Whitening - computing the covariance matrix without spikes

In some implementations (KS, Spyking Circus) the spikes are removed before computing the covariance matrix used for whitening. In the whiten.py pre-processing module as far as I can tell the random chunks used for the whitening matrix computation contain spikes. Is it worth including an option to remove spikes using some absolute threshold?

It would be great to also here any thoughts / advice people have on the whitening step. My understanding it's primary uses include reducing the spatial extent of spikes across channels to improve separation of clusters. In which case, I guess it would make sense to include the spikes in the estimation of the covariance matrix. Otherwise if the purpose is to remove correlated noise, then not including the spikes (at least intuitively at least) makes sense.

In general I am quite interested in this so if anyone can recommend any readings it would be much appreciated!

Start by reading src/spikeinterface/preprocessing/whiten.py and tracing how random chunks are selected for the whitening matrix. Review the linked KS and Spyking Circus references, then clarify whether spike removal is needed and what an option should mean; done requires an agreed behavior and corresponding implementation or tests.