Add SLAy as a method for compute_merge_unit_groups https://www.biorxiv.org/content/10.1101/2025.06.20.660590v1

SLAy combines a template similarity measure, a cross-correlation significance measure and a sliding refractory period violation measure to make a merge decision. You need an analyzer with templates, correlograms and template_similarity. If you do, you can try it using:

import spikeinterface.full as si
merges = si.compute_merge_unit_groups(analyzer, preset="slay")

or, for more control, e.g.

merges = si.compute_merge_unit_groups(sa, preset="slay", steps_params ={'slay_score': {'k1': 0.7, 'slay_threshold': 0.8}})

This is an approximate implementation. Some details

• For now, we use a simple template_similarity instead of the auto-encoder
• We port the _sliding_RP_viol and _compute_xcorr functions from https://github.com/saikoukunt/SLAy . I think the sliding RP computation is fairly different between our implementation in quality_metrics. Will take a closer look...

To do:

• unit tests
• test against results in paper

With help and permission from @saikoukunt