Spike Sorting for Extracellular Recordings

Kenneth D. HarrisRutgers University

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

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

Cluster Quality Measures Would like to automatically detect

which cells are well isolated.

Will define two measures.

Isolation Distance

L_ratio

21ratio clusternoise

L cdf N

False Positives and Negatives

Room for Improvement? Improved alignment methods, leading

to nicer clusters.

Faster automatic sorting.

Better human-machine interaction.

Fully automatic sorting.