Spike Sorting I:
Bijan Pesaran
New York University
Acknowledgements
• Ken Harris and Samar Mehta at
Neuroinformatics course Woods Hole.
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
Voltage (A/D Levels)
THE PROBLEM: Multiple Neural Signals
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3 msec
Primate retinal ganglion cells, courtesy of the lab of Dr. E.J. Chichilnisky
THE GOAL: Spike Times of Single Neurons
Region from previous slide
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Raw Data
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Spike
Detector
Neuron #1 Spikes
Neuron #2 Spikes
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THE ‘GRADUATE STUDENT’ ALGORITHM
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Raw Data
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Threshold detector at 32
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Interspike Interval Histogram
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Candidate Waveforms
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# of Intervals
Spike Height vs. Width Plot
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Width (msec)
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A GENERAL FRAMEWORK
Locate Spikes
Preprocess
Waveforms
Density
Estimation
Spike
Classification
Quality
Measures
Extracellular Recording Hardware
• You can buy two types of hardware,
allowing
• Wide-band continuous recordings
• Filtered, spike-triggered recordings
The Tetrode
• Four microwires twisted into a bundle
• Different neurons will have different
amplitudes on the four wires
Raw Data
Spikes
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.
Filtered Data
Cell 1
Cell 2
Spike Detection
• Locate spikes at times of maximum
extracellular negativity
• Exact alignment is important: is it on peak
of largest channel or summed channels?
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.
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?
“Feature Space”
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?
Cluster Cutting Methods
• Purely manual – time consuming, leads to
high error rates.
• Purely automatic – untrustworthy.
• Hybrid – less time consuming, lowest error
rates.
Semi-automatic Clustering
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
Model
Bizarre Extracellular Waveshapes
Experiment
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 Rates
Find ellipsoid that
minimizes weighted
sum of Type I and
Type II errors.
Must evaluate using
cross-validation!
Humans vs. B.E.E.R.
Waveshape Helps Separation
Why were human errors higher?
• 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
• Are clusters 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 time
BEER measure can use all of them
“Semi-Automatic” Clustering
•Uses all dimensions at once
•Errors should be lower
•Still requires human input
Semi-automatic Performance
klustakwik.sourceforge.net
Software: KlustaKwik
• Mixture of Gaussians, unconstrained
covariance matrices
• Speed is crucial
• CEM Algorithm – faster than EM
• Most probabilities not calculated
• Local maxima result in over- and underclustering
• Split and merge features to tunnel out of local
maxima
• Still requires supercomputer resources.
klusters.sourceforge.net
Software: Klusters
Waveforms
Timecourse
Auto/Cross
correlograms
Grouping Assistant
Recluster Feature
Ergonomic Design
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

Lratio   1  cdf  
noise
2

Ncluster
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
Easy
• Improved spike detection and alignment
• Quality measures that estimate % error
• Fully automatic sorting
• Resolve overlapping spikes
Hard