KIASORT: Knowledge-Integrated Automated Spike Sorting for Geometry-Free Neuron Tracking.

Modern high-density neural recordings demand spike-sorting algorithms that can handle diverse probe geometries and complex, neuron-specific drift, yet existing methods often rely on rigid geometric assumptions and one-dimensional drift models. Here, we introduce KIASORT (Knowledge-Integrated Automated Spike Sorting), a geometry-free approach for per-neuron drift tracking. KIASORT builds channel-specific sorting models from a hybrid linear-nonlinear sample-sorting stage, using representative template banks or supervised classifiers. These channel-specific models then sort spikes by independently tracking each neuron, unconstrained by probe layout. Biophysical simulations showed that even submicron probe displacements induce neuron-specific waveform distortions that standard drift models cannot correct. In ground-truth benchmarks with heterogeneous, neuron-specific drift, KIASORT outperformed Kilosort4 in recovering high-quality units while maintaining real-time performance on standard CPUs. Its robustness was further illustrated on both primate and mouse data. KIASORT combines automated sorting with manual curation in a unified graphical interface, offering a complete and user-friendly spike-sorting platform. The software is freely available at https://kiasort.com.

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Authors

Boroujeni KB; Womelsdorf T; Kastner S

Published

Jan 1, 2026

Journal or venue

The Journal of neuroscience : the official journal of the Society for Neuroscience

DOI

10.1523/jneurosci.1594-25.2026

Paper type

journal article

Source

Pubmed