Yifan Hua

Papers

Total Citations

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About

Yifan Hua is a researcher specializing in biologically inspired computing and autonomous robotic systems, with a particular focus on Spiking Neural Networks (SNNs) and their real-world applications. Their work sits at the intersection of computational neuroscience and robotics, exploring how principles drawn from biological neural systems can be harnessed to advance machine learning and autonomous behavior. Hua's most influential contribution, "An Autonomous Learning Mobile Robot Using Biological Reward Modulated STDP" (2021), has garnered 30 citations, demonstrating meaningful traction within the neuromorphic computing and robotics communities. This work leverages Spike-Timing-Dependent Plasticity (STDP) — a biologically grounded learning rule — to enable robots to learn adaptively from environmental feedback without explicit programming. Building on this foundation, their 2022 paper on bio-inspired autonomous learning algorithms applies SNNs to the practical challenge of mobile robot obstacle avoidance, further validating the third-generation neural network paradigm's potential beyond theoretical modeling. Hua's research is particularly notable for bridging the gap between neuroscientific theory and engineering application, contributing to the growing evidence that SNNs can serve as viable, efficient alternatives to conventional artificial neural networks in dynamic, real-world robotic scenarios.