Michail Maniadakis
- Cognitive Neuroscience top 10%
- Artificial Intelligence top 10%
- Control and Systems Engineering top 10%
- Experimental and Cognitive Psychology top 10%
- Social Psychology
- Co-authors
- Panos TrahaniasMaria KoskinopoulouSylvie Droit‐VoletGeorge PapadopoulosHartmut SurmannJun TaniGeorge A. RovithakisMichalis Zervakis
- Topics
- Neural dynamics and brain function (16 papers)Neuroscience and Music Perception (11 papers)Reinforcement Learning in Robotics (9 papers)
- Cited by
- Cognitive NeuroscienceIndustrial and Manufacturing EngineeringExperimental and Cognitive Psychology
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEIEEE Transactions on Biomedical Engineering
In The Last Decade
Michail Maniadakis
50 papers receiving 476 citations
Peers
Comparison fields: 5 of 97
- Cognitive Neuroscience 162
- Artificial Intelligence 118
- Control and Systems Engineering 70
- Experimental and Cognitive Psychology 70
- Social Psychology 66
Countries citing papers authored by Michail Maniadakis
This map shows the geographic impact of Michail Maniadakis'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 Michail Maniadakis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michail Maniadakis more than expected).
Fields of papers citing papers by Michail Maniadakis
This network shows the impact of papers produced by Michail Maniadakis. 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 Michail Maniadakis. The network helps show where Michail Maniadakis may publish in the future.
Co-authorship network of co-authors of Michail Maniadakis
This figure shows the co-authorship network connecting the top 25 collaborators of Michail Maniadakis. A scholar is included among the top collaborators of Michail Maniadakis 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 Michail Maniadakis. Michail Maniadakis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 86 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 4 | |
| 6 | 30 | |
| 7 | 4 | |
| 8 | 50 | |
| 9 | 13 | |
| 10 | 1 | |
| 11 | 2 | |
| 12 | 7 | |
| 13 | Experiencing and Processing Time with Neural Networks | 4 |
| 14 | Time Experiencing by Robotic Agents. | 3 |
| 15 | 26 | |
| 16 | 16 | |
| 17 | 6 | |
| 18 | 1 | |
| 19 | 15 | |
| 20 | 26 |
About Michail Maniadakis
Michail Maniadakis is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Industrial and Manufacturing Engineering, having authored 52 papers that have together received 503 indexed citations. Recurring topics across this work include Neural dynamics and brain function (16 papers), Neuroscience and Music Perception (11 papers) and Reinforcement Learning in Robotics (9 papers). The work is most often cited by research in Cognitive Neuroscience (162 citations), Industrial and Manufacturing Engineering (49 citations) and Experimental and Cognitive Psychology (70 citations). Michail Maniadakis has collaborated with scholars based in Greece, Germany and Japan. Frequent co-authors include Panos Trahanias, Maria Koskinopoulou, Sylvie Droit‐Volet, George Papadopoulos, Hartmut Surmann, Jun Tani, George A. Rovithakis, Michalis Zervakis, George Filippidis and M. Zervakis. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Transactions on Biomedical Engineering.
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.