Giorgio A. Ascoli

13.0k total citations
212 papers, 6.1k citations indexed

About

Giorgio A. Ascoli is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Biophysics. According to data from OpenAlex, Giorgio A. Ascoli has authored 212 papers receiving a total of 6.1k indexed citations (citations by other indexed papers that have themselves been cited), including 101 papers in Cognitive Neuroscience, 83 papers in Cellular and Molecular Neuroscience and 77 papers in Biophysics. Recurrent topics in Giorgio A. Ascoli's work include Cell Image Analysis Techniques (75 papers), Neural dynamics and brain function (70 papers) and Neuroscience and Neuropharmacology Research (61 papers). Giorgio A. Ascoli is often cited by papers focused on Cell Image Analysis Techniques (75 papers), Neural dynamics and brain function (70 papers) and Neuroscience and Neuropharmacology Research (61 papers). Giorgio A. Ascoli collaborates with scholars based in United States, Italy and Japan. Giorgio A. Ascoli's co-authors include Duncan Donohue, Maryam Halavi, Alexei V. Samsonovich, Ruggero Scorcioni, Ruchi Parekh, Michele Migliore, Jeffrey L. Krichmar, Sridevi Polavaram, Carlo Bertucci and Slawomir J. Nasuto and has published in prestigious journals such as New England Journal of Medicine, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Giorgio A. Ascoli

207 papers receiving 6.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Giorgio A. Ascoli United States 43 2.4k 2.3k 2.2k 1.7k 564 212 6.1k
Marius Pachitariu United States 20 2.4k 1.0× 1.0k 0.4× 1.6k 0.7× 1.4k 0.8× 257 0.5× 31 5.0k
Moritz Helmstaedter Germany 36 2.2k 0.9× 1.2k 0.5× 2.4k 1.1× 1.2k 0.7× 138 0.2× 53 4.8k
Carsen Stringer United States 15 1.4k 0.6× 1.0k 0.4× 968 0.4× 1.4k 0.8× 278 0.5× 22 4.1k
Shaoqun Zeng China 38 762 0.3× 2.0k 0.8× 1.1k 0.5× 1.1k 0.6× 455 0.8× 238 5.0k
Maryann E. Martone United States 57 1.7k 0.7× 1.2k 0.5× 4.1k 1.9× 6.0k 3.5× 129 0.2× 230 12.2k
Philipp Keller United States 39 494 0.2× 4.1k 1.8× 931 0.4× 3.4k 2.0× 270 0.5× 77 8.1k
Liam Paninski United States 52 8.5k 3.5× 945 0.4× 5.6k 2.5× 1.5k 0.8× 79 0.1× 163 12.0k
Ju Lu United States 23 627 0.3× 782 0.3× 1.1k 0.5× 1.1k 0.6× 173 0.3× 62 3.2k
Elizabeth M. C. Hillman United States 46 1.9k 0.8× 1.2k 0.5× 1.5k 0.7× 909 0.5× 98 0.2× 132 7.6k
Narayanan Kasthuri United States 23 907 0.4× 553 0.2× 1.5k 0.7× 819 0.5× 55 0.1× 57 3.2k

Countries citing papers authored by Giorgio A. Ascoli

Since Specialization
Citations

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

Fields of papers citing papers by Giorgio A. Ascoli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Giorgio A. Ascoli. 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 Giorgio A. Ascoli. The network helps show where Giorgio A. Ascoli may publish in the future.

