“…At the same time, calcium imaging presents some important analysis challenges: calcium signals represent a slow, nonlinear encoding of the underlying spike train signals of interest, and therefore it is necessary to denoise and temporally deconvolve temporal traces extracted from calcium video data into estimates of neural activity. These issues have received extensive attention in the literature (Vogelstein et al, 2009;Vogelstein et al, 2010;Pnevmatikakis et al, 2016;Deneux et al, 2016;Theis et al, 2016;Friedrich et al, 2017;Speiser et al, 2017;Aitchison et al, 2017;Berens et al, 2018;Pachitariu et al, 2018;Greenberg et al, 2018). Some of these deconvolution approaches estimate spiking probabilities directly (Vogelstein et al, 2009;Pnevmatikakis et al, 2016;Deneux et al, 2016;Speiser et al, 2017;Aitchison et al, 2017;Greenberg et al, 2018), but many approaches instead estimate the influx of calcium in each time bin, rather than a spiking probability (Vogelstein et al, 2010;Pnevmatikakis et al, 2016;Friedrich et al, 2017;Berens et al, 2018;Pachitariu et al, 2018;Stringer and Pachitariu, 2019); these non-probabilistic approaches tend to be faster and are therefore popular in practice.…”