Papers
9
Total Citations
299
H-Index
6
About
Fangwen Yu is a leading researcher at the frontier of neuromorphic computing and brain-inspired robotics, whose work bridges the gap between biological neural mechanisms and artificial intelligence. His primary research focuses on developing spiking neural networks (SNNs) with temporal dynamics, neuromorphic hardware for multi-tasking robots, and bio-inspired navigation systems. Yu’s most impactful contribution is the introduction of temporal dendritic heterogeneity in SNNs, enabling learning across multiple timescales—a paper that has garnered 97 citations since 2024. He also pioneered the Brain-inspired Multimodal Hybrid Neural Network for robot place recognition (82 citations), which addresses the challenge of robust spatial intelligence in dynamic environments. His work on the Neuromorphic Computing Chip with Spatiotemporal Elasticity (64 citations) represents a breakthrough in enabling efficient, low-latency multi-tasking for robots. Yu’s notable achievements include the development of ORB-NeuroSLAM, a brain-inspired 3D SLAM system, and adaptive spatiotemporal neural networks through complementary hybridization. With over 300 total citations across his publications, Yu is shaping the future of autonomous systems by creating energy-efficient, biologically plausible computing paradigms that allow robots to perceive, navigate, and interact with the world as naturally as living organisms.
Research Focus
Key Achievements
Top Papers
- 1
- 2Brain-inspired multimodal hybrid neural network for robot place recognition82 citations · 2023
- 3
- 4ORB-NeuroSLAM: A Brain-Inspired 3-D SLAM System Based on ORB Features20 citations · 2023
- 5Adaptive spatiotemporal neural networks through complementary hybridization13 citations · 2024
- 6
- 7
- 8Event-Driven Tactile Sensing With Dense Spiking Graph Neural Networks5 citations · 2025
- 9VF-Nav: visual floor-plan-based point-goal navigation1 citations · 2025