Binary-Splitting Clustering

SpikeSift isolates spikes from a single neuron by recursively applying binary splits to the detected waveforms. This step filters out noise and spikes from other neurons with similar waveforms.

How are waveforms split?

Waveforms are projected onto their principal axis of variance using power iteration, then separated into two candidate groups by a fast 1D nearest-neighbor chain clustering algorithm.

Why use binary splits?

Binary splits are simple, efficient, and adaptive. Instead of choosing the number of clusters in advance, SpikeSift refines the waveforms iteratively, splitting only when needed.

How does SpikeSift decide when to stop?

After each split, the two candidate clusters are compared using their spatial waveform structure. If they are sufficiently similar — measured by their relative Euclidean distance—they are merged. This decision is governed by a similarity threshold \(\lambda\) (merging_threshold).

How is spatial waveform structure captured?

Each cluster’s average waveform is converted into a compact inter-channel difference vector, which encodes the maximum voltage difference between every pair of electrodes.

Which cluster is retained?

In Template Formation, SpikeSift keeps the cluster with the larger average amplitude. In Template Matching, it keeps the one that best matches the current template.