Nicolas Schweighofer

6.9k total citations
114 papers, 4.4k citations indexed

About

Nicolas Schweighofer is a scholar working on Cognitive Neuroscience, Rehabilitation and Biomedical Engineering. According to data from OpenAlex, Nicolas Schweighofer has authored 114 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 75 papers in Cognitive Neuroscience, 38 papers in Rehabilitation and 28 papers in Biomedical Engineering. Recurrent topics in Nicolas Schweighofer's work include Motor Control and Adaptation (47 papers), Stroke Rehabilitation and Recovery (38 papers) and Muscle activation and electromyography studies (27 papers). Nicolas Schweighofer is often cited by papers focused on Motor Control and Adaptation (47 papers), Stroke Rehabilitation and Recovery (38 papers) and Muscle activation and electromyography studies (27 papers). Nicolas Schweighofer collaborates with scholars based in United States, Japan and France. Nicolas Schweighofer's co-authors include Kenji Doya, Michael A. Arbib, Mitsuo Kawato, Cheol E. Han, Carolee J. Winstein, Jeong-Yoon Lee, Shinya Kuroda, Saori Tanaka, Yasumasa Okamoto and Charalambos Papaxanthis and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Journal of Neuroscience.

In The Last Decade

Nicolas Schweighofer

110 papers receiving 4.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nicolas Schweighofer United States 40 2.6k 960 889 812 724 114 4.4k
Annette Sterr United Kingdom 44 2.6k 1.0× 1.1k 1.1× 946 1.1× 489 0.6× 501 0.7× 127 5.3k
Pietro Mazzoni United States 32 3.4k 1.3× 551 0.6× 363 0.4× 1.1k 1.4× 1.0k 1.4× 63 5.0k
Hubert R. Dinse Germany 46 4.6k 1.8× 1.8k 1.8× 462 0.5× 697 0.9× 554 0.8× 155 7.1k
Guy Chéron Belgium 39 2.6k 1.0× 1.2k 1.3× 328 0.4× 1.3k 1.6× 309 0.4× 171 5.9k
Eiichi Naito Japan 32 3.5k 1.3× 905 0.9× 291 0.3× 591 0.7× 1.4k 1.9× 102 4.7k
Paul Bach‐y‐Rita United States 41 4.4k 1.7× 875 0.9× 681 0.8× 990 1.2× 467 0.6× 139 6.7k
Maurice A. Smith United States 26 4.0k 1.6× 681 0.7× 241 0.3× 1.8k 2.2× 1.6k 2.3× 51 4.9k
José L. Contreras-Vidal United States 46 5.2k 2.0× 351 0.4× 1.0k 1.1× 2.9k 3.5× 566 0.8× 200 7.4k
Andrea Carìa Italy 27 3.5k 1.4× 340 0.4× 661 0.7× 1.0k 1.3× 314 0.4× 47 4.3k
Deborah L. Harrington United States 48 6.1k 2.3× 746 0.8× 286 0.3× 443 0.5× 1.2k 1.6× 96 8.0k

Countries citing papers authored by Nicolas Schweighofer

Since Specialization
Citations

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

Fields of papers citing papers by Nicolas Schweighofer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nicolas Schweighofer

