Jinying Liu
Papers
2
Total Citations
40
H-Index
2
About
Jinying Liu is a leading researcher in neuromorphic computing and bioinspired circuit design, specializing in memristive neural networks that emulate brain-like associative learning. Her work bridges the gap between neuroscience and hardware implementation, with a focus on developing intelligent systems capable of complex cognitive functions. Liu’s major contributions include the first memristive circuit implementation of associative learning with overshadowing and blocking—a landmark study with 23 citations that demonstrates how artificial synapses can replicate nuanced behavioral conditioning. She further advanced the field with her 2023 paper on cross-modal associative memory (17 citations), which drew inspiration from Drosophila’s neural mechanisms to create circuits that integrate sensory inputs from different modalities—a critical step toward truly brain-like AI. Her research has significant implications for edge computing, robotics, and adaptive systems, offering energy-efficient alternatives to traditional von Neumann architectures. By translating biological principles into functional hardware, Liu is shaping the future of intelligent, autonomous systems.
Research Focus
Key Achievements
Top Papers
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