Automated identification of neuronal activity from calcium imaging by sparse dictionary learning

We present ADINA, an automated pipeline for analyzing and identifying neuronal activity from calcium imaging data to investigate neuronal activity patterns. This entails the detection and classification of cell centroids and of calcium transients (events) that reappeared during different activity periods as memory consolidation. Specifically, the pipeline implements a sparse dictionary learning to infer the most relevant Ca²⁺ patterns, an image segmentation procedure using a wavelet- transform and watershed to identify single cells, and an estimation of the transient signals by means of sparse coding exploiting spatial and temporal sparsity. We validate our automated approach on artificial and two different calcium imaging sequences from mice hippocampal slice cultures acquired with fluorescence and confocal microscopes. Our approach achieves ca. 94% sensitivity on average for correctly detecting events, thus improving significantly the estimation of cell signals relative to published procedures.