Lazar Supic
Papers
7
Total Citations
102
H-Index
5
About
Lazar Supic is pioneering the intersection of neuromorphic computing and robotics, developing brain-inspired control systems that make robots more adaptive, efficient, and human-like. His major contributions center on implementing the Neural Engineering Framework (NEF) for robotic control, demonstrating that neuromorphic approaches can outperform conventional methods in robustness and adaptability. Supic’s work on inverse kinematics and PID control—his most-cited paper with 40 citations—established a new paradigm for robot motion planning using spiking neural networks. He has applied these principles to assistive technology, notably developing an adaptive control system for wheelchair-mounted robotic arms (21 citations) that reduces power consumption while maintaining responsiveness for users with upper extremity disabilities. His more recent research introduces resonator networks for neuromorphic visual scene understanding and visual odometry, enabling efficient self-motion estimation and scene inference without the computational bottlenecks of traditional computer vision. Supic’s bioinspired approach to achieving smooth, natural motion trajectories in robotic arms further bridges the gap between artificial and biological movement. His work represents a significant step toward low-power, real-time robotic systems that can operate autonomously in dynamic environments, with direct applications in assistive robotics and autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1Neuromorphic NEF-Based Inverse Kinematics and PID Control40 citations · 2021
- 2
- 3Neuromorphic visual scene understanding with resonator networks17 citations · 2024
- 4Visual odometry with neuromorphic resonator networks10 citations · 2024
- 5Bioinspired smooth neuromorphic control for robotic arms9 citations · 2023
- 6Neuromorphic Visual Scene Understanding with Resonator Networks3 citations · 2022
- 7Visual Odometry with Neuromorphic Resonator Networks2 citations · 2022