Qingqing Liu obtained her Ph.D. from the Institute of Biophysics, Chinese Academy of Sciences in 2016, and subsequently joined the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, where she held positions as a postdoctoral fellow, assistant researcher, and associate researcher. In recent years, Qingqing Liu has mainly focused on instinct-based motivational interaction and behavioral choices and their neural regulatory mechanisms. Taking the two most fundamental innate behaviors, feeding and risk avoidance, as entry points, she have utilized deep learning methods to precisely analyze behavioral patterns and employed techniques such as neural calcium signal analysis and optogenetic regulation to deeply explore the neural mechanisms by which animals make rapid and reasonable choices in different situations.

Motivational interaction and neural regulation of adaptive behaviors.

1. Granular motivational interaction and behavioral choice during feeding, Neuron, 2026
2. An iterative neural processing sequence orchestrates feeding, Neuron, 2023
3. An Infrared Touch System for Automatic Behavior Monitorin, Neurosci Bull, 2021
4. A simple threat-detection strategy in mice, BMC Biol, 2020
5. DLATA: Deep Learning-Assisted transformation alignment of 2D brain slice histology, Neurosci Lett, 2023
6. Gap junction networks in mushroom bodies participate in visual learning and memory in Drosophila, eLife, 2016

Host:
the National Natural Science Foundation of China (32471070, 31700907)
the Natural Science Foundation of Guangdong Province (2023A1515030201)

Participate:
the National Natural Science Foundation of China (82495184)
National Key R&D Program of China (2022YFE0140400)