Andrea Soltoggio
- Artificial Intelligence top 5%
- Reinforcement Learning in Robotics 11
- Neural Networks and Applications 6
- Cognitive Neuroscience top 10%
- Neural dynamics and brain function 11
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- Advanced Memory and Neural Computing 8
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- Neuroscience and Neural Engineering 5
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- Robot Manipulation and Learning 4
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- Metabolomics and Mass Spectrometry Studies 3
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- Human-Automation Interaction and Safety 3
- Co-authors
- Peter DürrClaudio MattiussiKenneth O. StanleyQinggang MengGerald SchaeferAmanda WhitbrookYang HuSebastian Risi
- Journals
- Neural Networks (5 papers)Frontiers in Neurorobotics (2 papers)IEEE Transactions on Cybernetics (1 paper)
- Partner nations
- United KingdomUnited StatesGermany
In The Last Decade
Andrea Soltoggio
41 papers receiving 670 citations
Peers
Comparison fields: 5 of 102
- Artificial Intelligence 267
- Computer Vision and Pattern Recognition 150
- Cognitive Neuroscience 128
- Computer Networks and Communications 112
- Industrial and Manufacturing Engineering 44
Countries citing papers authored by Andrea Soltoggio
This map shows the geographic impact of Andrea Soltoggio'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 Andrea Soltoggio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrea Soltoggio more than expected).
Fields of papers citing papers by Andrea Soltoggio
This network shows the impact of papers produced by Andrea Soltoggio. 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 Andrea Soltoggio. The network helps show where Andrea Soltoggio may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Andrea Soltoggio, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 4 | |
| 3 | 2022 | 4 | |
| 4 | 2022 | 7 | |
| 5 | Sliced Cramer Synaptic Consolidation for Preserving Deeply Learned Representations | 2020 | 4 |
| 6 | 2020 | 6 | |
| 7 | 2020 | 8 | |
| 8 | 2020 | 22 | |
| 9 | 2018 | 9 | |
| 10 | 2018 | 68 | |
| 11 | 2018 | 56 | |
| 12 | Online Representation Learning with Multi-layer Hebbian Networks for Image Classification Tasks. | 2017 | 1 |
| 13 | 2014 | 1 | |
| 14 | 2014 | 1 | |
| 15 | 2014 | 2 | |
| 16 | 2014 | 1 | |
| 17 | 2012 | 21 | |
| 18 | Neuromodulation Increases Decision Speed in Dynamic Environments | 2008 | 5 |
| 19 | Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios | 2008 | 75 |
| 20 | 2007 | 49 |
About Andrea Soltoggio
Andrea Soltoggio is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Cellular and Molecular Neuroscience, having authored 43 papers that have together received 694 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (11 papers), Neural dynamics and brain function (11 papers), Advanced Memory and Neural Computing (8 papers), Neural Networks and Applications (6 papers), Neuroscience and Neural Engineering (5 papers), Robot Manipulation and Learning (4 papers), Metabolomics and Mass Spectrometry Studies (3 papers) and Human-Automation Interaction and Safety (3 papers). The work is most often cited by research in Artificial Intelligence (267 citations), Computer Vision and Pattern Recognition (150 citations) and Cognitive Neuroscience (128 citations). Andrea Soltoggio has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Peter Dürr, Claudio Mattiussi, Kenneth O. Stanley, Qinggang Meng, Gerald Schaefer, Amanda Whitbrook, Yang Hu, Sebastian Risi, Russell Lock and Dario Floreano. Their work appears in journals such as Neural Networks, Frontiers in Neurorobotics, IEEE Transactions on Cybernetics, Analytical Chemistry and Machine Learning Science and Technology.
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.