas Trappenberg may publish in the future.

Co-authorship network of co-authors of Thomas Trappenberg

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Trappenberg. A scholar is included among the top collaborators of Thomas Trappenberg 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 Thomas Trappenberg. Thomas Trappenberg 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.
Trappenberg, Thomas, et al.. (2024). An Information-Geometric Formulation of Pattern Separation and Evaluation of Existing Indices. Entropy. 26(9). 737–737. 2 indexed citations
2.
Becker, Suzanna, Julie Garnham, Anouar Khayachi, et al.. (2024). A pilot study examining the impact of lithium treatment and responsiveness on mnemonic discrimination in bipolar disorder. Journal of Affective Disorders. 351. 49–57. 1 indexed citations
3.
4.
Nunes, Abraham, Thomas Trappenberg, & Martin Alda. (2020). The definition and measurement of heterogeneity. Translational Psychiatry. 10(1). 299–299. 33 indexed citations
5.
Nunes, Abraham, Thomas Trappenberg, & Martin Alda. (2020). Measuring heterogeneity in normative models as the effective number of deviation patterns. PLoS ONE. 15(11). e0242320–e0242320. 2 indexed citations
6.
Trappenberg, Thomas, et al.. (2019). A Novel Model for Arbitration Between Planning and Habitual Control Systems. PubMed Central. 2 indexed citations
7.
Coe, Brian C., Thomas Trappenberg, & Douglas P. Munoz. (2019). Modeling Saccadic Action Selection: Cortical and Basal Ganglia Signals Coalesce in the Superior Colliculus. Frontiers in Systems Neuroscience. 13. 3–3. 15 indexed citations
8.
Guida, Alessandro, Steven Patterson, Thomas Trappenberg, et al.. (2019). Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study. Computerized Medical Imaging and Graphics. 75. 14–23. 15 indexed citations
9.
Hassall, Cameron D., et al.. (2018). Learning what matters: A neural explanation for the sparsity bias. International Journal of Psychophysiology. 127. 62–72. 4 indexed citations
10.
Krigolson, Olav, et al.. (2015). A biological mechanism for Bayesian feature selection: Weight decay and raising the LASSO. Neural Networks. 67. 121–130. 13 indexed citations
11.
Hajra, Sujoy Ghosh, et al.. (2015). A rapid event-related potential (ERP) method for point-of-care evaluation of brain function: Development of the Halifax Consciousness Scanner. Journal of Neuroscience Methods. 245. 64–72. 22 indexed citations
12.
Heinke, Dietmar, et al.. (2015). Modeling human target reaching with an adaptive observer implemented with dynamic neural fields. Neural Networks. 72. 13–30. 4 indexed citations
13.
Satel, Jason, et al.. (2014). Simulating oculomotor inhibition of return with a two-dimensional dynamic neural field model of the superior colliculus. eCite Digital Repository (University of Tasmania). 27–32. 2 indexed citations
14.
Satel, Jason, Zhiguo Wang, Thomas Trappenberg, & Raymond M. Klein. (2011). Modeling inhibition of return as short-term depression of early sensory input to the superior colliculus. Vision Research. 51(9). 987–996. 41 indexed citations
15.
Trappenberg, Thomas. (2008). Tracking population densities using dynamic neural fields with moderately strong inhibition. Cognitive Neurodynamics. 2(3). 171–177. 5 indexed citations
16.
Standage, Dominic, et al.. (2007). Computational consequences of experimentally derived spike-time and weight dependent plasticity rules. Biological Cybernetics. 96(6). 615–623. 16 indexed citations
17.
Stringer, Simon M., Edmund T. Rolls, & Thomas Trappenberg. (2003). Self-organising continuous attractor networks with multiple activity packets, and the representation of space. Neural Networks. 17(1). 5–27. 50 indexed citations
18.
Stringer, Simon M., Edmund T. Rolls, Thomas Trappenberg, & Ivan E. de Araújo. (2003). Self-organizing continuous attractor networks and motor function. Neural Networks. 16(2). 161–182. 41 indexed citations
19.
Trappenberg, Thomas, Michael C. Dorris, Douglas P. Munoz, & Raymond M. Klein. (2001). A Model of Saccade Initiation Based on the Competitive Integration of Exogenous and Endogenous Signals in the Superior Colliculus. Journal of Cognitive Neuroscience. 13(2). 256–271. 354 indexed citations
20.
Trappenberg, Thomas. (1998). Dynamic Cooperation and Competition in a Network of Spiking Neurons.. International Conference on Neural Information Processing. 1299–1302. 3 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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