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More Spike Sorting
Kenneth D. HarrisRutgers University
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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.
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Intra-extra Recording Simultaneous recording with a wire
tetrode and glass micropipette.
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Intra-extra Recording
Extracellular waveform is almost minus derivative of intracellular
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Bizarre Extracellular Waveshapes
Model Experiment
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Waveshape Helps Separation
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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…
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Manual Clustering Contest
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Best Ellipsoid Error RatesFind ellipsoid that minimizes weighted sum of Type I and Type II errors.
Must evaluate using cross-validation!
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Humans vs. B.E.E.R.
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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
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Problem: Overlapping Spikes
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Problem: Cellular Synchrony
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Problem: Bursting
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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.
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Problem: Dimensionality
Manual clustering only uses 2 dimensions at a timeBEER measure can use all of them
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“Automatic” Clustering•Uses all dimensions at once•Errors should be lower•Still requires human input
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Human-machine Interface
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Semi-automatic Performance
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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.
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Isolation Distance
Size of ellipsoid within which as many spikes belong to our cluster as not
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L_ratio
21ratio clusternoise
L cdf N
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False Positives and Negatives
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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
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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.
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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