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SLAy-ing oversplitting errors in high-density electrophysiology spike sorting.

Sai Koukuntla1, Tate DeWeese1, Alexandra Cheng1

  • 1Department of Biomedical Engineering, Johns Hopkins University School of Medicine.

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|July 16, 2025
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Summary
This summary is machine-generated.

Automated electrophysiology spike sorting often errors, requiring manual correction. The new Spike-sorting Lapse Amelioration System (SLAy) algorithm automatically merges oversplit clusters, improving efficiency and reproducibility in neural data analysis.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • High-density silicon probes increase neuron recording counts in electrophysiology (ephys).
  • Automated spike sorting methods, while scalable, often fail to account for biophysical waveform changes, causing systematic errors.
  • Manual curation of these errors is time-consuming and lacks reproducibility.

Purpose of the Study:

  • To improve the efficiency and reproducibility of the spike-sorting pipeline.
  • To introduce an algorithm that automatically merges oversplit spike clusters.

Main Methods:

  • Developed the Spike-sorting Lapse Amelioration System (SLAy) algorithm.
  • Employed a novel neural network-based waveform similarity metric for spatially informed, time-shift invariant representations.
  • Utilized an earth-mover's distance-based cross-correlogram significance metric.

Main Results:

  • SLAy achieves approximately 85% agreement with human curators across diverse datasets.
  • Demonstrated SLAy's ability to fix spike sorting errors impacting burst-detection accuracy.
  • SLAy is computationally efficient due to GPU parallelization and multithreading.

Conclusions:

  • SLAy offers a practical and flexible solution for large-scale ephys data analysis.
  • The algorithm enhances the accuracy of neural data processing by correcting oversplit spike clusters.
  • SLAy improves downstream analysis, such as burst detection, by resolving spike sorting inaccuracies.