1. Spike Sorting for Extracellular Recordings
Artur Luczak
University of Lethbridge
Credits: Many slides taken from:
Kenneth D. Harris, Rutgers University

2. Aims
We would like to …
Monitor the activity of large numbers of neurons simultaneously
Know which neuron fired when
Know which neuron is of which type
Estimate our errors

3. The Tetrode Four microwires twisted into a bundle
Different neurons will have different amplitudes on the four wires

4. Buzsaki 2004

5. Methods: silicon probes
>50 cells from >100 experim. What requires 100 experiments with single glass electrode we can have in one experiment with silicon probe. Tradeoffs: quality, anatomy but we can study interactions! Less biased method
Courtesy of S. Sakata

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

7. Shape of spikes changes with distance from neuron

8. Bizarre Extracellular Waveshapes
Experiment
Model

9. Raw data from 8 shank probe
100ms of EEG recorded in cerebral cortex
Bartho et al. J Neurophysiol. 2004

10. Raw Data
Spikes

11. Filtering Data
Cell 1
Cell 2

12. High Pass Filtering
Local field potential is primarily at low frequencies.
Spikes are at higher frequencies.
So use a high pass filter. 800hz cutoff is good.

13. Two types of data Wide-band continuous recordings (LFP)
Filtered, spike-triggered recordings

14. Spike sorting

15. Data Reduction
We now have a waveform for each spike, for each channel.
Still too much information!
Before assigning individual spikes to cells, we must reduce further.

16. Principal Component Analysis
Create “feature vector” for each spike.
Typically takes first 3 PCs for each channel.
Do you use canonical principal components, or new ones for each file?

17. “Feature Space”
Luczak et al. 2005

18. Waveshape Helps Separation

19. Energy

20. Cluster Cutting Which spikes belong to which neuron?
Assume a single cluster of spikes in feature space corresponds to a single cell

21. Cluster Cutting Methods
Purely manual – time consuming, leads to high error rates.
Purely automatic – untrustworthy.
Hybrid – less time consuming, lowest error rates.

22. Semi-automatic Clustering

23. Problem: Bursting

24. Problem: Drift

25. Big Problem: Big Drift

26. Cluster Quality Measures
Would like to automatically detect which cells are well isolated.
Isolation Distance (Mahalanobis distance)

27. False Positives and Negatives

28. What else can we learn from spike waveforms?

29. Interneurons vs pyramidal cells
Luczak et al supl.mat.

30. Spatial distribution
Bartho et al. 2004

31. Questions?