![]() In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms pre-process, post-process, and visualize extracellular datasets validate, curate, and export sorting outputs and more. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. Much development has been directed toward improving the performance and automation of spike sorting. ![]()
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