Papers
9
Total Citations
118
H-Index
6
About
Yuxuan Zhao is a pioneering researcher at the intersection of cognitive neuroscience and robotics, whose work centers on endowing machines with brain-inspired models of self-consciousness and social cognition. Zhao’s major contributions lie in developing computational frameworks that translate neural mechanisms—such as theory of mind, bodily self-perception, and classical conditioning—into robotic systems. Notably, their 2020 paper “A Brain-Inspired Model of Theory of Mind” (39 citations) provides a computational architecture for robots to infer others’ mental states, a cornerstone for intuitive human-robot interaction. Zhao’s HMSNN model (20 citations) simulates hippocampal memory processes using spiking neural networks, while their series on robot self-consciousness (e.g., “Toward Robot Self-Consciousness II,” 17 citations) introduces mirror neuron and bodily self models for self-recognition and the rubber hand illusion. With over 120 total citations across nine publications, Zhao’s work bridges biological plausibility and engineering application, offering a roadmap for creating socially aware, self-aware robots. Their research is essential reading for anyone interested in cognitive robotics, neurorobotics, or the quest for artificial consciousness.
Research Focus
Key Achievements
Top Papers
- 1A Brain-Inspired Model of Theory of Mind39 citations · 2020
- 2HMSNN: Hippocampus inspired Memory Spiking Neural Network20 citations · 2016
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- 4Brain-inspired classical conditioning model15 citations · 2020
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- 9Design of intelligent algorithms for multi-mobile robot systems2 citations · 2015