Shengjie Zheng

Papers

Total Citations

H-Index

About

Shengjie Zheng’s research lies at the compelling intersection of artificial intelligence and computational neuroscience, with a primary focus on bridging the gap between biological intelligence and machine learning. His most cited work, “An Introductory Review of Spiking Neural Network and Artificial Neural Network: From Biological Intelligence to Artificial Intelligence” (2022), has garnered over 13 citations, establishing him as a thoughtful synthesizer in this rapidly evolving field. In this comprehensive review, Zheng systematically compares spiking neural networks (SNNs)—which mimic the brain’s spike-based communication—with traditional artificial neural networks (ANNs), highlighting how SNNs offer greater biological interpretability and energy efficiency. His major contribution is providing a clear, accessible roadmap for researchers seeking to understand how neuroscience can inspire next-generation AI, particularly in pattern recognition, robotics, and bioinformatics. By demystifying the transition from biological to artificial intelligence, Zheng’s work serves as a vital resource for students and scientists alike, helping to accelerate the development of neuromorphic computing systems. His scholarship reflects a deep commitment to translating complex neurobiological principles into practical AI frameworks, positioning him as an emerging voice in the quest for more brain-like, efficient, and interpretable intelligent systems.