Author

Peng Qu

Tsinghua UniversityORCID 0000-0002-1786-5372Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Peng Qu1 paper · Yunnan Agricultural University

Biology Disciplines

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Neuroscience· 2

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Assay:electronic health records· 1 Organism:mouse· 1 Organism:rat· 1

Frequent Co-Authors

Kai DuiD· 2 shared Kaiwen ShengiD· 2 shared Xiaofei LiuiD· 2 shared Youhui ZhangiD· 2 shared Le YangiD· 1 shared Lei MaiD· 1 shared Lei MaiD· 1 shared Liuyuan HeiD· 1 shared

Activity by Year

2021-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersNeuroscience

Kai DuORCID

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2 papers

Recent·Most cited

Domain-adaptive matching bridges synthetic and in vivo neural dynamics for neural circuit connectivity inference

Kaiwen Sheng, Shanghang Zhang, Shenjian Zhang, Yutao He, Maxime Beau, Peng Qu, Xiaofei Liu, Youhui Zhang, Lei Ma, Kai Du

This paper introduces DeepDAM, a framework that improves neural connectivity inference by aligning synthetic and in vivo data domains, achieving high accuracy in rodent and other systems.

bioRxiv · Oct 3, 2022 · PreprintNeuroscience

A General LSTM-based Deep Learning Method for Estimating Neuronal Models and Inferring Neural Circuitry

Kaiwen Sheng, Peng Qu, Le Yang, Xiaofei Liu, Liuyuan He, Youhui Zhang, Lei Ma, Kai Du

This paper introduces a general LSTM-based deep learning method, GNE, for automated parameter tuning of neuronal models and neural circuits, showing strong performance on both synthetic and experimental data.

bioRxiv · Mar 14, 2021 · PreprintNeuroscience