\section{Discussion}
Spike sorting has evolved over the last 20 years from a set of techniques to discriminate 5 to 10 distinct units in a space of a few tens of features to strategies for labeling many tens to hundreds of units in large feature spaces. However, the number of distinct units is expected to soon reach well into the thousands. The emerging demands these growing datasets have inspired the development of more capable automatic sorting algorithms. However, the manual overview of the spike sorting process remains an essential step of the pipeline, albeit one that is becoming increasingly labor intensive and error prone. The problem of producing human readable visualizations of structures that exist in large dimensional spaces is of course not unique to spike sorting but is common in all data intensive fields. One commonly employed strategy for working with such data is the use of non-linear dimensionality reduction methods that try to retain in their projections as much of the initial structure of the data as possible. The current state of the art in these methods is t-sne, which manages to project onto two or three dimensions the multi-dimensional data in a way that preserves its local structure and makes the visualization of this structure both possible and intuitive. A large literature has evolved applying this technique to a diverse number of big data problems ranging from AI \cite{mnih_human-level_2015} to genetics \cite{xu_visualization_2014, platzer_visualization_2013}, and behavior \cite{berman_mapping_2014}. We show here that, in the case of spike sorting, using t-sne on the results of either a masked PCA or of template matching, provides a visualization that offers a clear picture of how the individual spikes form groups in the high dimensional feature space. These groups directly relate to the individual units that generate the spikes, which can now be visualized and curated in an intuitive manner or compared to the results of an automatic clustering method. Furthermore, t-sne allows a visual inspection of the structural groups that different data sets of spikes contain, something that can be achieved neither by looking at 2D plots of pairs of features within a multi-dimensional space or by any current clustering algorithm. Here we demonstrate that t-sne visualization immediately reveals differences between biological and hybrid datasets (Figure 3), how spike sorting challenges accrue when each spike is represented by features generated by fewer and fewer electrodes (Figure 4), and that manual curation of the output from an automated template matching procedure becomes effortless in a low dimensional space (Figure 5).
\\The generality of the t-sne method also makes it applicable to datasets that arise from diverse sources. The successful application of t-sne to behavioral data \cite{berman_mapping_2014} suggests the possibility of applying it to datasets comprised of a combination of spike and other electrophysiological signals (LFP, etc.) as well as the behavioral features occuring during the recorded neural activity. It is thus possible that groupings in the t-sne visualization of such a dataset could provide informative clues as to the connection between different forms of brain activity and to behavior itself.
\\Finally, we are now working on using t-sne embeddings into three-dimensional space to generate spatial (X,Y,Z) representations that can be explored in virtual reality, thus providing a new form of access to complex datasets. (Supplementary Video 2)