  During deep sleep and under anaesthesia spontaneous patterns of cortical
activation frequently take the form of slow travelling waves. Slow wave sleep
is an important cognitive state especially because of its relevance for memory
consolidation. However, despite extensive research the exact mechanisms are
still ill-understood. Novel methods such as high speed widefield imaging of
GCamP activity offer new potentials. Here we show how data recorded from
transgenic mice under anesthesia can be processed to analyze sources, sinks and
patterns of flow. To make the best possible use of the data novel means of data
processing are necessary. Therefore, we (1) give a an brief account on
processes that play a role in generating slow waves and demonstrate (2) a novel
approach to characterize its patterns in GCamP recordings. While slow waves are
highly variable, it shows that some are surprisingly similar. To enable
quantitative means of analysis and examine the structure of such prototypical
events we propose a novel approach for the characterization of slow waves: The
Helmholtz-Decomposition of gradient-based Dense Optical Flow of the pixeldense
GCamP contrast (df/f). It allows to detect the sources and sinks of activation
and discern them from global patterns of neural flow. Aggregated features can
be analyzed with variational autoencoders. The results unravel regularities
between slow waves and shows how they relate to the experimental conditions.
The approach reveals a complex topology of different features in latent slow
wave space and identifies prototypical examples for each stage.
