1 / 9

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.

barr
Download Presentation

Unsupervised spike sorting with wavelets and super-paramagnetic clustering

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related