Assess electrophysiology cluster quality with ISI violation rates, signal-to-noise ratios, and Allen Institute grading criteria.
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A cluster can have low ISI violation rate but poor SNR (barely detectable unit) or high SNR but high ISI violations (well-detected but contaminated). The Allen Institute criteria require BOTH low ISI and high SNR for a “Good” grade.
If electrode drift is present, peak amplitude varies over time. A single SNR value computed from the mean waveform may overestimate quality for drifting clusters. Consider computing SNR in time bins.
The standard 1 ms refractory period works for most cortical neurons, but some fast-spiking interneurons have shorter refractory periods (~0.7 ms). Adjust the threshold if your target population includes fast-spiking cells.
Nature Neuroscience and other journals increasingly require spike sorting QC metrics. Report the distribution of quality grades, mean ISI violation rate, and the criteria used for including/excluding clusters.
ISI violation rate computed as violations within 1 ms refractory period divided by total spikes. Contamination estimated via Hill et al. (2011): fraction (T_recording / (2 T_refractory)). SNR = peak amplitude / noise RMS. Quality grades follow Allen Institute Visual Coding criteria: Good (ISI < 1%, SNR > 5), MUA (ISI 1–5% or SNR 2–5), Noise (ISI > 5% or SNR < 2).
Last validated 2026-04-08. Calculations are designed for planning and documentation support; verify procurement decisions against manufacturer specifications or institutional SOPs.
ConductScience Spike Sorting QC Calculator (v1.0). ConductScience, Inc. 2026. Available at: https://conductscience.com/tools/spike-sorting-qc-calculator
Hill DN, Mehta SB, Kleinfeld D. Quality metrics to accompany spike sorting of extracellular signals. J Neurosci. 2011;31(24):8699–8705.
Allen Institute for Brain Science. Visual Coding — Neuropixels spike sorting quality metrics. Technical White Paper. 2019.
Spike sorting assigns extracellular voltage waveforms to putative single neurons. Quality control is essential because sorting errors propagate into all downstream analyses — firing rates, correlations, tuning curves, and population decoding.
Several issues can confound spike sorting quality assessment:
• Drift: Electrode drift causes waveform amplitude to change over time, splitting a single unit into multiple clusters or merging distinct units • Bursting neurons: Neurons that fire in bursts naturally have short ISIs that can be misclassified as refractory violations if the refractory period threshold is set too long • Overlapping spikes: When two neurons fire nearly simultaneously, their waveforms superimpose, creating hybrid shapes that confuse sorters • Noise floor changes: Fluctuating noise RMS (e.g., from movement artifacts) makes SNR unreliable if computed over the full recording • Low firing rate units: Clusters with very few spikes have unstable ISI violation rates — a single coincidental violation can push the rate above thresholds • Over-splitting: Aggressive sorting can split one neuron into multiple high-quality clusters, inflating the Good cluster count while reducing each cluster’s spike count
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