Extending Kilosort for 3D Neural Probes

Kilosort 2.0 is a widely adopted spike sorting algorithm recognized for its efficiency and accuracy on planar electrode arrays, such as Neuropixels. To adapt its robust architecture to emerging three-dimensional (3D) neural probes, we present Kilosort 2.0-3D, a modified version that leverages 3D spatial information. Our modification specifically redefines the spatial processing components of Kilosort 2.0 to operate in 3D space while leaving the core template-matching process unchanged. By using synthetic extracellular recording data with ground-truth neuron positions and firing times, we demonstrate that Kilosort 2.0-3D effectively resolves spatial ambiguities and unit misclassifications inherent in 2D spatial assumptions. Our results show that Kilosort 2.0-3D achieves rotational invariance and maintains full backward compatibility with planar arrays. This work establishes a validated, scalable tool for spike sorting of high-density 3D neural electrophysiology data.