Parameter Tuning

SpikeSift exposes a compact set of interpretable parameters that control key trade-offs in spike sorting. All parameters are optional. The default values work robustly in most use cases.

Controlling Sensitivity vs. False Positives

The detection_sensitivity parameter (denoted \(\kappa\)) determines the detection threshold as a multiple of the median absolute deviation (MAD) on each channel.

detection_sensitivity = 10

  • Lower values (e.g., 6-8) detect weaker spikes but may increase false positives

  • Higher values (e.g., 12-14) suppress noise but may miss low-amplitude events

Controlling Cluster Merging vs. Splitting

The merging_threshold parameter (denoted \(\lambda\)) controls how similar two waveform profiles must be to be considered from the same neuron.

merging_threshold = 0.4

  • Higher values (e.g., 0.5-0.6) merge more clusters (less conservative)

  • Lower values (e.g., 0.2-0.3) preserve more distinct clusters (more conservative)

Including Rare Events

The min_spikes_per_cluster parameter (denoted \(N_{\min}\)) sets the minimum number of spikes required for a cluster to be retained.

min_spikes_per_cluster = 5

  • Lower values (e.g., 2-3) allow detection of low-firing neurons

  • Higher values (e.g., 10+) improve cluster stability and precision

Drift Alignment Resolution

The max_drift parameter (denoted \(D_{\max}\)) defines the maximum displacement (in micrometers) allowed when aligning clusters across segments.

max_drift = 30  # micrometers

  • Higher values (e.g., 50-100) allow alignment across larger probe displacements

  • Lower values (e.g., 10-20) restrict alignment to spatially consistent waveforms

Minimum Segment Duration

The min_segment_length parameter (denoted \(L_{\min}\)) sets the minimum duration (in seconds) for each processing segment.

min_segment_length = 10  # seconds

  • Smaller values (e.g., 5) improve drift tracking through finer segmentation

  • Larger values (e.g., 20) help detect rare events in stable recordings

Controlling Detection Polarity and Scaling

The detection_polarity parameter is a scalar multiplier applied to the signal during filtering. It determines the direction of spikes to detect and optionally rescales the data for amplitude consistency.

detection_polarity = -1.0

  • Use -1.0 to detect negative-going spikes (default)

  • Use +1.0 to detect positive-going spikes

  • Use a custom value (e.g., -1.0 / a) to undo scaling if the recording was linearly transformed

When Should You Change These?

You may consider tuning these parameters if you need to:

  • Increase sensitivity without over-detecting noise

  • Detect low-firing or low-amplitude spikes

  • Separate neurons with similar waveforms

  • Align spikes across faster or larger drift events

For algorithmic context, see the Implementation.