Author

Liuyuan He

Peking UniversityORCID 0000-0002-8032-2450Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:deep learning· 4 Research Focus:benchmarking· 3 Approach:statistical model· 3 Research Focus:dimensionality reduction· 2 Assay:electronic health records· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:deconvolution· 1

Frequent Co-Authors

Kai DuiD· 4 shared Tiejun HuangiD· 3 shared Kaiwen ShengiD· 2 shared Kehuan Lun· 2 shared Ruohua ShiiD· 2 shared Wenyao WangiD· 2 shared Лей МаiD· 2 shared Gan HeiD· 1 shared

Activity by Year

2021-2025 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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4 shared papersNeuroscience

Kai DuORCID

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5 papers

Recent·Most cited

Unifying the Electron Microscopy Multiverse through a Large-scale Foundation Model

Liuyuan He, Ruohua Shi, Wenyao Wang, Guanchen Fang, Yu Cai, Лей Ма

This paper introduces EM-DINO and OmniEM, a foundation model and architecture for EM image analysis, improving restoration and segmentation across diverse datasets.

bioRxiv · Apr 13, 2025 · PreprintBioengineering

A computational framework linking synaptic adaptation to circuit behaviors in the early visual system

Liuyuan He, Yutao He, Kehuan Lun, Lei Ma, Kai Du, Tiejun Huang

This paper introduces a computational framework to model synaptic adaptation in the early visual system, revealing diverse adaptive time constants and a frequency-sensitive gain-control strategy in ribbon synapses.

bioRxiv · Aug 27, 2022 · PreprintNeuroscience

Retina Gap Junction Networks Facilitate Blind Denoising in Visual Hierarchy

Yang Yue, Kehuan Lun, Liuyuan He, Gan He, Shenjian Zhang, Lei Ma, Jian K. Liu, Yonghong Tian, Kai Du, Tiejun Huang

This paper develops a computational model of retinal gap junctions (G-filter) that improves deep neural networks' performance in blind denoising tasks.

bioRxiv · May 16, 2022 · PreprintNeuroscience

U-RISC: An Ultra-high Resolution EM Dataset Challenging Existing Deep Learning Algorithms

Ruohua Shi, Wenyao Wang, Zhixuan Li, Liuyuan He, Kaiwen Sheng, Лей Ма, Kai Du, Tingting Jiang, Tiejun Huang

The paper introduces U-RISC, a high-resolution EM dataset for cell membrane segmentation, and evaluates deep learning methods, finding a gap between current performance and human-level accuracy.

Frontiers in Computational Neuroscience · Apr 11, 2022Peer-reviewedNeuroscience

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