Papers
7
Total Citations
138
H-Index
7
About
Tielin Zhang is a leading researcher at the frontier of brain-inspired artificial intelligence, with a primary focus on developing biologically plausible neural network models that bridge the gap between cognitive neuroscience and machine learning. His work centers on spiking neural networks (SNNs), reinforcement learning, and cognitive architectures for theory of mind and decision-making. Zhang’s most cited paper, “A Brain-Inspired Model of Theory of Mind” (2020, 39 citations), introduces a computational framework for attributing mental states—a cornerstone for social AI and human-robot interaction. His 2022 paper on “Multi-Scale Dynamic Coding Improved Spiking Actor Network for Reinforcement Learning” (32 citations) advances SNN-based reinforcement learning by incorporating population coding and dynamic neurons, achieving superior performance on complex robotic control tasks. Earlier foundational work includes the “Hippocampus inspired Memory Spiking Neural Network” (HMSNN, 2016, 20 citations), which models hippocampal memory processes, and the “Parallel Brain Simulator” (2016, 19 citations), a multi-scale platform for brain-inspired neural network simulation. Zhang’s contributions are notable for their emphasis on biological fidelity—such as multi-scale dynamics and cognitive architectures like SOAR—while delivering practical advances in reinforcement learning and interactive AI, making his work highly influential in the growing field of neuromorphic computing.
Research Focus
Key Achievements
Top Papers
- 1A Brain-Inspired Model of Theory of Mind39 citations · 2020
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- 3HMSNN: Hippocampus inspired Memory Spiking Neural Network20 citations · 2016
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