1. Unsupervised spike sorting with wavelets and super-paramagnetic clustering Rodrigo Quian Quiroga Div. of Biology Caltech

2. Problem: detect and separate spikes corresponding to different neurons

3. 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.

4. Outline of the method

5. Simulated data Ex. 2

6. Simulation results 0/495 3/521 1/507 5/468 Misses

7. Number of misses

8. 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.

9. Thanks! Richard Andersen Christof Koch Zoltan Nadasdy Yoram Ben-Shaul Sloan-Swartz Foundation