Chenxi Wu
Papers
2
Total Citations
13
H-Index
2
About
Chenxi Wu is a rising researcher in neuromorphic engineering, focusing on the intersection of biological neural computation and robotic control. Her work centers on developing spiking neural network (SNN) hardware implementations for real-time, energy-efficient robotic systems. Wu’s major contributions include pioneering the hardware realization of a Winner-Take-All (WTA) circuit for Central Pattern Generator (CPG)-based control of a spiking robotic arm, a key step toward emulating the multi-degree-of-freedom control seen in animal limbs. Her most cited paper, “Towards hardware Implementation of WTA for CPG-based control of a Spiking Robotic Arm” (2022, 11 citations), demonstrates how neuromorphic principles can be applied to solve complex engineering challenges. More recently, her 2025 work on a trajectory interpolation mechanism for smooth, closed-loop control of an event-based robotic arm advances the field toward practical, low-power robotic systems. Wu’s research is notable for bridging theoretical neuroscience with tangible hardware, offering a path toward autonomous robots that mimic biological efficiency. Her work is increasingly cited by engineers and neuroscientists alike, positioning her as a key contributor to the future of neuromorphic robotics.
Research Focus
Key Achievements
Top Papers
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