主题：The Hebbian Normalization Model
报告人：Jing Yuan，Huazhong University of Science and Technology
Whole-brain optical imaging is faced with the challenge of how to extract effective information from terabyte data sets. Previously, people focused on the use of computational science to find solutions, while another neglected strategy is to improve imaging quality. LiMo enables to achieve a better background suppression effect than traditional optical sectioning imaging, and greatly improve the image quality. HD-fMOST obtained the 3D dataset of a whole mouse brain with a voxel resolution of 0. 32 × 0. 32 × 1 μm and an average signal-to-noise ratio of 110 within 110 hours, leading to significantly improvement in neuronal morphology reconstruction. We also implemented >30-fold online lossless data compression, enabling online raw data storage to a USB drive or in the cloud, and real-time high-precision (>95%) whole-brain 3D cell counting. These results demonstrate the potential of HD-fMOST to facilitate large-scale acquisition and analysis of whole-brain high-resolution datasets.