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Updated: Mar 18, 2026

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Benchmarking spike source localization algorithms in high density probes.

Hao Zhao1, Xinhe Zhang1,2, Arnau Marin-Llobet1

  • 1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, United States of America.

Plos Computational Biology
|March 16, 2026
PubMed
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Benchmarking neuron localization algorithms reveals performance differences. Simpler heuristic models excel in noisy, long-term recordings, crucial for advancing brain-machine interfaces.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Accurate neuron localization is vital for brain-machine interfaces (BMIs).
  • It enhances spike sorting and monitors probe drift for reliable long-term recordings.
  • Current localization algorithms lack comprehensive benchmarking, with algorithm selection often based on theory or visual inspection.

Purpose of the Study:

  • To benchmark commonly used neuron localization algorithms for extracellular recordings.
  • To evaluate algorithm performance using simulated and experimental ground truth datasets.
  • To assess accuracy, robustness, and runtime under ideal and degraded recording conditions.

Main Methods:

  • Utilized a biophysically realistic simulated dataset for algorithm evaluation.

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  • Employed an experimental dataset combining patch-clamp and Neuropixels extracellular recordings.
  • Systematically assessed algorithm performance against ground truth data, considering noise and electrode degradation.

    • Main Results:

    • Significant performance variations were observed among different neuron localization algorithms.
    • Complex, biophysically realistic models performed better in ideal recording conditions.
    • Simpler heuristic-based models demonstrated superior robustness to noise and electrode degradation.

    Conclusions:

    • No single algorithm is optimal for all recording scenarios.
    • Heuristic-based models are more suitable for robust, long-term neural recordings.
    • This benchmarking provides a framework for developing improved neuron localization algorithms for advanced BMIs.