Unsupervised spike sorting with wavelets and super-paramagnetic clustering

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Unsupervised spike sorting with wavelets and
super-paramagnetic clustering
Rodrigo Quian Quiroga Div. of Biology Caltech
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Problem detect and separate spikes corresponding
to different neurons
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Goals

• Algorithm for automatic detection and sorting of
  spikes.
• Suitable for on-line analysis.
• Improve both detection and sorting in comparison
  with previous approaches.

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Outline of the method
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Simulated data
Ex. 2
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Simulation results
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Number of misses
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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.

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