  There is a broad need in the neuroscience community to understand and
visualize large-scale recordings of neural activity, big data acquired by tens
or hundreds of electrodes simultaneously recording dynamic brain activity over
minutes to hours. Such dynamic datasets are characterized by coherent patterns
across both space and time, yet existing computational methods are typically
restricted to analysis either in space or in time separately. Here we report
the adaptation of dynamic mode decomposition (DMD), an algorithm originally
developed for the study of fluid physics, to large-scale neuronal recordings.
DMD is a modal decomposition algorithm that describes high-dimensional dynamic
data using coupled spatial-temporal modes; the resulting analysis combines key
features of performing principal components analysis (PCA) in space and power
spectral analysis in time. The algorithm scales easily to very large numbers of
simultaneously acquired measurements. We validated the DMD approach on
sub-dural electrode array recordings from human subjects performing a known
motor activation task. Next, we leveraged DMD in combination with machine
learning to develop a novel method to extract sleep spindle networks from the
same subjects. We suggest that DMD is generally applicable as a powerful method
in the analysis and understanding of large-scale recordings of neural activity.
