Dongcheng Zhao

788 total citations
32 papers, 450 citations indexed

About

Dongcheng Zhao is a scholar working on Electrical and Electronic Engineering, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Dongcheng Zhao has authored 32 papers receiving a total of 450 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Electrical and Electronic Engineering, 22 papers in Cognitive Neuroscience and 9 papers in Artificial Intelligence. Recurrent topics in Dongcheng Zhao's work include Advanced Memory and Neural Computing (26 papers), Neural dynamics and brain function (19 papers) and Ferroelectric and Negative Capacitance Devices (12 papers). Dongcheng Zhao is often cited by papers focused on Advanced Memory and Neural Computing (26 papers), Neural dynamics and brain function (19 papers) and Ferroelectric and Negative Capacitance Devices (12 papers). Dongcheng Zhao collaborates with scholars based in China, Canada and Belgium. Dongcheng Zhao's co-authors include Yi Zeng, Guobin Shen, Tielin Zhang, Feifei Zhao, Yang Li, Yang Li, Qian Zhang, Jindong Li, Bo Xu and Yuxuan Zhao and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Pattern Recognition and Information Sciences.

In The Last Decade

Dongcheng Zhao

29 papers receiving 441 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Dongcheng Zhao China 13 345 279 152 70 21 32 450
Eric Hunsberger Canada 3 269 0.8× 235 0.8× 127 0.8× 85 1.2× 13 0.6× 5 370
Gregor Lenz France 5 274 0.8× 134 0.5× 126 0.8× 73 1.0× 18 0.9× 12 361
Milad Mozafari France 8 328 1.0× 271 1.0× 133 0.9× 94 1.3× 42 2.0× 12 430
Man Yao China 7 274 0.8× 175 0.6× 127 0.8× 37 0.5× 41 2.0× 15 366
Michael Hersche Switzerland 10 193 0.6× 210 0.8× 167 1.1× 85 1.2× 47 2.2× 17 446
Huihui Zhou China 7 199 0.6× 286 1.0× 112 0.7× 64 0.9× 24 1.1× 17 446
Amirreza Yousefzadeh Netherlands 13 356 1.0× 136 0.5× 114 0.8× 118 1.7× 30 1.4× 37 396
Massimiliano Versace United States 10 192 0.6× 255 0.9× 121 0.8× 127 1.8× 21 1.0× 24 502
Jianhao Ding China 6 325 0.9× 220 0.8× 128 0.8× 62 0.9× 25 1.2× 19 388
Daniel Ben Dayan Rubin United States 8 359 1.0× 232 0.8× 142 0.9× 141 2.0× 21 1.0× 9 434

Countries citing papers authored by Dongcheng Zhao

Since Specialization
Citations

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

Fields of papers citing papers by Dongcheng Zhao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dongcheng Zhao

This figure shows the co-authorship network connecting the top 25 collaborators of Dongcheng Zhao. A scholar is included among the top collaborators of Dongcheng Zhao 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 Dongcheng Zhao. Dongcheng Zhao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Li, Jindong, Ruiqi Chen, Guobin Shen, et al.. (2025). Hummingbird: A Smaller and Faster Large Language Model Accelerator on Embedded FPGA. 1–9.
2.
3.
Zhao, Feifei, et al.. (2024). Directly training temporal Spiking Neural Network with sparse surrogate gradient. Neural Networks. 179. 106499–106499. 3 indexed citations
4.
5.
He, Xiang, Yang Li, Dongcheng Zhao, Qingqun Kong, & Yi Zeng. (2024). MSAT: biologically inspired multistage adaptive threshold for conversion of spiking neural networks. Neural Computing and Applications. 36(15). 8531–8547. 4 indexed citations
6.
He, Xiang, Dongcheng Zhao, Yang Li, et al.. (2024). An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain. Proceedings of the AAAI Conference on Artificial Intelligence. 38(1). 512–520. 2 indexed citations
7.
Li, Jindong, Guobin Shen, Dongcheng Zhao, Qian Zhang, & Yi Zeng. (2024). FireFly v2: Advancing Hardware Support for High-Performance Spiking Neural Network With a Spatiotemporal FPGA Accelerator. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 43(9). 2647–2660. 8 indexed citations
8.
Li, Jindong, et al.. (2024). FireFly-S: Exploiting Dual-Side Sparsity for Spiking Neural Networks Acceleration With Reconfigurable Spatial Architecture. IEEE Transactions on Circuits and Systems I Regular Papers. 72(8). 4007–4020. 3 indexed citations
9.
Zhao, Dongcheng, et al.. (2024). Improving stability and performance of spiking neural networks through enhancing temporal consistency. Pattern Recognition. 159. 111094–111094. 2 indexed citations
10.
Shen, Guobin, et al.. (2024). Are Conventional SNNs Really Efficient? A Perspective from Network Quantization. 27528–27537. 3 indexed citations
11.
Zhao, Dongcheng, et al.. (2024). Temporal Knowledge Sharing Enable Spiking Neural Network Learning From Past and Future. IEEE Transactions on Artificial Intelligence. 5(7). 3524–3534. 7 indexed citations
12.
Li, Jindong, Guobin Shen, Dongcheng Zhao, Qian Zhang, & Yi Zeng. (2023). FireFly: A High-Throughput Hardware Accelerator for Spiking Neural Networks With Efficient DSP and Memory Optimization. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 31(8). 1178–1191. 31 indexed citations
13.
Shen, Guobin, et al.. (2023). Brain-inspired neural circuit evolution for spiking neural networks. Proceedings of the National Academy of Sciences. 120(39). e2218173120–e2218173120. 16 indexed citations
14.
Zeng, Yi, Dongcheng Zhao, Feifei Zhao, et al.. (2023). BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation. Patterns. 4(8). 100789–100789. 43 indexed citations
15.
Zhao, Dongcheng, et al.. (2023). An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connections. Neural Networks. 165. 799–808. 29 indexed citations
16.
Zhao, Dongcheng, Yi Zeng, & Yang Li. (2022). BackEISNN: A deep spiking neural network with adaptive self-feedback and balanced excitatory–inhibitory neurons. Neural Networks. 154. 68–77. 35 indexed citations
17.
Shen, Guobin, Dongcheng Zhao, & Yi Zeng. (2022). Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks. Patterns. 3(6). 100522–100522. 33 indexed citations
18.
Li, Yang, Dongcheng Zhao, & Yi Zeng. (2022). BSNN: Towards faster and better conversion of artificial neural networks to spiking neural networks with bistable neurons. Frontiers in Neuroscience. 16. 991851–991851. 22 indexed citations
19.
Zeng, Yi, Dongcheng Zhao, Feifei Zhao, et al.. (2022). BrainCog: A Spiking Neural Network Based Brain-Inspired Cognitive Intelligence Engine for Brain-Inspired AI and Brain Simulation. SSRN Electronic Journal. 12 indexed citations
20.

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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