Jochen Triesch

5.7k total citations
188 papers, 3.2k citations indexed

About

Jochen Triesch is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Jochen Triesch has authored 188 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 124 papers in Cognitive Neuroscience, 37 papers in Computer Vision and Pattern Recognition and 32 papers in Electrical and Electronic Engineering. Recurrent topics in Jochen Triesch's work include Neural dynamics and brain function (75 papers), Visual perception and processing mechanisms (49 papers) and Advanced Memory and Neural Computing (32 papers). Jochen Triesch is often cited by papers focused on Neural dynamics and brain function (75 papers), Visual perception and processing mechanisms (49 papers) and Advanced Memory and Neural Computing (32 papers). Jochen Triesch collaborates with scholars based in Germany, United States and Hong Kong. Jochen Triesch's co-authors include C. von der Malsburg, Gedeon O. Deák, Christoph von der Malsburg, Mary Hayhoe, Brian Sullivan, D.H. Ballard, Eric T. Carlson, Bertram E. Shi, Ulf Ziemann and Christof Teuscher and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and PLoS ONE.

In The Last Decade

Jochen Triesch

176 papers receiving 3.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jochen Triesch Germany 31 1.8k 765 553 496 436 188 3.2k
Hanspeter A. Mallot Germany 31 1.5k 0.8× 1.2k 1.5× 207 0.4× 193 0.4× 214 0.5× 112 3.9k
Marcel van Gerven Netherlands 39 3.5k 1.9× 649 0.8× 201 0.4× 118 0.2× 304 0.7× 149 4.9k
David C. Knill United States 36 5.1k 2.8× 857 1.1× 233 0.4× 315 0.6× 107 0.2× 72 6.3k
R. H. S. Carpenter United Kingdom 35 3.6k 2.0× 331 0.4× 280 0.5× 224 0.5× 223 0.5× 121 5.4k
Peter Ford Dominey France 36 2.2k 1.2× 176 0.2× 119 0.2× 957 1.9× 312 0.7× 140 3.6k
Anatole Lécuyer France 39 3.4k 1.9× 657 0.9× 2.4k 4.4× 120 0.2× 357 0.8× 192 5.0k
Markus Lappe Germany 46 5.4k 3.0× 1.4k 1.8× 1.6k 2.9× 226 0.5× 108 0.2× 220 7.2k
Denis Fize France 18 4.1k 2.3× 816 1.1× 108 0.2× 234 0.5× 295 0.7× 26 4.9k
Maximilian Riesenhuber United States 25 5.4k 3.0× 2.0k 2.7× 121 0.2× 556 1.1× 553 1.3× 63 7.3k
Andrew H. Fagg United States 24 1.4k 0.8× 202 0.3× 96 0.2× 129 0.3× 176 0.4× 89 2.5k

Countries citing papers authored by Jochen Triesch

Since Specialization
Citations

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

Fields of papers citing papers by Jochen Triesch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jochen Triesch

This figure shows the co-authorship network connecting the top 25 collaborators of Jochen Triesch. A scholar is included among the top collaborators of Jochen Triesch 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 Jochen Triesch. Jochen Triesch 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.
Fronius, Maria, et al.. (2025). Virtual reality-based Harms tangent screen test for strabismus measurement. Graefe s Archive for Clinical and Experimental Ophthalmology. 263(5). 1435–1442.
2.
Rahimi, Ali, et al.. (2024). Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch. Frontiers in Neuroinformatics. 18. 1331220–1331220. 3 indexed citations
3.
Fleming, Roland W., et al.. (2024). Self-Supervised Learning of Color Constancy. 1–7. 1 indexed citations
4.
5.
Rothkopf, Constantin A., Frank Bremmer, Katja Fiehler, Katharina Dobs, & Jochen Triesch. (2023). Models of vision need some action. Behavioral and Brain Sciences. 46. e405–e405. 1 indexed citations
6.
Triesch, Jochen, et al.. (2023). Variability in infant social responsiveness: Age and situational differences in attention-following. Developmental Cognitive Neuroscience. 63. 101283–101283. 6 indexed citations
7.
Hafner, Anne‐Sophie & Jochen Triesch. (2023). Synaptic logistics: Competing over shared resources. Molecular and Cellular Neuroscience. 125. 103858–103858. 2 indexed citations
8.
Shi, Bertram E., et al.. (2020). Active efficient coding explains the development of binocular vision and its failure in amblyopia. Proceedings of the National Academy of Sciences. 117(11). 6156–6162. 17 indexed citations
9.
Wang, Quan, Constantin A. Rothkopf, & Jochen Triesch. (2017). A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity. PLoS Computational Biology. 13(8). e1005632–e1005632. 7 indexed citations
10.
Konda, Kishore, et al.. (2015). Real-time activity recognition via deep learning of motion features. The European Symposium on Artificial Neural Networks. 1 indexed citations
11.
Shi, Bertram E., et al.. (2015). The role of contrast sensitivity in the development of binocular vision: A computational study. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 33–38. 2 indexed citations
12.
Zheng, Pengsheng, Christos Dimitrakakis, & Jochen Triesch. (2013). Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex. PLoS Computational Biology. 9(1). e1002848–e1002848. 65 indexed citations
13.
Saeb, Sohrab, Cornelius Weber, & Jochen Triesch. (2011). Learning the Optimal Control of Coordinated Eye and Head Movements. PLoS Computational Biology. 7(11). e1002253–e1002253. 24 indexed citations
14.
Simmering, Vanessa R., Jochen Triesch, Gedeon O. Deák, & John P. Spencer. (2010). A Dialogue on the Role of Computational Modeling in Developmental Science. Child Development Perspectives. 4(2). 152–158. 7 indexed citations
15.
Gerhard, Felipe, Cristina Savin, & Jochen Triesch. (2009). A robust biologically plausible implementation of ICA-like learning. The European Symposium on Artificial Neural Networks. 147–152. 3 indexed citations
16.
Butko, Nicholas J. & Jochen Triesch. (2006). Exploring the role of intrinsic plasticity for the learning of sensory representations.. The European Symposium on Artificial Neural Networks. 467–472. 3 indexed citations
17.
Lazăr, Andreea, Gordon Pipa, & Jochen Triesch. (2006). The combination of STDP and intrinsic plasticity yields complex dynamics in recurrent spiking networks.. The European Symposium on Artificial Neural Networks. 647–652. 5 indexed citations
18.
Murphy-Chutorian, Erik & Jochen Triesch. (2005). Shared Features for Scalable Appearance-Based Object Recognition. 16–21. 31 indexed citations
19.
Triesch, Jochen. (2004). Synergies between Intrinsic and Synaptic Plasticity in Individual Model Neurons. Neural Information Processing Systems. 17. 1417–1424. 38 indexed citations
20.
Bryson, Joanna J., Jochen Triesch, & Tony Jebara. (2004). Modularity and Specialized Learning: Reexamining Behavior-Based Artificial Intelligence. 1 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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