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Calcium imaging for observing spiking activity from large populations ofneurons are quickly gaining popularity. While the raw data are fluorescencemovies, the underlying spike trains are of interest. This work presents a fastnon-negative deconvolution filter to infer the approximately most likely spiketrain for each neuron, given the fluorescence observations. This algorithmoutperforms optimal linear deconvolution (Wiener filtering) on both simulatedand biological data. The performance gains come from restricting the inferredspike trains to be positive (using an interior-point method), unlike the Wienerfilter. The algorithm is fast enough that even when imaging over 100 neurons,inference can be performed on the set of all observed traces faster thanreal-time. Performing optimal spatial filtering on the images further refinesthe estimates. Importantly, all the parameters required to perform theinference can be estimated using only the fluorescence data, obviating the needto perform joint electrophysiological and imaging calibration experiments.

Fast non-negative deconvolution for spike train inference from population calcium imaging