Anand Subramoney
- Electrical and Electronic Engineering
- Cognitive Neuroscience top 10%
- Artificial Intelligence top 10%
- Cellular and Molecular Neuroscience
- Computer Vision and Pattern Recognition
- Co-authors
- Darjan SalajGuillaume BellecRobert LegensteinWolfgang MaassFranz ScherrArne RoennauJacques KaiserRüdiger Dillmann
- Topics
- Advanced Memory and Neural Computing (7 papers)Neural dynamics and brain function (6 papers)Neural Networks and Reservoir Computing (5 papers)
- Partner nations
- GermanyAustriaUnited Kingdom
In The Last Decade
Anand Subramoney
12 papers receiving 351 citations
Hit Papers
Peers
Comparison fields: 5 of 46
- Electrical and Electronic Engineering 286
- Cognitive Neuroscience 223
- Artificial Intelligence 161
- Cellular and Molecular Neuroscience 84
- Computer Vision and Pattern Recognition 8
Countries citing papers authored by Anand Subramoney
This map shows the geographic impact of Anand Subramoney'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 Anand Subramoney with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anand Subramoney more than expected).
Fields of papers citing papers by Anand Subramoney
This network shows the impact of papers produced by Anand Subramoney. 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 Anand Subramoney. The network helps show where Anand Subramoney may publish in the future.
Co-authorship network of co-authors of Anand Subramoney
This figure shows the co-authorship network connecting the top 25 collaborators of Anand Subramoney. A scholar is included among the top collaborators of Anand Subramoney 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 Anand Subramoney. Anand Subramoney is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 6 | |
| 4 | 3 | |
| 5 | 4 | |
| 6 | 1 | |
| 7 | 4 | |
| 8 | 34 | |
| 9 | A solution to the learning dilemma for recurrent networks of spiking neuronsbreakdown → | 269 |
| 10 | 0 | |
| 11 | Eligibility traces provide a data-inspired alternative to backpropagation through time | 5 |
| 12 | 16 | |
| 13 | Evaluating Modular Neuroevolution in Robotic Keepaway Soccer | 2 |
| 14 | 7 |
About Anand Subramoney
Anand Subramoney is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Electrical and Electronic Engineering, having authored 14 papers that have together received 353 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (7 papers), Neural dynamics and brain function (6 papers) and Neural Networks and Reservoir Computing (5 papers). The work is most often cited by research in Cognitive Neuroscience (223 citations), Artificial Intelligence (161 citations) and Electrical and Electronic Engineering (286 citations). Anand Subramoney has collaborated with scholars based in Germany, Austria and United Kingdom. Frequent co-authors include Darjan Salaj, Guillaume Bellec, Robert Legenstein, Wolfgang Maass, Franz Scherr, Arne Roennau, Jacques Kaiser, Rüdiger Dillmann, Ashish Jain and Christian Mayr. Their work appears in journals such as Nature Communications, Scientific Reports and eLife.
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.