Xuchen Guan
Papers
1
Total Citations
7
H-Index
1
About
Xuchen Guan is a researcher at the forefront of neuromorphic computing and autonomous robotics, with a focus on developing biologically inspired, low-latency control systems. His major contributions center on integrating convolutional spiking neural networks (SNNs) with reward-modulated learning to enable unsupervised conditional reflex behaviors in mobile robots. In his most-cited work, "Unsupervised Conditional Reflex Learning Based on Convolutional Spiking Neural Network and Reward Modulation" (2020, 7 citations), Guan introduced an efficient, easily trainable method for automatic decision-making and lane-keeping tasks. This approach addresses the critical challenge of computational limitations on mobile platforms by offering low computational consumption and minimal latency, making it highly suitable for real-time robotic applications. By bridging the gap between biological learning principles and practical engineering constraints, Guan’s work has laid a foundation for more adaptive, energy-efficient autonomous systems. His research is particularly notable for its potential to advance edge computing in robotics, where power and processing constraints are paramount.
Research Focus
Key Achievements
Top Papers
- 1