Ari E. Kahn

2.5k total citations · 1 hit paper
21 papers, 1.1k citations indexed

About

Ari E. Kahn is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Ari E. Kahn has authored 21 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Cognitive Neuroscience, 4 papers in Radiology, Nuclear Medicine and Imaging and 3 papers in Molecular Biology. Recurrent topics in Ari E. Kahn's work include Neural dynamics and brain function (11 papers), Functional Brain Connectivity Studies (10 papers) and EEG and Brain-Computer Interfaces (4 papers). Ari E. Kahn is often cited by papers focused on Neural dynamics and brain function (11 papers), Functional Brain Connectivity Studies (10 papers) and EEG and Brain-Computer Interfaces (4 papers). Ari E. Kahn collaborates with scholars based in United States, France and Australia. Ari E. Kahn's co-authors include Danielle S. Bassett, Jean M. Vettel, Fabio Pasqualetti, John D. Medaglia, Qawi K. Telesford, Alfred B. Yu, Scott T. Grafton, Matthew Cieslak, Shi Gu and Michael B. Miller and has published in prestigious journals such as Nature Communications, Neuron and NeuroImage.

In The Last Decade

Ari E. Kahn

19 papers receiving 1.1k citations

Hit Papers

Controllability of struct... 2015 2026 2018 2022 2015 100 200 300 400 500

Author Peers

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

Author Last Decade Papers Cites
Ari E. Kahn 846 226 176 99 99 21 1.1k
Shi Gu 1.1k 1.3× 339 1.5× 243 1.4× 167 1.7× 102 1.0× 42 1.6k
S.C. Ponten 1.0k 1.2× 218 1.0× 80 0.5× 59 0.6× 103 1.0× 9 1.1k
Alfred B. Yu 535 0.6× 143 0.6× 101 0.6× 69 0.7× 55 0.6× 17 837
Michael Vourkas 1.0k 1.2× 192 0.8× 174 1.0× 55 0.6× 57 0.6× 15 1.1k
Sarah F. Muldoon 703 0.8× 131 0.6× 115 0.7× 124 1.3× 156 1.6× 40 1.1k
T. Montez 1.5k 1.8× 229 1.0× 156 0.9× 84 0.8× 57 0.6× 7 1.6k
Søren Rahn Christensen 908 1.1× 117 0.5× 158 0.9× 161 1.6× 95 1.0× 23 1.2k
Yonatan Sanz Perl 721 0.9× 139 0.6× 101 0.6× 38 0.4× 111 1.1× 74 1.1k
Bhim M. Adhikari 644 0.8× 156 0.7× 138 0.8× 49 0.5× 172 1.7× 54 942
Andreas Spiegler 1.2k 1.4× 354 1.6× 73 0.4× 55 0.6× 131 1.3× 23 1.3k

Countries citing papers authored by Ari E. Kahn

Since Specialization
Citations

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

Fields of papers citing papers by Ari E. Kahn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ari E. Kahn. 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 Ari E. Kahn. The network helps show where Ari E. Kahn may publish in the future.

