@article{bibcite_204756,
  author = {Kianoush Boroujeni and Thilo Womelsdorf and Sabine Kastner},
  title = {KIASORT: Knowledge-Integrated Automated Spike Sorting for Geometry-Free Neuron Tracking},
  abstract = {<p>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{\textendash}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 <a href="https://kiasort.com">https://kiasort.com</a>.</p>},
  year = {2026},
  journal = {J. Neurosci.},
  volume = {46},
  url = {https://www.jneurosci.org/content/46/27/e1594252026},
}