Yijin Shen
Papers
2
Total Citations
35
H-Index
2
About
Yijin Shen is a rising innovator at the intersection of neuromorphic computing and intelligent robotics, with a core focus on designing memristor-based neural network circuits that emulate complex biological learning phenomena. Shen’s major contributions lie in hardware implementation of advanced associative memory behaviors—specifically, retrospective revaluation and latent inhibition—which go beyond simple conditioning to model how the brain updates cues in the absence of direct stimuli. Their 2025 work on retrospective revaluation circuits, already garnering 28 citations, demonstrates a novel mechanism for cue revaluation in hardware, with direct application to intelligent household robots that adapt to changing environments. Complementing this, Shen’s 2024 circuit incorporating latent inhibition and transient forgetting effects (7 citations) addresses industrial intelligent grasping, enabling robots to prioritize relevant stimuli and suppress irrelevant ones—a critical step toward more efficient, brain-like decision-making in automation. By bridging cognitive psychology with circuit design, Shen is pioneering a path toward robots that learn, forget, and re-evaluate like living organisms, marking a significant leap in adaptive hardware intelligence.
Research Focus
Key Achievements
Top Papers
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- 2