KIASORT: Knowledge-Integrated Automated Spike Sorting for Geometry-Free Neuron Tracking.
Overview
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544
- Department of Psychology, Vanderbilt University, Nashville, Tennessee 37212
- Department of Psychology, Princeton University, Princeton, New Jersey 08544
Abstract
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://
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
No file of the authors' code could be read here: it is described below, and read at its source.
kiasort.com
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
- 27 September 2026: the link answers (HTTP 200)
The paper's code and data availability statement is in the Data section.
Tracing map
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Data
No dataset and no data link were found in the paper.
Data availability
The whole pipeline and code for KIASORT is available from https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 7 MeSH terms, 4 funders, 35 references.
Cite
This paper
Boroujeni, K. B., Womelsdorf, T., & Kastner, S. (2026). KIASORT: Knowledge-Integrated Automated Spike Sorting for Geometry-Free Neuron Tracking. The Journal of neuroscience : the official journal of the Society for Neuroscience, 46(27), e1594252026. https://
BibTeX
@article{boroujeni2026ki
author = {Boroujeni, Kianoush Banaie and Womelsdorf, Thilo and Kastner, Sabine},
title = {{KIASORT: Knowledge-Integrated Automated Spike Sorting for Geometry-Free Neuron Tracking}},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
year = {2026},
month = jul,
volume = {46},
number = {27},
pages = {e1594252026},
publisher = {Society for Neuroscience},
issn = {0270-6474},
doi = {10.1523/
url = {https://
pmid = {42248684},
pmcid = {PMC13322612}
}
RIS
TY - JOUR
AU - Boroujeni, Kianoush Banaie
AU - Womelsdorf, Thilo
AU - Kastner, Sabine
TI - KIASORT: Knowledge-Integrated Automated Spike Sorting for Geometry-Free Neuron Tracking
T2 - The Journal of neuroscience : the official journal of the Society for Neuroscience
J2 - J Neurosci
PY - 2026
DA - 2026/
VL - 46
IS - 27
SP - e1594252026
SN - 0270-6474
PB - Society for Neuroscience
DO - 10.1523/
UR - https://
LA - en
ER -
CSL-JSON
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