Iterative Sorting

SpikeSift performs a loop of Template Formation and Template Matching, using Binary-Splitting Clustering to isolate spikes from one neuron at a time.

Why isolate one neuron at a time?

It makes the problem simpler and more robust. Clustering all spikes at once is difficult — especially when waveforms overlap or vary in amplitude. By removing strong units early, SpikeSift clears the way for weaker or overlapping spikes that would otherwise be missed.

Why use a template matching approach?

Without a clear template waveform, it’s hard to recover all spikes from a neuron without also capturing noise or spikes from other neurons. SpikeSift starts with a small, reliable cluster to build a clean template, used to detect additional matches — improving sensitivity without losing precision.

When does the loop stop?

The loop ends when no more units can be isolated — either because the remaining spikes are too weak or too inconsistent to form a reliable cluster.