Si Wu
- Cognitive Neuroscience top 2%
- Cellular and Molecular Neuroscience top 5%
- Electrical and Electronic Engineering
- Artificial Intelligence top 5%
- Biomedical Engineering
- Co-authors
- Шун-ичи АмариHiroyuki NakaharaMalte J. RaschKang LeeMisha TsodyksK. Y. Michael WongLuo HongYutaka Sakai
- Topics
- Neural dynamics and brain function (52 papers)Visual perception and processing mechanisms (18 papers)Advanced Memory and Neural Computing (17 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Si Wu
72 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 118
- Cognitive Neuroscience 931
- Cellular and Molecular Neuroscience 358
- Electrical and Electronic Engineering 229
- Artificial Intelligence 219
- Biomedical Engineering 193
Countries citing papers authored by Si Wu
This map shows the geographic impact of Si Wu'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 Si Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Si Wu more than expected).
Fields of papers citing papers by Si Wu
This network shows the impact of papers produced by Si Wu. 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 Si Wu. The network helps show where Si Wu may publish in the future.
Co-authorship network of co-authors of Si Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Si Wu. A scholar is included among the top collaborators of Si Wu 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 Si Wu. Si Wu 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 | 1 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits | 7 |
| 8 | 28 | |
| 9 | 7 | |
| 10 | 13 | |
| 11 | “Congruent” and “Opposite” Neurons: Sisters for Multisensory Integration and Segregation | 5 |
| 12 | 32 | |
| 13 | 3 | |
| 14 | 5 | |
| 15 | 100 | |
| 16 | Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively | 4 |
| 17 | 17 | |
| 18 | Delay Compensation with Dynamical Synapses | 5 |
| 19 | 8 | |
| 20 | 4 |
About Si Wu
Si Wu is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Biophysics, having authored 76 papers that have together received 1.4k indexed citations. Recurring topics across this work include Neural dynamics and brain function (52 papers), Visual perception and processing mechanisms (18 papers) and Advanced Memory and Neural Computing (17 papers). The work is most often cited by research in Cognitive Neuroscience (931 citations), Cellular and Molecular Neuroscience (358 citations) and Statistical and Nonlinear Physics (178 citations). Si Wu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Шун-ичи Амари, Hiroyuki Nakahara, Malte J. Rasch, Kang Lee, Misha Tsodyks, K. Y. Michael Wong, Luo Hong, Yutaka Sakai, Chi Chung Alan Fung and Wei Jing. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and Neuron.
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.