VolPy: automated and scalable analysis pipelines for voltage imaging datasets

Voltage imaging enables monitoring neural activity at sub-millisecond and sub-compartment scale, and therefore opens the path to studying sub-threshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (>800MB/s) and low signal-to-noise ratios have created a severe bottleneck for analysis of such datasets. Here we present VolPy, the first turn-key, automated and scalable pipeline to pre-process voltage imaging datasets. VolPy features fast motion correction, memory mapping, segmentation, and spike inference, all built on a highly parallelized and computationally efficient framework that optimizes memory and speed. Given the lack of single cell voltage imaging ground truth examples, we introduce a corpus of 24 manually annotated datasets from different preparations and voltage indicators. We benchmark VolPy against this corpus and electrophysiology recordings, demonstrating excellent performance in neuron localization, spike extraction, and scalability.