This figure shows the co-authorship network connecting the top 25 collaborators of Nicolas Schweighofer. A scholar is included among the top collaborators of Nicolas Schweighofer 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 Nicolas Schweighofer. Nicolas Schweighofer 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.
Zhang, Yifan, et al.. (2025). Two ways to learn in visuomotor adaptation. Journal of Neurophysiology. 134(4). 1085–1096.
2.
Luo, Haipeng, et al.. (2025). Towards AI-based precision rehabilitation via contextual model-based reinforcement learning. Journal of NeuroEngineering and Rehabilitation. 22(1). 263–263.
3.
Schweighofer, Nicolas, et al.. (2024). Force reserve predicts compensation in reaching movement with induced shoulder strength deficit. Journal of Neurophysiology. 132(2). 470–484.
4.
Schweighofer, Nicolas, et al.. (2023). Long-term forecasting of a motor outcome following rehabilitation in chronic stroke via a hierarchical bayesian dynamic model. Journal of NeuroEngineering and Rehabilitation. 20(1). 83–83. 4 indexed citations
5.
Sánchez, Natalia, Nicolas Schweighofer, Sara J. Mulroy, et al.. (2023). Multi-Site Identification and Generalization of Clusters of Walking Behaviors in Individuals With Chronic Stroke and Neurotypical Controls. Neurorehabilitation and neural repair. 37(11-12). 810–822. 4 indexed citations
6.
Schweighofer, Nicolas, et al.. (2023). Reinforcement learning establishes a minimal metacognitive process to monitor and control motor learning performance. Nature Communications. 14(1). 3988–3988. 11 indexed citations
7.
Kim, Sujin, Cheol E. Han, Bokkyu Kim, Carolee J. Winstein, & Nicolas Schweighofer. (2021). Effort, success, and side of lesion determine arm choice in individuals with chronic stroke. Journal of Neurophysiology. 127(1). 255–266. 13 indexed citations
8.
Ito, Kaori, Bokkyu Kim, Jingchun Liu, et al.. (2021). Corticospinal Tract Lesion Load Originating From Both Ventral Premotor and Primary Motor Cortices Are Associated With Post-stroke Motor Severity. Neurorehabilitation and neural repair. 36(3). 179–182. 14 indexed citations
9.
Kambara, Hiroyuki, Atsushi Takagi, Toshihiro Kawase, et al.. (2021). Computational reproductions of external force field adaption without assuming desired trajectories. Neural Networks. 139. 179–198. 3 indexed citations
10.
Kim, Bokkyu, Nicolas Schweighofer, Justin P. Haldar, Richard M. Leahy, & Carolee J. Winstein. (2021). Corticospinal Tract Microstructure Predicts Distal Arm Motor Improvements in Chronic Stroke. Journal of Neurologic Physical Therapy. 45(4). 273–281. 13 indexed citations
11.
Wang, Chunji, Carolee J. Winstein, David Z. D’Argenio, & Nicolas Schweighofer. (2020). The Efficiency, Efficacy, and Retention of Task Practice in Chronic Stroke. Neurorehabilitation and neural repair. 34(10). 881–890. 18 indexed citations
12.
Kutch, Jason J., et al.. (2020). When 90% of the variance is not enough: residual EMG from muscle synergy extraction influences task performance. Journal of Neurophysiology. 123(6). 2180–2190. 25 indexed citations
13.
Kim, Sujin, et al.. (2018). Measuring Habitual Arm Use Post-stroke With a Bilateral Time-Constrained Reaching Task. Frontiers in Neurology. 9. 883–883. 10 indexed citations
14.
Wang, Chunji, et al.. (2016). The duration of reaching movement is longer than predicted by minimum variance. Journal of Neurophysiology. 116(5). 2342–2345. 27 indexed citations
15.
Mottet, Denis, et al.. (2015). Quantification of learned non-use of the upper limb after a stroke. Annals of Physical and Rehabilitation Medicine. 58. e4–e4. 1 indexed citations
16.
Schweighofer, Nicolas, Eric J. Lang, & Mitsuo Kawato. (2013). Role of the olivo-cerebellar complex in motor learning and control. Frontiers in Neural Circuits. 7. 94–94. 44 indexed citations
17.
Wu, Allan D., et al.. (2010). Fast estimation of transcranial magnetic stimulation motor threshold. Brain stimulation. 4(1). 50–57. 39 indexed citations
18.
Tanaka, Saori, Kazuhiro Shishida, Nicolas Schweighofer, et al.. (2009). Serotonin Affects Association of Aversive Outcomes to Past Actions. Journal of Neuroscience. 29(50). 15669–15674. 45 indexed citations
19.
Choi, Younggeun, et al.. (2008). Performance-Based Adaptive Schedules Enhance Motor Learning. Journal of Motor Behavior. 40(4). 273–280. 46 indexed citations
20.
Schweighofer, Nicolas, et al.. (2007). Multiple model-based reinforcement learning explains dopamine neuronal activity. Neural Networks. 20(6). 668–675. 13 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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