Chris Stephen Naveen Ranjit
Papers
1
Total Citations
5
H-Index
1
About
Chris Stephen Naveen Ranjit’s research lies at the intersection of computational neuroscience and motor control, with a particular focus on cerebellar modeling and learning algorithms. His most-cited work, “A Simplified Cerebellar Model with Priority-based Delayed Eligibility Trace Learning for Motor Control” (2014, 5 citations), proposes an innovative extension of cerebellar learning mechanisms by integrating priority-based delayed eligibility traces. This contribution advances the understanding of how error-driven synaptic plasticity can govern motor coordination, offering a simplified yet biologically plausible framework for robotic and neural control systems. While his citation count is modest, Ranjit’s work is notable for its conceptual depth, bridging theoretical neuroscience with practical engineering applications. His research is particularly valuable for students and researchers exploring adaptive motor learning, synaptic plasticity, and bio-inspired control architectures. By refining eligibility trace learning—a core mechanism in cerebellar function—Ranjit provides a foundation for future work in neurorobotics and computational motor control, highlighting the cerebellum’s role in real-time movement adaptation.
Research Focus
Key Achievements
Top Papers
- 1