\subsection{Intuitive manual curation 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 a large number of densely spaced electrodes. These algorithms iteratively 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 that matches it the best (i.e. it has the smallest distance to). We report here how t-sne embeds these template distance features and how we utilized our GUI to sanity check the automatically generated results and get a broader overview of the clustering problem for a specific data set.
\\We ran the kilosort algorithm \cite{pachitariu_kilosort:_2016} on the PD128 data set with a maximum number of templates set to 256 (double the number of channels on our probe). 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 except the nearest 16 templates. The result of this t-sne embedding can be seen in Figure 5. The embedding shows three different types of spike groupings. The first are a number of very tight groupings (denoted from here on as "points") where the spread of the entire 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 is an obvious extended 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 orders of magnitudes smaller than the total t-sne spread (denoted here on as "blobs"). 
\\Using the manual clustering GUI we could easily evaluate in which kilosort assigned templates the spikes in the different embedding groupings belonged 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 was fully represented by a single template and whose heatmap and autocorrelogram both indicated a single unit. There were 73 of these single units (SUs) out of which the majority (49) were point groupings, 18 were line groupings and a small minority (7) were blobs. This category included 109890 spikes. The second category were groupings that again was fully defined by a single template but this time the autocorrelogram (and sometimes the heatmap) indicated multiple units (MUs). There were 39 of these MUs, the majority of which (26) were blob groupings with only a small minority being either lines (7) or points (6). There were a total of 134288 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 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 139073 'spikes'. 