Jiamin Wu

The paper shows that stimulus-driven changes in neuronal connectivity and thermodynamic states lead to more efficient coding in mouse V1 by reorganizing neural activity onto low-dimensional manifolds.

The paper introduces DeepWonder3D, a pipeline for rapid and robust 3D neuronal extraction from multi-view projections, achieving high fidelity and efficiency in calcium dynamics analysis.

This paper introduces a novel optical segmentation method for compressed readout of neuronal voltage dynamics, achieving high-speed recording of subcellular activity with improved data efficiency.

The paper identifies a TRPM7-Annexin A1 pathway that enables circulating tumor cells to adapt to fluid shear stress in capillaries, facilitating metastasis.

This paper introduces a method to remove defocus backgrounds in fluorescence microscopy using dark channels and dual frequency separation, significantly improving image quality.

UniSPAC improves neuronal segmentation in connectomics by combining 2D interactive proofreading and 3D tracing, reducing manual effort and enabling semi-automatic labeling.

This paper introduces a miniaturized mesoscope (SOMM) for high-resolution calcium imaging in freely moving mice, achieving widefield, cellular resolution with robustness against motion artifacts.

This study investigates the role of acetylcholine in shaping stable olfactory representations across the Drosophila brain using advanced imaging techniques and network analysis.

This paper develops a TIS window technique for long-term high-resolution cortical imaging in awake mice without skull removal.

This paper introduces DeepCAD, a self-supervised learning method to enhance calcium imaging by reducing noise and improving SNR, leading to better neuron extraction and spike inference.

This paper introduces an unsupervised image transformation method for optical microscopy that avoids data annotation and achieves high-fidelity image transformations across modalities.