Gaspar Tognetti
Papers
1
Total Citations
9
H-Index
1
About
Gaspar Tognetti is a pioneering researcher in neuromorphic engineering, specializing in spike-based sensory processing and closed-loop robotic control. His major contributions lie at the intersection of biologically inspired vision systems and autonomous navigation, where he has demonstrated how retinomorphic sensors and event-driven computation can enable real-time, low-latency decision-making in robots. His most cited work, a 2017 study on a neuromorphic self-driving robot, integrates the Asynchronous Time-based Image Sensor (ATIS) with IBM’s TrueNorth processor to achieve fully spike-based perception and control—a landmark demonstration of end-to-end neuromorphic autonomy. This work, with 9 citations, has influenced subsequent research in edge AI and energy-efficient robotics. Tognetti’s achievements include advancing the practical deployment of spiking neural networks in real-world systems, bridging the gap between theoretical neuroscience and applied robotics. His research continues to shape the development of compact, low-power autonomous platforms, making him a key figure in the push toward brain-inspired computing for mobile systems.
Research Focus
Key Achievements
Top Papers
- 1