P. Balamurali

Papers

Total Citations

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About

P. Balamurali is a researcher at the intersection of neuromorphic computing, robotics, and intelligent systems. Their work centers on developing biologically inspired computational models, particularly Spiking Neural Networks (SNNs), which more closely mimic mammalian neural circuits than traditional artificial networks. Balamurali’s most cited paper, “A Reservoir-based Convolutional Spiking Neural Network for Gesture Recognition from DVS Input” (2020, 35 citations), demonstrates a novel approach to processing spatio-temporal spike data from event-based vision sensors, enabling efficient gesture recognition. This work highlights their contribution to advancing energy-efficient, real-time sensory processing for neuromorphic hardware. Additionally, Balamurali explores the conceptualization of robotic solutions within Industry 4.0, as seen in their 2019 paper (9 citations), which takes a knowledge-centric approach to automating warehouse operations. By bridging neuroscience-inspired algorithms with practical robotics applications, Balamurali’s research offers pathways toward more adaptive, low-power intelligent systems. Their work is particularly relevant for students and researchers interested in neuromorphic engineering, event-driven vision, and the integration of cognitive principles into autonomous systems.