  A fundamental challenge in calcium imaging has been to infer the timing of
action potentials from the measured noisy calcium fluorescence traces. We
systematically evaluate a range of spike inference algorithms on a large
benchmark dataset recorded from varying neural tissue (V1 and retina) using
different calcium indicators (OGB-1 and GCamp6). We show that a new algorithm
based on supervised learning in flexible probabilistic models outperforms all
previously published techniques, setting a new standard for spike inference
from calcium signals. Importantly, it performs better than other algorithms
even on datasets not seen during training. Future data acquired in new
experimental conditions can easily be used to further improve its spike
prediction accuracy and generalization performance. Finally, we show that
comparing algorithms on artificial data is not informative about performance on
real population imaging data, suggesting that a benchmark dataset may greatly
facilitate future algorithmic developments.
