SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters

• DOI: 10.7554/elife.55167
• Publication Date: 2020-05-19T16:00:20Z
• Authors: Jeremy Magland; James J Jun; Elizabeth Lovero; Alexander J Morley; Cole Lincoln Hurwitz; Alessio Paolo Buc...; Samuel Garcia; Alex H Barnett
• Keywords: 570; none; QH301-705.5; [SDV]Life Sciences [q-bio]; Science; Models, Neurological; 610; Action Potentials; MESH: Algorithms; spike sorting; neuroscience; MESH: Software; Computer-Assisted; MESH: Models; Animals; MESH: Animals; Biology (General); MESH: Signal Processing; reproducibility; MESH: Action Potentials; validation; Q; R; Reproducibility of Results; Signal Processing, Computer-Assisted; electrophysiology; MESH: Reproducibility of Results; Neurological; Medicine; ground truth; Algorithms; Software; Neuroscience

Spike sorting is a crucial step in electrophysiological studies of neuronal activity. While many spike sorting packages are available, there is little consensus about which are most accurate under different experimental conditions. SpikeForest is an open-source and reproducible software suite that benchmarks the performance of automated spike sorting algorithms across an extensive, curated database of ground-truth electrophysiological recordings, displaying results interactively on a continuously-updating website. With contributions from eleven laboratories, our database currently comprises 650 recordings (1.3 TB total size) with around 35,000 ground-truth units. These data include paired intracellular/extracellular recordings and state-of-the-art simulated recordings. Ten of the most popular spike sorting codes are wrapped in a Python package and evaluated on a compute cluster using an automated pipeline. SpikeForest documents community progress in automated spike sorting, and guides neuroscientists to an optimal choice of sorter and parameters for a wide range of probes and brain regions.