RT Journal Article
SR Electronic
T1 Supervised learning sets benchmark for robust spike detection from calcium imaging signals
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 010777
DO 10.1101/010777
A1 Lucas Theis
A1 Philipp Berens
A1 Emmanouil Froudarakis
A1 Jacob Reimer
A1 Miroslav Román Rosón
A1 Tom Baden
A1 Thomas Euler
A1 Andreas Tolias
A1 Matthias Bethge
YR 2015
UL http://biorxiv.org/content/early/2015/02/27/010777.abstract
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.