Vivek Gopan
Papers
1
Total Citations
2
H-Index
1
About
Vivek Gopan’s research lies at the intersection of computational neuroscience and robotics, focusing on how biological principles—particularly those of the cerebellum—can inspire more adaptive and precise robotic control systems. His most-cited work, “Comparing robotic control using a spiking model of cerebellar network and a gain adapting forward-inverse model” (2017), directly compares a biologically plausible spiking cerebellar network with a classical forward-inverse model for controlling anthropomorphic manipulators. This study demonstrates how cerebellar-inspired internal models can fine-tune robotic movements with remarkable precision, offering a compelling alternative to traditional artificial neural network approaches. Though his citation count is modest, Gopan’s contribution is notable for bridging the gap between neural computation and practical robotics, providing a framework for developing more adaptive, human-like control in autonomous systems. His work is particularly valuable for researchers exploring neuromorphic engineering and bio-inspired robotics, as it highlights the potential of leveraging cerebellar circuitry for real-time motor learning and error correction.
Research Focus
Key Achievements
Top Papers
- 1