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深度自监督去噪助力钙成像的数据分析
作者:小柯机器人 发布时间:2021/8/18 16:58:04

深度自监督去噪可用于钙成像的数据分析,这一成果由清华大学戴琼海等研究人员合作完成。2021年8月16日,《自然—方法学》杂志在线发表了这项成果。

研究人员开发了DeepCAD,一种自我监督的深度学习方法,可用于钙成像数据的时空增强,不需要任何高信噪比(SNR)观察。DeepCAD抑制了检测噪声,并将信噪比提高了10倍以上,这加强了神经元提取和脉冲推断的准确性,从而促进了神经回路的功能分析。

据悉,钙成像提供了一种以单细胞分辨率监测神经回路活动的方法,从而改变了神经科学研究。然而,钙成像本身就容易受到检测噪声的影响,尤其是在高帧率或低激发剂量下成像时。

附:英文原文

Title: Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising

Author: Li, Xinyang, Zhang, Guoxun, Wu, Jiamin, Zhang, Yuanlong, Zhao, Zhifeng, Lin, Xing, Qiao, Hui, Xie, Hao, Wang, Haoqian, Fang, Lu, Dai, Qionghai

Issue&Volume: 2021-08-16

Abstract: Calcium imaging has transformed neuroscience research by providing a methodology for monitoring the activity of neural circuits with single-cell resolution. However, calcium imaging is inherently susceptible to detection noise, especially when imaging with high frame rate or under low excitation dosage. Here we developed DeepCAD, a self-supervised deep-learning method for spatiotemporal enhancement of calcium imaging data that does not require any high signal-to-noise ratio (SNR) observations. DeepCAD suppresses detection noise and improves the SNR more than tenfold, which reinforces the accuracy of neuron extraction and spike inference and facilitates the functional analysis of neural circuits. 

DOI: 10.1038/s41592-021-01225-0

Source: https://www.nature.com/articles/s41592-021-01225-0

期刊信息

Nature Methods:《自然—方法学》,创刊于2004年。隶属于施普林格·自然出版集团,最新IF:28.467
官方网址:https://www.nature.com/nmeth/
投稿链接:https://mts-nmeth.nature.com/cgi-bin/main.plex