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Unsupervised spike sorting with wavelets and super-paramagnetic clustering

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. Outline of the method:. I - Spike detection: amplitude threshold.

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Unsupervised spike sorting with wavelets and super-paramagnetic clustering

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  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. Outline of the method: I - Spike detection: amplitude threshold. II - Feature extraction: wavelets. III - Sorting: Super-paramagnetic clustering. Goals: • Algorithm for automatic detection and sorting of spikes. • Suitable for on-line analysis. • Improve both detection and sorting in comparison with previous approaches.

  4. Outline of the method

  5. Simulated data Ex. 2

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

  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

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