\subsection{Manual overview of automated clustering}
Template matching algorithms are proving to be fast alternatives to the more classical detect, embed and classify spike sorting pipeline, especially for probes with large number of channels and recordings in the order of millions of spikes. These algorithms iteratively use the data to generate templates of the spikes present and then use these templates to detect and classify the individual spikes. The result is a list of spike templates and the euclidean distances of each spike to each of these templates. Each spike is assigned to the template which matches it the most (i.e. has the smallest distance to). We run the kilosort algorithm on the PD128 data set with a maximum number of templates set to 256. It generated 252 templates and detected 466313 spikes. We then used the distances of each spike to all the templates as the t-sne input feature space (a 252 dimensional one), setting to 0 all the distances but the closer 16. The result of this t-sne can be seen in Figure 5. The embedding shows three different types of spike groupings. The first is a really tight grouping (denoted from here on as points) where the spread of the whole group in both the x and y axis is at least 3 orders of magnitude smaller than the total spread of the embedding. The second is a linear grouping where there are obvious two axis to the grouping (not necessary along the x and y axis of the full embedding), one with a very small and one with a much larger spread (denoted from here on as lines). The third type of grouping has large spreads on both the x and y axis, no less than 2 order of magnitudes smaller than the total t-sne spread (denoted as blobs). Using the manual clustering gui we could easily evaluate in which kilosort assigned templates the spikes in the different embedding groupings belonged to and vise versa (i.e. which groupings held spikes from any single template). From this comparison five general categories emerged. The first category involved groupings that each was fully represented by a single template whose heatmap and autocorrelogram both indicated a single unit.There were 73 of these single units (SUs) out of which the majority (XX) were point groupings, XX were line groupings and a small minority (XX) were blobs. This category included XXX spikes. The second category were groupings that again each was fully defined by a single template but this time the autocorrelogram (and sometimes the heatmap) indicated multiple units (MUs). There were XX of these MUs, the majority of which were either blob (XX) or line (XX) groupings with only a small minority (XX) being point units. There were a total of XXX spikes in this category of MUs. The third category was represented by the three largest, semi-connected, blob groupings in the center of the embedding and the 4th largest blob on their right. Their 'spikes' belonged to 5 templates (4 for the central blob and one for the blob on the side) and each template had its 'spikes' grouped mainly together but with a large minority spread throughout other parts of the groupings. These 5 templates were the most numerous and they all showed average spike trains that were very small in amplitude (< 100uV), did not resemble spike shapes and were identical on all channels. We denoted this category as noise. It included a total of XXX 'spikes'. In the fourth category we lambed together all the cases that after splitting or merging of templates or moving spikes from one template to another ended up with acceptable SUs (based on their average spike shape, their probe heatmap and their autocorrelogram). We arrived at XX SUs that included a total of XXX spikes. In these cases the combined information of where the spike lied in the embedding and in which template it belonged to made it fairly straight forward to either appropriately merge or split templates or move spike along templates. Finally the 5th category involved all these spikes that we were unable to assign to any of the previous divisions (SUs, MUs or noise). There were spikes that showed no abovious correlation between the embedding position and their template assignment. For example one blob in this category had XXX spikes that kilosort had assigned to 92 seperate templates (most of which were templates with fewer than 10 spikes each). 