Figure 5.

DeepCINAC step by step workflow. A, Schematic of two-photon imaging experiment. B, Screenshot of DeepCINAC GUI used to explore and annotate data. C, The GUI produces .cinac files that contain the necessary data to train or benchmark a classifier. D, Schematic representation of the architecture of the model that will be used to train the classifier and predict neuronal activity. E, Training of the classifier using the previously defined model. F, Schematic of a raster plot resulting from the inference of the neuronal activity using the trained classifier. G, Evaluation of the classifier performance using precision, sensitivity and F1 score. H, Active learning pipeline: screenshots of the GUI used to identify edge cases where the classifier wrongly infers the neuronal activity and annotate new data on similar situations to add data for a new classifier training.