Random spike selection new methods

[tayheau]

So following #4251, a PR with a few new sampling methods for random_spikes_selection :

• percentage cap
• maximum_rate cap
• temporal_bins sampling for a better temporal representation of the original population, i tried to fully vectorize it so the temporal bin sampling problem is dual with a subsorting problem.

I also added to possibility to get_segment_durations to set segment_indices to None.

[samuelgarcia]

Hi @tayheau.
I am ok with this change, could you put in the code and docstring a bit more explanation on the strategy for this new "temporal_bins" method, please ?
I am not sure to follow myself entirely the logic

[tayheau]

Hey @samuelgarcia, yeah of course, just to summarize here quickly:

So the method is temporal bin sampling and i expressed it as a sub sampling problem.

• bin_index = unit_spikes["sample_index"] // bin_size_freq allow me to allocate a bin_index to every spike in the unit, but bin_index will reset for each segment since we use a concatenated spike vector.

• group_values = np.stack((segment_index, bin_index), axis=1) allow me to create a (bin, segment) key that share the same indices as unit_spikes so that the the (bin, segment) key of the spike found at spikes[index] can be find at group_values[index].

• _, group_keys = np.unique(group_values, return_inverse=True, axis=0) just allow me to have a single integer key to rather than a (bin, segment) key.

• I then assign a random value (score) to each spike in the unit. Instead of making bins and do a random uniform distribution of k samples for each bins, we just go for each (bin, segment) subgroups and randomly sort your samples and take the k first. So now we are working with 3 main data :

  • spikes which is the concatenated spike vector of the current unit
  • a group_keys vector which assimilate a spike of a given index to a (bin, segment) group identified by an integer
  • a score vector which assimilate a spike of a given index to a random value

• order = np.lexsort((score, group_keys)) is an array of indices that will allow us to order spikes first by group_keys and then by score - i.e to have a concatenated array of spikes of ascending score per group (so per bin per segment).

• Our final goal is to take the first k_per_bin spikes of each groups. Which is then equivalent to do a unifrom random pick per temporal bin. To do so we then compute the relative ranks of each spike inside their own group based the previously computed order - we have then to compute each group starting point and length, respectivly group_start and counts). ranks would look like something like this [0,1,2,3,4,0,0,0,1,2,0,1,0,1,2,...]

• We then apply a mask to ranks to select the top k_per_bin for each group - i.e for each bin per segment. And then we apply the resulting mask to the ordered spikes vector.

I m not sure if my explainations are very clear, that's why i prefer to write it here in a first time to have some feed back on it.

[tayheau]

By the way @samuelgarcia, @alejoe91 , perhaps we can add the temporal_bins explanation to the doc and just reference to it in the docstring - so that we have a better layout and so readability. What do you think ?

[alejoe91]

By the way @samuelgarcia, @alejoe91 , perhaps we can add the temporal_bins explanation to the doc and just reference to it in the docstring - so that we have a better layout and so readability. What do you think ?

Yes it definitely deserve some more explanation! :)

[alejoe91]

I don't think we have a specific place yet, but maybe it's worth adding a Core Analyzer Extensions section here?

https://spikeinterface.readthedocs.io/en/latest/modules/core.html#sortinganalyzer