More Spike Sorting

Kenneth D. HarrisRutgers University

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.

Intra-extra Recording Simultaneous recording with a wire

tetrode and glass micropipette.

Intra-extra Recording

Extracellular waveform is almost minus derivative of intracellular

Bizarre Extracellular Waveshapes

Model Experiment

Waveshape Helps Separation

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…

Manual Clustering Contest

Best Ellipsoid Error RatesFind ellipsoid that minimizes weighted sum of Type I and Type II errors.

Must evaluate using cross-validation!

Humans vs. B.E.E.R.

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

Problem: Overlapping Spikes

Problem: Cellular Synchrony

Problem: Bursting

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.

Problem: Dimensionality

Manual clustering only uses 2 dimensions at a timeBEER measure can use all of them

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

Human-machine Interface

Semi-automatic Performance

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.

Isolation Distance

Size of ellipsoid within which as many spikes belong to our cluster as not

L_ratio

21ratio clusternoise

L cdf N

False Positives and Negatives

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

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.

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