Gouhei Tanaka
- Artificial Intelligence top 0.5%
- Electrical and Electronic Engineering top 5%
- Cognitive Neuroscience top 2%
- Statistical and Nonlinear Physics top 1%
- Computer Networks and Communications top 2%
- Co-authors
- Kazuyuki AiharaRyosho NakaneAkira HiroseDaiju NakanoToshiyuki YamaneNaoki KanazawaJ. B. HérouxHidetoshi Numata
- Topics
- Advanced Memory and Neural Computing (32 papers)Neural Networks and Reservoir Computing (30 papers)Neural dynamics and brain function (16 papers)
In The Last Decade
Gouhei Tanaka
80 papers receiving 2.8k citations
Hit Papers
Peers
Comparison fields: 5 of 144
- Artificial Intelligence 1.6k
- Electrical and Electronic Engineering 1.4k
- Cognitive Neuroscience 730
- Statistical and Nonlinear Physics 608
- Computer Networks and Communications 596
Countries citing papers authored by Gouhei Tanaka
This map shows the geographic impact of Gouhei Tanaka'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 Gouhei Tanaka with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gouhei Tanaka more than expected).
Fields of papers citing papers by Gouhei Tanaka
This network shows the impact of papers produced by Gouhei Tanaka. 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 Gouhei Tanaka. The network helps show where Gouhei Tanaka may publish in the future.
Co-authorship network of co-authors of Gouhei Tanaka
This figure shows the co-authorship network connecting the top 25 collaborators of Gouhei Tanaka. A scholar is included among the top collaborators of Gouhei Tanaka 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 Gouhei Tanaka. Gouhei Tanaka 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 | 0 | |
| 3 | 4 | |
| 4 | 3 | |
| 5 | 25 | |
| 6 | 18 | |
| 7 | 19 | |
| 8 | 3 | |
| 9 | Recent advances in physical reservoir computing: A reviewbreakdown → | 1256 |
| 10 | 13 | |
| 11 | Nonlinear Time Series Prediction using Multi-Step Learning Echo State Networks | 1 |
| 12 | 16 | |
| 13 | 12 | |
| 14 | 24 | |
| 15 | 25 | |
| 16 | 3 | |
| 17 | 50 | |
| 18 | 20 | |
| 19 | 6 | |
| 20 | 13 |
About Gouhei Tanaka
Gouhei Tanaka is a scholar working on Modeling and Simulation, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 85 papers that have together received 2.9k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (32 papers), Neural Networks and Reservoir Computing (30 papers) and Neural dynamics and brain function (16 papers). The work is most often cited by research in Modeling and Simulation (250 citations), Artificial Intelligence (1.6k citations) and Statistical and Nonlinear Physics (608 citations). Gouhei Tanaka has collaborated with scholars based in Japan, China and Spain. Frequent co-authors include Kazuyuki Aihara, Ryosho Nakane, Akira Hirose, Daiju Nakano, Toshiyuki Yamane, Naoki Kanazawa, J. B. Héroux, Hidetoshi Numata, Seiji Takeda and Kai Morino. Their work appears in journals such as Advanced Materials, PLoS ONE and Journal of Applied Physics.
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.