Preethi Sundaradevan
Papers
1
Total Citations
2
H-Index
1
About
Preethi Sundaradevan is a researcher whose work lies at the intersection of computational neuroscience and robotics, with a particular focus on biologically plausible neural networks for real-world applications. Her most cited paper, "Theta Neuron Networks: Robustness to Noise in Embedded Applications" (2007), explores the use of Theta Neuron Networks (TNNs)—a spiking neural network model that is more biologically realistic than the commonly used leaky integrate-and-fire approach. In this study, Sundaradevan demonstrates how a single-layer TNN can successfully implement a Braitenberg obstacle avoidance algorithm on a Khepera robot, highlighting the network’s robustness to noise in embedded systems. This work is notable for bridging the gap between theoretical neuroscience and practical robotics, showing that more biologically faithful models can be effectively deployed in hardware. While her citation count is modest, her contributions are significant for researchers interested in neuromorphic computing, noise-tolerant neural architectures, and the application of spiking networks in autonomous agents. Sundaradevan’s research offers a compelling foundation for those exploring how biological principles can enhance machine learning in resource-constrained environments.
Research Focus
Key Achievements
Top Papers
- 1Theta Neuron Networks: Robustness to Noise in Embedded Applications2 citations · 2007