T-SNE visualization of large-scale neural recordings - Supplementary

George Dimitriadis1,*, Joana Neto1, Adam R. Kampff1

1Sainsbury Wellcome Centre, UCL, London, UK
*g.dimitriadis@ucl.ac.uk

Design of the 32 channel probe used to collect the PD32 data set.

Center of mass method for speeding up the t-sne process. We use two of our data sets (A: PD128, B: HD1) to check whether it was possible to pass through the t-sne algorithm only a percentage of the spikes and use the resulting 2D embedding as a template for positioning the rest of the spikes on the same plot. In both our experiments, the spikes that were placed post-t-sne using as a position the average of their 5 nearest neighbors in the high dimensional space, end up in the same place on the plot as would have been expected if they were part of the original t-sne.

Receiver Operating Characteristics (ROC) values for the different data sets used. Each point represents a unit with known spikes that appears on the t-sne plot with some spikes not belonging to it (False Positives). The blue line is the 50% True Positive Rate over False Positive Rate ratio. All the points had such small False Positive Rates that we used a semi-log plot to be able to show the relative distribution of the points on the plot.