unsupervised spike sorting with wavelets and super-paramagnetic clustering rodrigo quian quiroga...
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Unsupervised spike sorting with wavelets and super-paramagnetic clustering
Rodrigo Quian Quiroga
Div. of Biology
Caltech
Problem: detect and separate spikes corresponding to different neurons
Goals:• Algorithm for automatic detection and sorting of spikes. • Suitable for on-line analysis.• Improve both detection and sorting in comparison with
previous approaches.
Outline of the method:I - Spike detection: amplitude threshold.
II - Feature extraction: wavelets.
III - Sorting: Super-paramagnetic clustering.
Outline of the method
Simulated dataEx. 2
Simulation results
0/495
3/521
1/507
5/468
Misses
Number of missesExample # Nr. of
[noise level] spikes Wavelets PCA Classic
Ex. 1 [0.05] 474 0 0 16
[0.10] 521 1 6 25
[0.15] 482 1 9 69
[0.20] 490 6 7 280 (2)
Ex. 2 [0.05] 510 0 5 20
[0.10] 468 9 66 247 (2)
[0.15] 462 98 297 (1) 316 (1)
[0.20] 517 193 (2) 329 (1) 366 (1)
Ex. 3 [0.05] 495 0 1 20
[0.10] 484 65 55 223 (2)
[0.15] 479 310 (1) 310 (1) 310 (1)
[0.20] 520 344 (1) 344 (1) 344 (1)
Ex. 4 [0.05] 507 1 32 276 (1)
[0.10] 486 170 (2) 195 (2) 318 (1)
[0.15] 507 251 (2) 313 (1) 313 (1)
[0.20] 490 310 (1) 310 (1) 310 (1)
Conclusions:
• We presented an unsupervised and fast method for spike detection and sorting.
• By using a small set of wavelet coefficients we can focus on localized differences in the spike shapes of the different units.
• Super-paramagnetic clustering does not require a well-defined mean, low variance, Normality or non-overlapping clusters.
Thanks!
Richard AndersenChristof Koch
Zoltan NadasdyYoram Ben-Shaul
Sloan-Swartz Foundation