Machine Learning
While genetically encoded calcium and voltage indicators and fluorescent microscopy have enabled large-scale in vivo recording of neural activities, a systematic and automatic video processing pipeline is required to extract neural activities from vastly growing videos. We are interested in developing some machine learning algorithms to segment individual active neurons from the videos, and extract independent calcium activities for these neurons.
We have developed Shallow U-Net Neuron Segmentation (SUNS) to quickly and accurately segment active neurons from two-photon fluorescence imaging videos. We used temporal filtering and whitening schemes to extract temporal features associated with active neurons, and used a compact shallow U-Net to extract spatial features of neurons. Our method was both more accurate and an order of magnitude faster than state-of-the-art techniques. We also developed an online version, potentially enabling real-time feedback neuroscience experiments. We want to further extend this method to one-photon videos.
Example segmented neuron contours of SUNS batch, overlaid on the projection of the video. Most neurons were correctly identified (true positives, green).
Key papers:
Yijun Bao, Somayyeh Soltanian-Zadeh, Sina Farsiu, and Yiyang Gong. Segmentation of neurons from fluorescence calcium recordings beyond real time. Nature Machine Intelligence volume 3, pages 590–600 (2021)
Somayyeh Soltanian-Zadeh, Kaan Sahingur, Sarah Blau, Yiyang Gong, and Sina Farsiu. Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning. PNAS 116, 8554-8563 (2019).
Yijun Bao, Emily Redington, Agnim Agarwal, and Yiyang Gong. Decontaminate Traces From Fluorescence Calcium Imaging Videos Using Targeted Non-negative Matrix Factorization. Frontiers in Neuroscience, 15:797421 (2022).
Casey Baker and Yiyang Gong. Identifying properties of pattern completion neurons in a computational model of the visual cortex. PLOS Computational Biology 19(6): e1011167 (2023).