1. More Spike Sorting Kenneth D. Harris Rutgers University

2. How Do You Know It Works? We can split waveforms into clusters, but are we sure they correspond to single cells? Simultaneous intra- and extra-cellular recordings allow us to estimate errors. Quality measures allow us to guess errors even without simultaneous intracellular recording.

3. Intra-extra Recording Simultaneous recording with a wire tetrode and glass micropipette.

4. Intra-extra Recording Extracellular waveform is almost minus derivative of intracellular

5. Bizarre Extracellular Waveshapes ModelExperiment

6. Waveshape Helps Separation

7. Two Types of Error Type I error (false positive) Incorrect inclusion of noise, or spikes of other cells Type II error (false negative) Omission of true spikes from cluster Which is worse? Depends on application…

8. Manual Clustering Contest

9. Best Ellipsoid Error Rates Find ellipsoid that minimizes weighted sum of Type I and Type II errors. Must evaluate using cross-validation!

10. Humans vs. B.E.E.R.

11. Why were human errors so high? To understand this, try to understand why clusters have the shape they do Simplest possibility: spike waveform is constant, cluster spread comes from background noise Clusters should be multivariate normal

12. Problem: Overlapping Spikes

13. Problem: Cellular Synchrony

14. Problem: Bursting

15. Problem: Misalignment When you have a spike whose peak occurs at different times on different channels, it can align on either. This causes the cluster to be split in two.

16. Problem: Dimensionality Manual clustering only uses 2 dimensions at a time BEER measure can use all of them

17. “Automatic” Clustering Uses all dimensions at once Errors should be lower Still requires human input

18. Human-machine Interface

19. Semi-automatic Performance

20. Cluster Quality Measures Would like to automatically detect which cells are well isolated. BEER measure needs intracellular data, which we don’t have in general. Will define two measures that only use extracellular data.

21. Isolation Distance Size of ellipsoid within which as many spikes belong to our cluster as not

22. L_ratio

23. False Positives and Negatives

24. Which Measure to Use? Isolation distance correlates with false positive error rates Measures distance to other clusters L_ratio correlates with false negative error rates Measures number of spikes near cluster boundary

25. Conclusions Automatic clustering will save time and reduce errors. Errors can be as low as ~5%. Quality measures give you a feeling of how bad your errors are.

26. Room for Improvement Make it faster Better human-machine interaction Improved spike detection and alignment Quality measures that estimate % error Fully automatic sorting Resolve overlapping spikes Easy Hard