PT  - JOURNAL ARTICLE
AU  - Lucas Theis
AU  - Philipp Berens
AU  - Emmanouil Froudarakis
AU  - Jacob Reimer
AU  - Miroslav Román Rosón
AU  - Tom Baden
AU  - Thomas Euler
AU  - Andreas Tolias
AU  - Matthias Bethge
TI  - Supervised learning sets benchmark for robust spike detection from calcium imaging signals
AID  - 10.1101/010777
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 010777
4099  - http://biorxiv.org/content/early/2015/02/27/010777.short
4100  - http://biorxiv.org/content/early/2015/02/27/010777.full
AB  - 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.