Reviews: Fast amortized inference of neural activity from calcium imaging data with variational autoencoders

This paper presents a variational auto-encoder-style method for extracting spike-trains from two-photon fluorescence microscopy time traces. This is a task that scientists are interested in for the purposes of interpreting one- two- or three- photon microscopy data, which are becoming a staple method in neuroscience. The authors introduce 3 different spike-to-fluorescence models and use one of two neural networks to create an decoder to recover the (discritized) spike trains from the fluorescence. Overall, the method does show some promise. That said, my biggest concern with this work lies with the characterization of the method.