Spike Sorting for Extracellular Recordings Kenneth D. Harris Rutgers 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

False Positives and Negatives

Room for Improvement? Improved alignment methods, leading to nicer clusters. Faster automatic sorting. Better human-machine interaction. Fully automatic sorting.