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Voltage imaging enables monitoring neural activity at sub-millisecond and 12 sub-compartment scale, and therefore opens the path to studying sub-threshold activity, synchrony, 13 and network dynamics with unprecedented spatio-temporal resolution. However, high data rates 14 (>800MB/s) and low signal-to-noise ratios have created a severe bottleneck for analysis of such 15 datasets. Here we present VolPy, the first turn-key, automated and scalable pipeline to pre-process 16 voltage imaging datasets. VolPy features fast motion correction, memory mapping, segmentation, 17 and spike inference, all built on a highly parallelized and computationally efficient framework that 18 optimizes memory and speed. Given the lack of single cell voltage imaging ground truth examples, 19 we introduce a corpus of 24 manually annotated datasets from different preparations and voltage 20 indicators. We benchmark VolPy against this corpus and electrophysiology recordings, 21 demonstrating excellent performance in neuron localization, spike extraction, and scalability.

Voltage imaging enables monitoring neural activity at sub-millisecond and sub-cellular scale, unlocking the study of subthreshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (>800MB/s) and low signal-to-noise ratios create bottlenecks for analyzing such datasets. Here we present VolPy, an automated and scalable pipeline to pre-process voltage imaging datasets. VolPy features motion correction, memory mapping, automated segmentation, denoising and spike extraction, all built on a highly parallelizable, modular, and extensible framework optimized for memory and speed. To aid automated segmentation, we introduce a corpus of 24 manually annotated datasets from different preparations, brain areas and voltage indicators. We benchmark VolPy against ground truth segmentation, simulations and electrophysiology recordings, and we compare its performance with existing algorithms in detecting spikes. Our results indicate that VolPy’s performance in spike extraction and scalability are state-of-the-art.