Papers
20
Total Citations
655
H-Index
13
About
Zhigang Zeng is a prolific researcher whose work spans neuromorphic computing, multimodal affective computing, multi-robot systems, and generative deep learning. He is perhaps best known for his pioneering contributions to memristive circuit design, where he has developed biologically inspired hardware systems capable of mimicking complex brain functions including emotional learning, associative memory, and decision-making. His series of memristive circuit papers — collectively accumulating hundreds of citations — draws on neural mechanisms such as Pavlov conditioning and operant conditioning to bridge neuroscience and electronic engineering in truly innovative ways. Beyond hardware, Zeng has made significant contributions to conversational emotion recognition, with his GA2MIF framework (91 citations) advancing multimodal, graph-based fusion techniques for human-computer interaction. His 2018 work on video generation using concatenated GANs (124 citations) demonstrated early mastery of generative modeling for realistic temporal synthesis. He has also contributed meaningfully to multi-robot coordination, addressing formation-containment control and UAV target enclosing under real-world constraints. With a research portfolio spanning AI, neuromorphic engineering, and robotics, Zeng exemplifies interdisciplinary depth, making his work essential reading for students exploring the intersection of biological intelligence and intelligent systems design.
Research Focus
Key Achievements
Top Papers
- 1Generating Realistic Videos From Keyframes With Concatenated GANs124 citations · 2018
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- 4Memristive Circuit Design of Brain-Like Emotional Learning and Generation61 citations · 2021
- 5The Design of Memristive Circuit for Affective Multi-Associative Learning53 citations · 2020
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