Table 1. Pairing neural data pre-processing and dimensionality reduction strategies.

Classification results obtained using a NN classifier on data processed using different combinations of algorithms. Column headings denote the dimensionality reduction algorithm: principal component analysis (PCA), multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), or ‘RAW’ when no dimensionality reduction was performed. Each column heading also lists the data pre-processing method: spike counts (COUNTS), or spike train similarity metrics (SIM). The highest classification values for each task, dimensionality, and temporal accuracy setting (rows) are highlighted. A. Results for temporal accuracy of 100msec (1/q = 100msec for SIM, bin size = 100msec. for COUNTS) B. Results for temporal accuracy of 10msec. In 7 out of 8 combinations of dataset, temporal accuracy setting, and dimensionality (table rows) the SSIMS algorithm (t-SNE + SIM) outperformed or matched the classification accuracy obtained using any of the other methods evaluated.

		RAW COUNTS	RAW SIM.		PCA COUNTS	PCA SIM.	MDS COUNTS	MDS SIM.	t-SNE COUNTS	SSIMS
A	FRG (100ms)	79%	89%	10D	76%	91%	73%	91%	83%	96%*
				2D	40%	29%	42%	58%	70%	87%*
	COUT (100ms)	91%	96%*	10D	96%*	95%	95%	95%	96%*	96%*
				2D	60%	57%	86%	86%	98%*	97%
B	FRG (10ms)	40%	91%	10D	57%	90%	51%	91%	62%	92%*
				2D	36%	42%	42%	50%	44%	88%*
	COUT (10ms)	19%	92%	10D	84%	95%	56%	95%	54%	96%*
				2D	55%	39%	89%	86%	68%	96%*