Template Matching

After Template Formation, SpikeSift scans for similar spikes throughout the current segment.

Why recover more spikes after clustering?

Initial detection uses a strict threshold to ensure reliability. But that means many valid spikes — especially those close to the detection threshold — may go undetected.

Does this mean scanning the entire signal?

No. SpikeSift avoids full convolutions. It begins with a shortlist of likely spike locations (local minima) and then checks each one for similarity to the current template.

How is similarity evaluated efficiently?

Most candidates are filtered out with a fast amplitude-based test across the five nearest channels. This lightweight check eliminates the majority of false detections.

Why not rely on a fixed threshold?

Because a fixed cutoff rarely separates spikes from noise across all conditions. Instead, SpikeSift uses a Binary-Splitting Clustering step with the template as reference to isolate a clean unit.

What if clustering fails?

If the new cluster is too small (i.e., fewer than \(N_{\min}\) (min_spikes_per_cluster) spikes) or too weak (below the detection threshold), it’s discarded. The respective reference channel used in Template Formation is also skipped in future passes to avoid unnecessary computation.

What happens to accepted spikes?

Their average waveform is subtracted at each occurrence. This removes interference from strong units and helps uncover weaker or overlapping spikes in later iterations.