Papers
6
Total Citations
88
H-Index
4
About
Jingyue Zhao is a researcher at the forefront of neuromorphic engineering and biologically inspired robotics, with a focus on developing low-power, adaptive control systems that bridge computational neuroscience and practical robotics applications. Their work centers on implementing spiking neural networks on neuromorphic hardware to enable real-time, energy-efficient motor control and sensorimotor integration in robotic systems. Among their most significant contributions is the demonstration of closed-loop spiking control on the iCub humanoid robot using neuromorphic processors, a landmark step toward fully autonomous artificial agents that has garnered 36 citations. Zhao has also pioneered the neuromorphic implementation of spiking relational neural networks for motor control and developed innovative approaches to learning inverse kinematics through neural computational primitives — tackling the inherent variability challenges of analog neuromorphic substrates. Their exploration of central pattern generator-based robotic arm control and winner-take-all circuits further underscores a deep engagement with biologically plausible computing architectures. More recently, Zhao has expanded into auditory processing, applying liquid state machines to binaural sound source localization. Collectively, their body of work represents a compelling vision for the next generation of intelligent, brain-inspired autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6