Shuangming Yang
Tianjin University, Beijing Academy of Artificial Intelligence
Papers
11
Total Citations
281
H-Index
7
About
Shuangming Yang is a leading researcher at the frontier of neuromorphic computing and biologically inspired neural networks. His work centers on developing efficient digital hardware implementations of spiking neural networks (SNNs), with a particular focus on creating brain-like computational models that bridge the gap between biological plausibility and practical engineering. Yang's major contributions include the development of SAM, a unified self-adaptive multicompartmental spiking neuron model that enables learning with working memory—a fundamental cognitive feature—garnering 77 citations. He has also pioneered the NADOL architecture for spike-driven online learning using dendrites (44 citations), advancing biologically plausible learning mechanisms. His highly cited work on digital implementations of thalamocortical and basal ganglia models (46 and 32 citations) has direct applications in understanding and potentially treating neurological disorders like Parkinson's disease. Yang's research extends to real-time FPGA implementations of auditory and visual SNNs, demonstrating robust, anti-noise cognitive processing. His achievements include reconstructing fully parallelized auditory systems and investigating spike-timing-dependent plasticity for context-dependent learning, establishing him as a key innovator in making neuromorphic systems practical for real-world cognitive tasks.
Research Focus
Key Achievements
Top Papers
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- 9Reconstruction of Brain-inspired Visual Spiking Neural Network on BiCoSS3 citations · 2021
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