A powerful approach for understanding neural population dynamics is to
extract low-dimensional trajectories from population recordings using
dimensionality reduction methods. Current approaches for dimensionality
reduction on neural data are limited to single population recordings, and can
not identify dynamics embedded across multiple measurements. We propose an
approach for extracting low-dimensional dynamics from multiple, sequential
recordings. Our algorithm scales to data comprising millions of observed
dimensions, making it possible to access dynamics distributed across large
populations or multiple brain areas. Building on subspace-identification
approaches for dynamical systems, we perform parameter estimation by minimizing
a moment-matching objective using a scalable stochastic gradient descent
algorithm: The model is optimized to predict temporal covariations across
neurons and across time. We show how this approach naturally handles missing
data and multiple partial recordings, and can identify dynamics and predict
correlations even in the presence of severe subsampling and small overlap
between recordings. We demonstrate the effectiveness of the approach both on
simulated data and a whole-brain larval zebrafish imaging dataset.

本文提出了一种用于多个序列记录的低维动力学提取的方法，该方法利用了降维方法并构建了一种优化模型，能够有效地分析分布在大型脑区域中的动力学，并在遇到严重子采样和小重叠度的情况下标识动力学并预测相关性，同时对处理丢失数据也有很好的表现。