Junxiu Liu
Papers
11
Total Citations
271
H-Index
8
About
Junxiu Liu is a leading researcher at the intersection of neuromorphic computing, fault-tolerant systems, and autonomous robotics. His work focuses on developing bio-inspired spiking neural networks (SNNs) that emulate the self-repair and learning capabilities of biological brains. A key contribution is the introduction of homeostatic fault tolerance in SNNs, where he proposed plastic neural network models that enable dynamic hardware to detect and correct faults autonomously, a concept with over 58 citations. Liu further advanced this by integrating astrocyte cells into spiking neural networks, creating self-repairing astrocyte-neuron networks (SANNs) that mimic biological tripartite synapses, achieving 50 citations for his foundational 2018 paper. His impact extends to practical applications, including mobile robot navigation and traffic signal control, where he combined SNNs with reinforcement learning and teacher-student frameworks (35+ citations). Notable achievements include demonstrating self-repairing mobile robotic cars and developing autonomous learning algorithms for obstacle avoidance. With over 270 total citations, Liu’s work bridges theoretical neuroscience and real-world engineering, offering scalable solutions for neuromorphic systems and multi-robot coordination.
Research Focus
Key Achievements
Top Papers
- 1
- 2Exploring Self-Repair in a Coupled Spiking Astrocyte Neural Network50 citations · 2018
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- 5An autonomous learning mobile robot using biological reward modulate STDP30 citations · 2021
- 6Self-repairing mobile robotic car using astrocyte-neuron networks21 citations · 2016
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- 9Fault-Tolerant Learning in Spiking Astrocyte-Neural Networks on FPGAs8 citations · 2018
- 10