Yinqian Sun
Papers
2
Total Citations
16
H-Index
2
About
Yinqian Sun is a researcher at the forefront of brain-inspired artificial intelligence, with a primary focus on spiking neural networks (SNNs) and their application to reinforcement learning and ethical AI systems. Sun’s most significant contribution addresses a critical technical challenge in neuromorphic computing: the "spike feature information vanishing problem" in spiking deep Q networks. By introducing potential-based normalization, Sun’s 2022 work (cited 14 times) provides a principled solution that preserves temporal information in SNN-based deep reinforcement learning, enabling more stable and effective learning in perceptual tasks like image classification and target detection. This work bridges the gap between biological plausibility and practical deep learning performance. More recently, Sun has ventured into the emerging field of value-aligned AI, proposing novel brain-inspired emotional empathy mechanisms to build altruistic and moral AI agents (2025). This forward-looking research addresses the critical challenge of embedding ethical considerations directly into AI architectures, moving beyond external constraints to internalize human moral values. Sun’s work represents a compelling synthesis of computational neuroscience and AI safety, positioning them as a rising voice in creating socially responsible, brain-inspired intelligent systems.
Research Focus
Key Achievements
Top Papers
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