Man Yao

816 citations
15 papers · 421 · 2 hit papers · h-index 8

Impact in

Papers in

Man Yao

11 papers receiving 410 citations

Man Yao's Hit Papers

Advancing Spiking Neural Networks Toward Deep Residual Learning 2024 · 68 citations
680+1+2Years since publication4080120

Peers

Man Yao
Comparison fields: 5 of 57
  • Computational Mathematics 12
  • Cognitive Neuroscience 175
  • Electrical and Electronic Engineering 304
  • Artificial Intelligence 133
  • Cellular and Molecular Neuroscience 40
Replace Zhenzhi Wu with:
Zhenzhi Wu China
Youngeun Kim United States
Abhishek Moitra United States
Hanle Zheng China
Dingheng Wang China
Davis Barch United States
Jeff Kusnitz United States
Daniel Ben Dayan Rubin United States
Dhireesha Kudithipudi United States
Deepak Kadetotad United States
Man Yao relative to Zhenzhi Wu China Zhenzhi Wu's profile →
Citations per field
00.5×
Zhenzhi Wu · 1×
Citations per year

Countries citing papers authored by Man Yao

Since Specialization
Citations

This map shows the geographic impact of Man Yao'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 Man Yao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Man Yao more than expected).

Fields of papers citing papers by Man Yao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Man Yao. 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 Man Yao. The network helps show where Man Yao may publish in the future.

Co-authors

The 25 scholars most cited alongside Man Yao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Man Yao Line = papers co-authored together Man Yao links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1
Attention Spiking Neural Networks
Hit paper breakdown →
2023140
2 2021103
3
Advancing Spiking Neural Networks Toward Deep Residual Learning
Hit paper breakdown →
202468
4 202454
5 202318
6 202117
7 20257
8 20247
9 20254
10 20242
11 20251
12 20250
13 20250
14 20250
15 20240

About Man Yao

Man Yao is a scholar working on Electrical and Electronic Engineering, Cognitive Neuroscience, Cellular and Molecular Neuroscience, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 15 papers that have together received 421 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (11 papers), Ferroelectric and Negative Capacitance Devices (6 papers), Neural dynamics and brain function (6 papers), Neuroscience and Neural Engineering (3 papers), Neural Networks and Reservoir Computing (3 papers), Optical Coherence Tomography Applications (1 paper), Medical Image Segmentation Techniques (1 paper) and Advanced Fiber Optic Sensors (1 paper). The work is most often cited by research in Computational Mathematics (12 citations), Cognitive Neuroscience (175 citations), Electrical and Electronic Engineering (304 citations), Artificial Intelligence (133 citations) and Cellular and Molecular Neuroscience (40 citations). Man Yao has collaborated with scholars based in China, Hong Kong and Switzerland. Frequent co-authors include Guoqi Li, Guangshe Zhao, Lei Deng, Yifan Hu, Dingheng Wang, Yonghong Tian, Hengyu Zhang, Bo Xu, Yihan Lin and Zhao-Xu Yang. Their work appears in journals such as Neural Networks, Nature Communications, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Pattern Analysis and Machine Intelligence and National Science Review.

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.

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