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

Helmholtz-Decomposition and Optical Flow: A new method to characterize GCamP recordings