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About

Guangzhi Tang is a pioneering researcher at the intersection of neuromorphic computing, spiking neural networks (SNNs), and autonomous robotics. His work focuses on developing biologically inspired computational frameworks that enable mobile robots to navigate and map unknown environments with dramatically reduced energy consumption — a critical challenge as robots increasingly operate under real-world resource constraints. Tang's most influential contribution is his application of SNNs to simultaneous localization and mapping (SLAM), demonstrating that brain-inspired, event-driven architectures can replicate the spatial reasoning capabilities of mammalian neural circuits while running efficiently on neuromorphic hardware (96 citations). Building on this foundation, he pioneered reinforcement co-learning strategies that combine deep and spiking neural networks for mapless navigation (80 citations), effectively bridging conventional deep reinforcement learning with neuromorphic efficiency. His "Gridbot" project (44 citations) further explored how grid-cell-inspired neural models can translate biological intelligence into robotic behavior. Across his body of work, Tang consistently demonstrates that non-Von Neumann computing paradigms offer compelling alternatives for real-time robotic control, including continuous motor control and oculomotor simulation. With over 280 cumulative citations, his research has meaningfully advanced the field of neuromorphic robotics and established him as a key voice in energy-aware autonomous systems research.