Fabio Anselmi

1.7k total citations
35 papers, 1.1k citations indexed

About

Fabio Anselmi is a scholar working on Artificial Intelligence, Molecular Biology and Cognitive Neuroscience. According to data from OpenAlex, Fabio Anselmi has authored 35 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 11 papers in Molecular Biology and 10 papers in Cognitive Neuroscience. Recurrent topics in Fabio Anselmi's work include Neural dynamics and brain function (9 papers), Neural Networks and Applications (7 papers) and Hearing, Cochlea, Tinnitus, Genetics (6 papers). Fabio Anselmi is often cited by papers focused on Neural dynamics and brain function (9 papers), Neural Networks and Applications (7 papers) and Hearing, Cochlea, Tinnitus, Genetics (6 papers). Fabio Anselmi collaborates with scholars based in Italy, United States and United Kingdom. Fabio Anselmi's co-authors include Fabio Mammano, Tomaso Poggio, Giulia Crispino, Saida Ortolano, Lorenzo Rosasco, Howard M. Wiseman, Antonella Viola, Stephen D. Roper, William D. Richardson and Victor H. Hernández and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and The EMBO Journal.

In The Last Decade

Fabio Anselmi

33 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fabio Anselmi Italy 17 453 298 203 168 124 35 1.1k
Uri Manor United States 22 1.1k 2.5× 312 1.0× 12 0.1× 67 0.4× 82 0.7× 54 2.0k
Jacques Boutet de Monvel France 21 508 1.1× 889 3.0× 28 0.1× 438 2.6× 36 0.3× 49 1.4k
Michalis Michaelos United States 8 887 2.0× 26 0.1× 149 0.7× 152 0.9× 26 0.2× 8 1.9k
Hunter Elliott United States 17 971 2.1× 43 0.1× 26 0.1× 115 0.7× 21 0.2× 31 2.0k
Geoffrey J. Goodhill Australia 33 974 2.2× 45 0.2× 162 0.8× 988 5.9× 79 0.6× 119 3.1k
Andrew Dean United Kingdom 29 453 1.0× 68 0.2× 75 0.4× 1.6k 9.6× 33 0.3× 57 3.4k
C. Shan Xu United States 21 1.5k 3.3× 34 0.1× 21 0.1× 86 0.5× 23 0.2× 49 3.0k
Jérôme Boulanger France 26 1.2k 2.5× 34 0.1× 43 0.2× 110 0.7× 10 0.1× 67 2.8k
Tim Wang China 2 877 1.9× 16 0.1× 142 0.7× 63 0.4× 22 0.2× 5 1.7k
Cody J. Smith United States 19 337 0.7× 27 0.1× 29 0.1× 35 0.2× 174 1.4× 46 1.4k

Countries citing papers authored by Fabio Anselmi

Since Specialization
Citations

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

Fields of papers citing papers by Fabio Anselmi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fabio Anselmi

This figure shows the co-authorship network connecting the top 25 collaborators of Fabio Anselmi. A scholar is included among the top collaborators of Fabio Anselmi 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 Fabio Anselmi. Fabio Anselmi 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.
Smirne, Andrea, et al.. (2025). Driving enhanced exciton transfer by automatic differentiation. Machine Learning Science and Technology. 6(2). 25034–25034.
2.
Househam, Jacob, Marc Williams, Fabio Anselmi, et al.. (2024). Computational validation of clonal and subclonal copy number alterations from bulk tumor sequencing using CNAqc. Genome biology. 25(1). 38–38. 4 indexed citations
3.
Dey, Sourav, et al.. (2024). Translational symmetry in convolutions with localized kernels causes an implicit bias toward high frequency adversarial examples. Frontiers in Computational Neuroscience. 18. 1387077–1387077.
4.
Li, Zhe, Evgenia Rusak, Wieland Brendel, et al.. (2023). Robust deep learning object recognition models rely on low frequency information in natural images. PLoS Computational Biology. 19(3). e1010932–e1010932. 12 indexed citations
5.
d’Onofrio, Alberto, et al.. (2023). A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing. PLoS Computational Biology. 19(11). e1011557–e1011557. 2 indexed citations
6.
Anselmi, Fabio, et al.. (2023). Generative abstraction of Markov population processes. Theoretical Computer Science. 977. 114169–114169. 1 indexed citations
7.
Franceschiello, Benedetta, Alexia Bourgeois, Micah M. Murray, et al.. (2022). Machine learning algorithms on eye tracking trajectories to classify patients with spatial neglect. Computer Methods and Programs in Biomedicine. 221. 106929–106929. 13 indexed citations
8.
Sahs, Justin, et al.. (2022). Shallow Univariate ReLU Networks as Splines: Initialization, Loss Surface, Hessian, and Gradient Flow Dynamics. Frontiers in Artificial Intelligence. 5. 889981–889981. 5 indexed citations
9.
Anselmi, Fabio, Ankit Patel, & Lorenzo Rosasco. (2020). Neurally plausible mechanisms for learning selective and invariant representations. SHILAP Revista de lepidopterología. 10(1). 12–12. 4 indexed citations
10.
Leibo, Joel Z., Qianli Liao, Fabio Anselmi, Winrich A. Freiwald, & Tomaso Poggio. (2016). View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation. Current Biology. 27(1). 62–67. 34 indexed citations
11.
Poggio, Tomaso & Fabio Anselmi. (2016). Visual Cortex and Deep Networks: Learning Invariant Representations. 18 indexed citations
12.
Anselmi, Fabio, Lorenzo Rosasco, & Tomaso Poggio. (2016). On invariance and selectivity in representation learning. Information and Inference A Journal of the IMA. 5(2). 134–158. 27 indexed citations
13.
Anselmi, Fabio, Joel Z. Leibo, Lorenzo Rosasco, et al.. (2015). Unsupervised learning of invariant representations. Theoretical Computer Science. 633. 112–121. 42 indexed citations
14.
Leibo, Joel Z., Qianli Liao, Fabio Anselmi, & Tomaso Poggio. (2015). The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. PLoS Computational Biology. 11(10). e1004390–e1004390. 23 indexed citations
15.
Wang, Chiuhui Mary, Cristina Ploia, Fabio Anselmi, Adélaïda Sarukhan, & Antonella Viola. (2014). Adenosine triphosphate acts as a paracrine signaling molecule to reduce the motility of T cells. The EMBO Journal. 33(12). 1354–1364. 55 indexed citations
16.
Campello, Silvia, et al.. (2010). Adhesion shapes T cells for prompt and sustained T‐cell receptor signalling. The EMBO Journal. 29(23). 4035–4047. 50 indexed citations
17.
Mammano, Fabio & Fabio Anselmi. (2009). Inner ear connexins, intercellular signalling and deafness. Audiological Medicine. 8(2). 50–55. 1 indexed citations
18.
Vaccaro, J. A., Fabio Anselmi, Howard M. Wiseman, & Kurt Jacobs. (2008). Tradeoff between extractable mechanical work, accessible entanglement, and ability to act as a reference system, under arbitrary superselection rules. Physical Review A. 77(3). 88 indexed citations
19.
Mammano, Fabio, Mario Bortolozzi, Saida Ortolano, & Fabio Anselmi. (2007). Ca2+ Signaling in the Inner Ear. Physiology. 22(2). 131–144. 44 indexed citations
20.
Callaini, Giuliano & Fabio Anselmi. (1988). Centrosome splitting during nuclear elongation in the Drosophila embryo. Experimental Cell Research. 178(2). 415–425. 25 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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