Talmo Pereira

3.3k total citations · 2 hit papers
17 papers, 1.1k citations indexed

About

Talmo Pereira is a scholar working on Cell Biology, Cellular and Molecular Neuroscience and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Talmo Pereira has authored 17 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Cell Biology, 4 papers in Cellular and Molecular Neuroscience and 4 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Talmo Pereira's work include Zebrafish Biomedical Research Applications (5 papers), Animal Behavior and Reproduction (3 papers) and Neurobiology and Insect Physiology Research (3 papers). Talmo Pereira is often cited by papers focused on Zebrafish Biomedical Research Applications (5 papers), Animal Behavior and Reproduction (3 papers) and Neurobiology and Insect Physiology Research (3 papers). Talmo Pereira collaborates with scholars based in United States, Netherlands and Germany. Talmo Pereira's co-authors include Mala Murthy, Joshua W. Shaevitz, Samuel S.‐H. Wang, Diego Aldarondo, Lindsay Willmore, Mikhail Kislin, Ben Deverett, Aleksandra Badura, Jessica L. Verpeut and Thomas J. Pisano and has published in prestigious journals such as Neuron, Nature Neuroscience and Nature Methods.

In The Last Decade

Talmo Pereira

15 papers receiving 1.0k citations

Hit Papers

Fast animal pose estimation using deep neural networks 2018 2026 2020 2023 2018 2024 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Fast animal pose estimation using deep neural networks
    2018 387 citations Talmo Pereira, Diego Aldarondo et al. Nature Methods profile →
  2. Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics
    2024 48 citations Caleb Weinreb, Sherry Lin et al. Nature Methods profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Talmo Pereira United States 11 312 253 189 166 162 17 1.1k
Mikhail Kislin Finland 12 207 0.7× 242 1.0× 151 0.8× 143 0.9× 97 0.6× 31 972
Tanmay Nath United States 11 422 1.4× 227 0.9× 46 0.2× 174 1.0× 133 0.8× 18 1.4k
Ann Kennedy United States 15 336 1.1× 320 1.3× 75 0.4× 88 0.5× 88 0.5× 27 1.1k
David G. C. Hildebrand United States 14 197 0.6× 220 0.9× 83 0.4× 203 1.2× 60 0.4× 23 988
Ruud A.J. Tegelenbosch Netherlands 7 146 0.5× 258 1.0× 51 0.3× 344 2.1× 125 0.8× 7 1.2k
Ralph E. Peterson United States 4 384 1.2× 394 1.6× 34 0.2× 164 1.0× 48 0.3× 6 911
Mayank Kabra India 7 356 1.1× 264 1.0× 28 0.1× 66 0.4× 108 0.7× 20 840
Taiga Abe United States 3 963 3.1× 755 3.0× 117 0.6× 481 2.9× 187 1.2× 6 2.6k
Winthrop F. Gillis United States 10 331 1.1× 367 1.5× 58 0.3× 120 0.7× 44 0.3× 14 776
Johannes Kohl Germany 16 214 0.7× 551 2.2× 59 0.3× 83 0.5× 217 1.3× 32 1.6k

Countries citing papers authored by Talmo Pereira

Since Specialization
Citations

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

Fields of papers citing papers by Talmo Pereira

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Talmo Pereira

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

All Works

17 of 17 papers shown
1.
Ignatowska‐Jankowska, Bogna M., et al.. (2025). Deep Imputation for Skeleton data (DISK) for behavioral science. Nature Methods. 23(1). 236–247.
2.
Pereira, Talmo, et al.. (2025). The utility of animal models to inform the next generation of human space exploration. npj Microgravity. 11(1). 7–7.
3.
Weinreb, Caleb, Sherry Lin, Mohammed Abdal Monium Osman, et al.. (2024). Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics. Nature Methods. 21(7). 1329–1339. 48 indexed citations breakdown →
4.
Gepshtein, Sergei, Thomas A. Albright, Nick Andrews, et al.. (2023). Quantifying Behavior Using Deep Learning. Biological Psychiatry. 93(9). S7–S7. 1 indexed citations
5.
Krishnan, Keerthi, et al.. (2023). Open-source tools for behavioral video analysis: Setup, methods, and best practices. eLife. 12. 38 indexed citations
6.
Gall, Gabriella E. C., Talmo Pereira, Alex Jordan, & Yasmine Meroz. (2022). Fast estimation of plant growth dynamics using deep neural networks. Plant Methods. 18(1). 21–21. 8 indexed citations
7.
Wang, Z. Yan, Grace C. McKenzie‐Smith, Weijie Liu, et al.. (2022). Isolation disrupts social interactions and destabilizes brain development in bumblebees. Current Biology. 32(12). 2754–2764.e5. 18 indexed citations
8.
Jones, Jessica, William Foster, Colin R. Twomey, et al.. (2020). A machine-vision approach for automated pain measurement at millisecond timescales. eLife. 9. 39 indexed citations
9.
Pereira, Talmo, Joshua W. Shaevitz, & Mala Murthy. (2020). Quantifying behavior to understand the brain. Nature Neuroscience. 23(12). 1537–1549. 161 indexed citations
10.
Charles, Adam S., Nicholas L. Turner, Talmo Pereira, et al.. (2020). Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics. Annual Review of Neuroscience. 43(1). 441–464. 11 indexed citations
11.
Badura, Aleksandra, Jessica L. Verpeut, Talmo Pereira, et al.. (2018). Normal cognitive and social development require posterior cerebellar activity. eLife. 7. 129 indexed citations
12.
Clemens, Jan, Philip Coen, Talmo Pereira, et al.. (2018). Discovery of a New Song Mode in Drosophila Reveals Hidden Structure in the Sensory and Neural Drivers of Behavior. Current Biology. 28(15). 2400–2412.e6. 45 indexed citations
13.
Pereira, Talmo, Diego Aldarondo, Lindsay Willmore, et al.. (2018). Fast animal pose estimation using deep neural networks. Nature Methods. 16(1). 117–125. 387 indexed citations breakdown →
14.
Giovannucci, Andrea, et al.. (2017). Automated gesture tracking in head-fixed mice. Journal of Neuroscience Methods. 300. 184–195. 13 indexed citations
15.
Pereira, Talmo & Mala Murthy. (2017). To Fight or Not to Fight. Neuron. 95(5). 986–988. 3 indexed citations
16.
Giovannucci, Andrea, Aleksandra Badura, Ben Deverett, et al.. (2017). Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning. Nature Neuroscience. 20(5). 727–734. 144 indexed citations
17.
Pereira, Talmo, et al.. (2016). A Bayesian inference method for the analysis of transcriptional regulatory networks in metagenomic data. Algorithms for Molecular Biology. 11(1). 19–19. 8 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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