Thomas Nowotny

3.4k total citations
104 papers, 1.9k citations indexed

About

Thomas Nowotny is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Electrical and Electronic Engineering. According to data from OpenAlex, Thomas Nowotny has authored 104 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Cellular and Molecular Neuroscience, 39 papers in Cognitive Neuroscience and 29 papers in Electrical and Electronic Engineering. Recurrent topics in Thomas Nowotny's work include Neurobiology and Insect Physiology Research (43 papers), Neural dynamics and brain function (36 papers) and Advanced Memory and Neural Computing (29 papers). Thomas Nowotny is often cited by papers focused on Neurobiology and Insect Physiology Research (43 papers), Neural dynamics and brain function (36 papers) and Advanced Memory and Neural Computing (29 papers). Thomas Nowotny collaborates with scholars based in United Kingdom, United States and Germany. Thomas Nowotny's co-authors include M. I. Rabinovich, Ramón Huerta, Henry D. I. Abarbanel, James C. Knight, James P. Turner, Jennifer S. Haas, Stijn Cassenaer, Gilles Laurent, Dávid Samu and Allen I. Selverston and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Physical Review Letters.

In The Last Decade

Thomas Nowotny

99 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Thomas Nowotny United Kingdom	25	1.0k	942	578	279	219	104	1.9k
Martin Paul Nawrot Germany	23	1.1k 1.1×	910 1.0×	264 0.5×	386 1.4×	183 0.8×	62	1.7k
Ashok Litwin-Kumar United States	20	1.2k 1.2×	1.3k 1.4×	346 0.6×	263 0.9×	215 1.0×	36	2.0k
Shaul Druckmann United States	23	1.5k 1.5×	1.8k 2.0×	354 0.6×	214 0.8×	78 0.4×	43	2.7k
Andreas V. M. Herz Germany	35	1.7k 1.7×	2.4k 2.6×	538 0.9×	312 1.1×	722 3.3×	89	4.0k
Ofer Mazor United States	8	921 0.9×	592 0.6×	123 0.2×	242 0.9×	58 0.3×	13	1.4k
Andrew S. French Canada	32	2.3k 2.3×	964 1.0×	127 0.2×	744 2.7×	226 1.0×	199	3.9k
Alexander Volkovskii United States	17	442 0.4×	693 0.7×	365 0.6×	68 0.2×	773 3.5×	36	1.7k
Rob R. de Ruyter van Steveninck United States	13	1.7k 1.7×	2.5k 2.7×	542 0.9×	182 0.7×	612 2.8×	20	3.8k
J. H. van Hateren Netherlands	31	1.8k 1.8×	2.5k 2.7×	206 0.4×	259 0.9×	54 0.2×	63	4.0k
Elad Schneidman Israel	25	1.3k 1.3×	2.4k 2.6×	298 0.5×	128 0.5×	814 3.7×	51	3.8k

Countries citing papers authored by Thomas Nowotny

Since Specialization

Citations

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

Fields of papers citing papers by Thomas Nowotny

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas Nowotny

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Nowotny. A scholar is included among the top collaborators of Thomas Nowotny 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 Nowotny. Thomas Nowotny is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Nowotny, Thomas, James P. Turner, & James C. Knight. (2025). Loss shaping enhances exact gradient learning with Eventprop in spiking neural networks. Neuromorphic Computing and Engineering. 5(1). 14001–14001. 6 indexed citations

Pleßer, Hans Ekkehard, Andrew P. Davison, Markus Diesmann, et al.. (2025). Building on models—a perspective for computational neuroscience. Cerebral Cortex. 35(11). 1 indexed citations

Knight, James C., et al.. (2025). Efficient event-based delay learning in spiking neural networks. Nature Communications. 16(1). 10422–10422.

Knight, James C., et al.. (2024). Learning delays through gradients and structure: emergence of spatiotemporal patterns in spiking neural networks. Frontiers in Computational Neuroscience. 18. 1460309–1460309. 1 indexed citations

Knight, James C., et al.. (2024). Adaptive Route Memory Sequences for Insect-Inspired Visual Route Navigation. Biomimetics. 9(12). 731–731. 1 indexed citations

Kriener, Laura, et al.. (2024). Learning efficient backprojections across cortical hierarchies in real time. Nature Machine Intelligence. 6(6). 619–630. 3 indexed citations

Nowotny, Thomas, et al.. (2023). Insect-inspired Spatio-temporal Downsampling of Event-based Input. 1–5.

Knight, James C. & Thomas Nowotny. (2023). Easy and efficient spike-based Machine Learning with mlGeNN. 115–120. 4 indexed citations

Ogawa, Yuri, Sarah Nicholas, Richard Leibbrandt, et al.. (2023). Descending neurons of the hoverfly respond to pursuits of artificial targets. Current Biology. 33(20). 4392–4404.e5. 4 indexed citations

10.

Nowotny, Thomas, et al.. (2023). Robustness of the Infomax Network for View Based Navigation of Long Routes. 4 indexed citations

11.

Turner, James P., et al.. (2022). mlGeNN: accelerating SNN inference using GPU-enabled neural networks. Neuromorphic Computing and Engineering. 2(2). 24002–24002. 7 indexed citations

12.

Philippides, Andrew, et al.. (2021). Learning with reinforcement prediction errors in a model of the Drosophila mushroom body. Nature Communications. 12(1). 2569–2569. 33 indexed citations

13.

Knight, James C. & Thomas Nowotny. (2021). Larger GPU-accelerated brain simulations with procedural connectivity. Nature Computational Science. 1(2). 136–142. 34 indexed citations

14.

Pannunzi, Mario & Thomas Nowotny. (2021). Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies. PLoS Computational Biology. 17(12). e1009583–e1009583. 6 indexed citations

15.

Stimberg, Marcel, Dan F. M. Goodman, & Thomas Nowotny. (2020). Brian2GeNN: accelerating spiking neural network simulations with graphics hardware. Scientific Reports. 10(1). 29 indexed citations

16.

Schmuker, Michael, et al.. (2019). An unsupervised neuromorphic clustering algorithm. Biological Cybernetics. 113(4). 423–437. 13 indexed citations

17.

Reeve, Hayley M., Thomas Nowotny, Johannes Hirrlinger, et al.. (2019). The Emergence of a Stable Neuronal Ensemble from a Wider Pool of Activated Neurons in the Dorsal Medial Prefrontal Cortex during Appetitive Learning in Mice. Journal of Neuroscience. 40(2). 395–410. 20 indexed citations

18.

Smith, Brian H., et al.. (2018). Odorant mixtures elicit less variable and faster responses than pure odorants. PLoS Computational Biology. 14(12). e1006536–e1006536. 21 indexed citations

19.

Nowotny, Thomas, et al.. (2016). Olfactory experience shapes the evaluation of odour similarity in ants: a behavioural and computational analysis. Proceedings of the Royal Society B Biological Sciences. 283(1837). 20160551–20160551. 12 indexed citations

20.

Muezzinoglu, Mehmet K., Alexander Vergara, Ramón Huerta, et al.. (2008). Artificial Olfactory Brain for Mixture Identification. Neural Information Processing Systems. 21. 1121–1128. 10 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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