\subsection{Paired Data}
We ran the t-sne algorithm (for a 2D embedding) on the full 128820 spikes of the PD128 set; each spike was represented as a masked vector of 384 dimensions (128 channels * 3 largest PCA components per channel). The masking of the PCA components (i.e. modulated between 0 and their full value) is described in Rossant et al. \cite{rossant_spike_2016}. The resultant embedding is shown in Figure 2A. The embedding generates a number of distinct groups of spikes and a number of more diffuse clouds with varying internal densities. A spike grouping at the bottom of the figure contains the majority of the extracellular spikes that correspond to juxtacellularly recorded spikes (i.e  ground truth spikes from the isolated single cell). The figure shows that a small percentage of the spikes coming from the labeled cell are spread throughout the entire 2D space, while the labeled cluster also contains some spikes that are not generated by the labeled cell. For this set of ground truth spikes the Precission, Recall and F-factor are 0.86, 0.83 and 0.84 respectively, which translates to 14\% of the ground truth spikes not being classified as part of the main cluster and to 17\% of the main cluster spikes being misclassified as part of the ground-truth. 
\\The color coding of the samples with a corresponding juxtacellular spike shows that the embedded cluster's internal structure has a strong relationship with physical characteristics of the actual spikes, in this case their peak amplitude. The t-sne algorithm is designed to retain the local structure of the multi-dimensional space during its transformation into the 2D embedding space. That means that any correspondence between the physical properties of the samples and their distances in the high dimensional space of their PCA components will be retained in the subsequent embedding, at least for the samples that are close together (i.e. the ones within a single cluster). The 2D embedding space is easy to visualize and thus allows quickly noticing such relationships. By simply changing the color coding scheme to represent different properties one can easily browse the relationships (or lack thereof) between any number of specific characteristics. For example, color coding the spikes according to the time they appear in the recording reveals that there is no correspondence between the size of spikes and the time they were recorded (results not shown).
\\The clearest picture of how t-sne operates on the data can be gained from videos of the t-sne process in which each frame is the result of progressive iterations of the embedding algorithm. We captured this process for the PD128 data set in Supplementary Video 1. 
\\Figure 2B shows the t-sne embedding of the 73814 spikes of the PD32 set. Here the embedding generates a more homogeneous cloud with significantly fewer easy to delineate groupings of spikes. The Precission, Recall and F-factor for the juxtacellularly labeled spikes in this case are 0.91, 0.65 and 0.76 respectively for the t-sne/DBSCAN generated cluster with the most color coded spikes. In this case the cluster is a fairly homogeneous one (with only 9\% of the spikes not belonging to the unit it represents) but it fails to capture a large percentage (\%35) of spikes from the same unit which end up in other groups. Also, the cluster's internal structure shows no correspondence with the spikes' peak amplitude as measured by the juxtacellular electrode. We will propose (see Probe electrode densities and clustering quality) that the drop in clustering performance between the P128 and P32 embeddings is mainly due to the lower sampling density of the extracellular space provided by the 32 channel probe.