用于钙成像数据自动分析的鲁棒PCA的高效神经网络逼近
Efficient Neural Network Approximation of Robust PCA for Automated Analysis of Calcium Imaging Data
摘要：
钙离子成像是研究神经元群体活动的重要工具。然而，图像中高水平的背景荧光阻碍了神经元的准确识别和神经元活动的提取。虽然鲁棒主成分分析（RPCA）是一种很有前途的方法，可以分解的前景和背景，在这样的图像，其计算复杂度和内存需求是过高的处理大规模的钙成像数据。在这里，我们提出了BEAR，一个简单的双线性神经网络的RPCA的有效近似，实现了一个数量级的速度提高与GPU加速相比，传统的RPCA算法。此外，我们表明，BEAR可以执行前景-背景分离的钙成像数据大到几十千兆字节。我们还证明了两个BEAR可以级联执行同时RPCA和非负矩阵分解，用于从钙成像数据中自动提取空间和时间足迹。论文中使用的源代码可在https://github.com/NICALab/BEAR上获得。
英文摘要：
Calcium imaging is an essential tool to study the activity of neuronal populations. However, the high level of background fluorescence in images hinders the accurate identification of neurons and the extraction of neuronal activities. While robust principal component analysis (RPCA) is a promising method that can decompose the foreground and background in such images, its computational complexity and memory requirement are prohibitively high to process large-scale calcium imaging data. Here, we propose BEAR, a simple bilinear neural network for the efficient approximation of RPCA which achieves an order of magnitude speed improvement with GPU acceleration compared to the conventional RPCA algorithms. In addition, we show that BEAR can perform foreground-background separation of calcium imaging data as large as tens of gigabytes. We also demonstrate that two BEARs can be cascaded to perform simultaneous RPCA and non-negative matrix factorization for the automated extraction of spatial and temporal footprints from calcium imaging data. The source code used in the paper is available at https://github.com/NICALab/BEAR.
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