Co-authorship network of co-authors of Giorgio A. Ascoli

This figure shows the co-authorship network connecting the top 25 collaborators of Giorgio A. Ascoli. A scholar is included among the top collaborators of Giorgio A. Ascoli 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 Giorgio A. Ascoli. Giorgio A. Ascoli 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.
Wheeler, Diek W., et al.. (2025). Organization and Community Usage of a Neuron Type Circuitry Knowledge Base of the Hippocampal Formation. Biomedicines. 13(10). 2363–2363.
2.
Liu, Lijuan, Zhixi Yun, Hanbo Chen, et al.. (2025). Connectivity of single neurons classifies cell subtypes in mouse brains. Nature Methods. 22(4). 861–873. 1 indexed citations
3.
4.
Wheeler, Diek W. & Giorgio A. Ascoli. (2024). A novel method for clustering cellular data to improve classification. Neural Regeneration Research. 20(9). 2697–2705.
5.
Santarelli, Anthony, et al.. (2023). Combinatorial quantification of distinct neural projections from retrograde tracing. Nature Communications. 14(1). 7271–7271. 2 indexed citations
6.
7.
Nanda, Sumit, et al.. (2023). Local Microtubule and F-Actin Distributions Fully Constrain the Spatial Geometry of Drosophila Sensory Dendritic Arbors. International Journal of Molecular Sciences. 24(7). 6741–6741. 1 indexed citations
8.
Ascoli, Giorgio A., et al.. (2023). Bibliometric analysis of neuroscience publications quantifies the impact of data sharing. Bioinformatics. 39(12). 2 indexed citations
9.
Attili, Sarojini M., et al.. (2022). Quantification of neuron types in the rodent hippocampal formation by data mining and numerical optimization. European Journal of Neuroscience. 55(7). 1724–1741. 9 indexed citations
10.
Poline, Jean‐Baptiste, David N. Kennedy, Friedrich T. Sommer, et al.. (2022). Is Neuroscience FAIR? A Call for Collaborative Standardisation of Neuroscience Data. Neuroinformatics. 20(2). 507–512. 18 indexed citations
11.
Sánchez-Aguilera, Alberto, Diek W. Wheeler, Manuel Valero, et al.. (2021). An update to Hippocampome.org by integrating single-cell phenotypes with circuit function in vivo. PLoS Biology. 19(5). e3001213–e3001213. 21 indexed citations
12.
López‐Schier, Hernán, et al.. (2021). Quantitative neuronal morphometry by supervised and unsupervised learning. STAR Protocols. 2(4). 100867–100867. 9 indexed citations
13.
Anderson, K., Julie A. Harris, Lydia Ng, et al.. (2021). Highlights from the Era of Open Source Web-Based Tools. Journal of Neuroscience. 41(5). 927–936. 12 indexed citations
14.
Barreto, Ernest, et al.. (2020). Itinerant complexity in networks of intrinsically bursting neurons. Chaos An Interdisciplinary Journal of Nonlinear Science. 30(6). 61106–61106. 4 indexed citations
15.
Ascoli, Giorgio A.. (2020). Meet Our Editorial Board Member. Current Medicinal Chemistry. 27(1). 1–1. 16 indexed citations
16.
Armañanzas, Rubén & Giorgio A. Ascoli. (2015). Towards the automatic classification of neurons. Trends in Neurosciences. 38(5). 307–318. 71 indexed citations
17.
Giovagnoni, Andrea, et al.. (2009). Health technology assessment: principles, methods and current status. La radiologia medica. 114(5). 673–691. 7 indexed citations
18.
Ascoli, Giorgio A., et al.. (2004). Incorporating anatomically realistic cellular-level connectivity in neural network models of the rat hippocampus. Biosystems. 79(1-3). 173–181. 21 indexed citations
19.
Nasuto, Slawomir J., Jeffrey L. Krichmar, Ruggero Scorcioni, & Giorgio A. Ascoli. (2001). Algorithmic statistical analysis of electrophysiological data for the investigation of structure-activity relationship in single neurons. The Journal Of Hand Surgery. 37(10). 2160–4; quiz 2164. 1 indexed citations
20.
Ascoli, Giorgio A., et al.. (2001). Computer generation and quantitative morphometric analysis of virtual neurons. Anatomy and Embryology. 204(4). 283–301. 71 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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