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Understanding Spike Sorting for Multineuronal Recordings

Learn methods and tools for accurate spike sorting in multineuronal recordings. Understand waveform analysis, filtering data techniques, and cluster cutting methods to identify neuron types and minimize errors. Explore spike sorting challenges and cluster quality measures for precise results.

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Understanding Spike Sorting for Multineuronal Recordings

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  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 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 Bartho et al. J Neurophysiol. 2004

  10. Spikes Raw Data

  11. Cell 1 Cell 2 Filtering Data

  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. 2007 supl.mat.

  30. Spatial distribution Bartho et al. 2004

  31. Questions?

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