Co-authorship network of co-authors of Ari E. Kahn

This figure shows the co-authorship network connecting the top 25 collaborators of Ari E. Kahn. A scholar is included among the top collaborators of Ari E. Kahn 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 Ari E. Kahn. Ari E. Kahn 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.
Kahn, Ari E., et al.. (2025). Network structure influences the strength of learned neural representations. Nature Communications. 16(1). 994–994.
2.
Kahn, Ari E. & Nathaniel D. Daw. (2025). Humans rationally balance detailed and temporally abstract world models. Communications Psychology. 3(1). 1–1. 1 indexed citations
3.
Kahn, Ari E., Danielle S. Bassett, & Nathaniel D. Daw. (2025). Trial-by-trial learning of successor representations in human behavior. PLoS Computational Biology. 21(11). e1013696–e1013696.
4.
Cornblath, Eli J., David M. Lydon‐Staley, Dale Zhou, et al.. (2023). Alprazolam modulates persistence energy during emotion processing in first-degree relatives of individuals with schizophrenia: a network control study. Molecular Psychiatry. 28(8). 3314–3323. 6 indexed citations
5.
Kahn, Ari E., et al.. (2023). Dual credit assignment processes underlie dopamine signals in a complex spatial environment. Neuron. 111(21). 3465–3478.e7. 19 indexed citations
6.
Corsi, Marie‐Constance, Pierpaolo Sorrentino, Denis Schwartz, et al.. (2023). Measuring neuronal avalanches to inform brain-computer interfaces. iScience. 27(1). 108734–108734. 4 indexed citations
7.
Stiso, Jennifer, Christopher W. Lynn, Ari E. Kahn, et al.. (2022). Neurophysiological Evidence for Cognitive Map Formation during Sequence Learning. eNeuro. 9(2). ENEURO.0361–21.2022. 7 indexed citations
8.
Corsi, Marie‐Constance, Mario Chávez, Denis Schwartz, et al.. (2020). BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks. arXiv (Cornell University). 5 indexed citations
9.
Corsi, Marie‐Constance, Mario Chávez, Denis Schwartz, et al.. (2020). Functional disconnection of associative cortical areas predicts performance during BCI training. NeuroImage. 209. 116500–116500. 25 indexed citations
10.
Tompson, Steven, Ari E. Kahn, Emily B. Falk, Jean M. Vettel, & Danielle S. Bassett. (2020). Functional brain network architecture supporting the learning of social networks in humans. NeuroImage. 210. 116498–116498. 27 indexed citations
11.
Kahn, Ari E., Christopher W. Lynn, Lia Papadopoulos, & Danielle S. Bassett. (2019). Human Information Processing in Complex Networks. Bulletin of the American Physical Society. 2019. 1 indexed citations
12.
Betzel, Richard F., et al.. (2019). Structural, geometric and genetic factors predict interregional brain connectivity patterns probed by electrocorticography. Nature Biomedical Engineering. 3(11). 902–916. 76 indexed citations
13.
Karuza, Elisabeth A., Ari E. Kahn, & Danielle S. Bassett. (2019). Human Sensitivity to Community Structure Is Robust to Topological Variation. Complexity. 2019(1). 18 indexed citations
14.
Tompson, Steven, Ari E. Kahn, Emily B. Falk, Jean M. Vettel, & Danielle S. Bassett. (2018). Individual differences in learning social and nonsocial network structures.. Journal of Experimental Psychology Learning Memory and Cognition. 45(2). 253–271. 19 indexed citations
15.
Kim, Jason Z., et al.. (2017). Role of graph architecture in controlling dynamical networks with applications to neural systems. Nature Physics. 14(1). 91–98. 77 indexed citations
16.
Baum, Graham L., Rastko Ćirić, David R. Roalf, et al.. (2017). Modular Segregation of Structural Brain Networks Supports the Development of Executive Function in Youth. Current Biology. 27(11). 1561–1572.e8. 245 indexed citations
17.
Tang, Evelyn, Chad Giusti, Graham L. Baum, et al.. (2016). Structural drivers of diverse neural dynamics and their evolution across development. 3 indexed citations
18.
Gu, Shi, Fabio Pasqualetti, Matthew Cieslak, et al.. (2015). Controllability of structural brain networks. Nature Communications. 6(1). 8414–8414. 536 indexed citations breakdown →
19.
Hörne, F. R. & Ari E. Kahn. (2000). Water loss and viability in Zizania (Poaceae) seeds during short‐term desiccation. American Journal of Botany. 87(11). 1707–1711. 7 indexed citations
20.
Guiot, G, P Derome, & Ari E. Kahn. (1968). [The ventral posterior nucleus: an electrophysiological reference point in stereotaxic thalamotomies].. PubMed. 31(2). 112–8. 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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