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Updated: Jun 29, 2026
10:31
A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
Published on: February 10, 2017
11.1K
Assessing cross-contamination in spike-sorted electrophysiology data
Jack P Vincent1,2, Michael N Economo1,2,3
1Department of Biomedical Engineering, Boston University, Boston, MA.
Biorxiv : the Preprint Server for Biology
|January 8, 2024
Summary
Accurately assessing spike sorting accuracy is crucial for extracellular electrophysiology. This study introduces a new analytical solution to predict false discovery rate (FDR) from inter-spike-interval (ISI) violations, improving spike sorting quality assessment.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Signal Processing
Background:
- Extracellular electrophysiology enables simultaneous recording of thousands of neurons.
- Accurate spike sorting is essential for analyzing large-scale neural data.
- Inter-spike-interval (ISI) violations are commonly used to estimate spike sorting accuracy, but their relationship with false discovery rate (FDR) is not well understood.
Approach:
- Developed an analytical solution to predict FDR from ISI violation rates.
- Validated the model using in silico Monte Carlo simulations.
- Applied the model to publicly available extracellular electrophysiology datasets.
Key Points:
- The relationship between ISI violation rate and FDR is highly nonlinear.
- FDR prediction depends on firing rate, neural activity correlation, and contaminant neuron count.
- Predicted median FDRs in public datasets ranged from 3.1% to 50.0%.
Conclusions:
- The analytical solution provides a principled method for assessing spike sorting accuracy.
- While predicting FDR for single clusters is challenging, population-level estimation is accurate.
- Findings will aid researchers in reliably evaluating spike sorting performance in large-scale neural recordings.
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