1. Spike Sorting for Extracellular Recordings
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. Extracellular Recording Hardware
You can buy two types of hardware, allowing
Wide-band continuous recordings
Filtered, spike-triggered recordings

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

5. Raw Data
Spikes

6. 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.

7. Filtered Data
Cell 1
Cell 2

8. Spike Detection
Locate spikes at times of maximum extracellular negativity
Exact alignment is important: is it on peak of largest channel or summed channels?

9. 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.

10. 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?

11. “Feature Space”

12. Cluster Cutting Which spikes belong to which neuron?
Assume a single cluster of spikes in feature space corresponds to a single cell
Automatic or manual clustering?

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

14. Semi-automatic Clustering

15. Cluster Quality Measures
Would like to automatically detect which cells are well isolated.
Will define two measures.

16. Isolation Distance

17. L_ratio

18. False Positives and Negatives

19. Room for Improvement?
Improved alignment methods, leading to nicer clusters.
Faster automatic sorting.
Better human-machine interaction.
Fully automatic sorting.