Table 1

Number of classification errors and noise levels, obtained using FSPS, SPC and K-means, in all simulated examples
Example no. Noise level Number of noisy spikes Classification errors
SPC K-means FSPS
Spike Shape PCA Wavelets PCA Wavelets PSVD
1 2 3 4 5 6 7 8
1. 1 [0.05] 2729 0 1 1 0 0 0
2. [0.10] 2753 0 17 5 0 0 0
3. [0.15] 2693 0 19 5 0 0 1
4. [0.20] 2678 24 130 12 17 17 47
5. [0.25] 2586 266 911 64 68 69 157
6. [0.30] 2629 838 1913 276 220 177 221
7. [0.35] 2702 1424 1926 483 515 308 354
8. [0.40] 2645 1738 1738 741 733 930 462
9. 2 [0.05] 2619 2 4 3 0 0 0
10. [0.10] 2694 59 704 10 53 2 2
11. [0.15] 2648 1054 1732 45 336 31 27
12. [0.20] 2715 2253 1791 306 740 154 48
13. 3 [0.05] 2616 3 7 0 1 0 0
14. [0.10] 2638 794 1781 41 184 850 0
15. [0.15] 2660 2131 1748 81 848 859 17
16. [0.20] 2624 2449 1711 651 1170 874 22
17. 4 [0.05] 2535 24 1310 1 212 686 0
18 [0.10] 2742 970 946 8 579 271 7
19. [0.15] 2631 1709 1716 443 746 546 51
20. [0.20] 2716 1732 1732 1462 1004 872 195
Average 2663 874 1092 232 371 332 81

Noise level is represented in terms of its standard deviation relative to the peak amplitude of the spikes. All spike classes had a peak value of 1. The absolute number of false matching spikes is shown in the column 8 as the outcome of our algorithm corresponding to the datasets containing noisy spikes (column 2).

Oliynyk et al.

Oliynyk et al. BMC Neuroscience 2012 13:96   doi:10.1186/1471-2202-13-96

Open Data