Jun-ichiro Hirayama
- Cognitive Neuroscience top 10%
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Signal Processing top 10%
- Information Systems
- Co-authors
- Michael U. GutmannShin IshiiMotoaki KawanabeTakeshi OGAWAAapo HyvärinenAtsunori KanemuraHiroshi MoriokaShigeyuki Ikeda
- Topics
- Neural dynamics and brain function (12 papers)Functional Brain Connectivity Studies (10 papers)Blind Source Separation Techniques (8 papers)
- Journals
- PLoS ONENeuroImageNeural Computation
- Partner nations
- JapanFinlandUnited Kingdom
In The Last Decade
Jun-ichiro Hirayama
24 papers receiving 309 citations
Peers
Comparison fields: 5 of 81
- Cognitive Neuroscience 137
- Artificial Intelligence 105
- Computer Vision and Pattern Recognition 48
- Signal Processing 47
- Information Systems 34
Countries citing papers authored by Jun-ichiro Hirayama
This map shows the geographic impact of Jun-ichiro Hirayama'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 Jun-ichiro Hirayama with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun-ichiro Hirayama more than expected).
Fields of papers citing papers by Jun-ichiro Hirayama
This network shows the impact of papers produced by Jun-ichiro Hirayama. 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 Jun-ichiro Hirayama. The network helps show where Jun-ichiro Hirayama may publish in the future.
Co-authorship network of co-authors of Jun-ichiro Hirayama
This figure shows the co-authorship network connecting the top 25 collaborators of Jun-ichiro Hirayama. A scholar is included among the top collaborators of Jun-ichiro Hirayama 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 Jun-ichiro Hirayama. Jun-ichiro Hirayama 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 | 19 | |
| 3 | 7 | |
| 4 | 8 | |
| 5 | 3 | |
| 6 | 2 | |
| 7 | SPLICE: Fully tractable hierarchical extension of ICA with pooling | 4 |
| 8 | 4 | |
| 9 | 5 | |
| 10 | 4 | |
| 11 | 78 | |
| 12 | 10 | |
| 13 | 8 | |
| 14 | 1 | |
| 15 | 3 | |
| 16 | Structural equations and divisive normalization for energy-dependent component analysis | 4 |
| 17 | Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI 2011) | 103 |
| 18 | 14 | |
| 19 | 4 | |
| 20 | 13 |
About Jun-ichiro Hirayama
Jun-ichiro Hirayama is a scholar working on Signal Processing, Cognitive Neuroscience and Statistics and Probability, having authored 24 papers that have together received 318 indexed citations. Recurring topics across this work include Neural dynamics and brain function (12 papers), Functional Brain Connectivity Studies (10 papers) and Blind Source Separation Techniques (8 papers). The work is most often cited by research in Cognitive Neuroscience (137 citations), Human-Computer Interaction (26 citations) and Signal Processing (47 citations). Jun-ichiro Hirayama has collaborated with scholars based in Japan, Finland and United Kingdom. Frequent co-authors include Michael U. Gutmann, Shin Ishii, Motoaki Kawanabe, Takeshi OGAWA, Aapo Hyvärinen, Atsunori Kanemura, Hiroshi Morioka, Shigeyuki Ikeda, Junichiro Yoshimoto and Shin‐ichi Maeda. Their work appears in journals such as PLoS ONE, NeuroImage and Neural Computation.
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.