Luca Ambrogioni
- Cognitive Neuroscience top 5%
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Radiology, Nuclear Medicine and Imaging
- Biophysics top 10%
- Co-authors
- Marcel van GervenEric MarisUmut GüçlüYağmur GüçlütürkKatja SeeligerW. Pieter MedendorpAlice TomassiniNadine Dijkstra
- Topics
- Neural dynamics and brain function (10 papers)Functional Brain Connectivity Studies (6 papers)Advanced Memory and Neural Computing (5 papers)
- Partner nations
- NetherlandsGermanyItaly
In The Last Decade
Luca Ambrogioni
21 papers receiving 382 citations
Peers
Comparison fields: 5 of 73
- Cognitive Neuroscience 264
- Computer Vision and Pattern Recognition 79
- Artificial Intelligence 41
- Radiology, Nuclear Medicine and Imaging 36
- Biophysics 35
Countries citing papers authored by Luca Ambrogioni
This map shows the geographic impact of Luca Ambrogioni'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 Luca Ambrogioni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luca Ambrogioni more than expected).
Fields of papers citing papers by Luca Ambrogioni
This network shows the impact of papers produced by Luca Ambrogioni. 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 Luca Ambrogioni. The network helps show where Luca Ambrogioni may publish in the future.
Co-authorship network of co-authors of Luca Ambrogioni
This figure shows the co-authorship network connecting the top 25 collaborators of Luca Ambrogioni. A scholar is included among the top collaborators of Luca Ambrogioni 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 Luca Ambrogioni. Luca Ambrogioni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 0 | |
| 3 | 8 | |
| 4 | 5 | |
| 5 | 7 | |
| 6 | 20 | |
| 7 | 6 | |
| 8 | 0 | |
| 9 | 17 | |
| 10 | 1 | |
| 11 | GAIT-prop: A biologically plausible learning rule derived from backpropagation of error | 1 |
| 12 | 6 | |
| 13 | 29 | |
| 14 | 1 | |
| 15 | 5 | |
| 16 | 97 | |
| 17 | 79 | |
| 18 | Bayesian model ensembling using meta-trained recurrent neural networks | 1 |
| 19 | 7 | |
| 20 | 36 |
About Luca Ambrogioni
Luca Ambrogioni is a scholar working on Cognitive Neuroscience, Biophysics and Artificial Intelligence, having authored 24 papers that have together received 387 indexed citations. Recurring topics across this work include Neural dynamics and brain function (10 papers), Functional Brain Connectivity Studies (6 papers) and Advanced Memory and Neural Computing (5 papers). The work is most often cited by research in Cognitive Neuroscience (264 citations), Biophysics (35 citations) and Computer Vision and Pattern Recognition (79 citations). Luca Ambrogioni has collaborated with scholars based in Netherlands, Germany and Italy. Frequent co-authors include Marcel van Gerven, Eric Maris, Umut Güçlü, Yağmur Güçlütürk, Katja Seeliger, W. Pieter Medendorp, Alice Tomassini, Nadine Dijkstra, Diego Vidaurre and Max Hinne. Their work appears in journals such as PLoS ONE, NeuroImage and Trends in Cognitive Sciences.
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.