Spike sorting for polytrodes: a divide and conquer approach

Nicholas V Swindale,Martin A Spacek

doi:10.3389/fnsys.2014.00006

Nicholas V Swindale, Martin A Spacek

Open Access

https://doi.org/10.3389/fnsys.2014.00006

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Abstract

In order to determine patterns of neural activity, spike signals recorded by extracellular electrodes have to be clustered (sorted) with the aim of ensuring that each cluster represents all the spikes generated by an individual neuron. Many methods for spike sorting have been proposed but few are easily applicable to recordings from polytrodes which may have 16 or more recording sites. As with tetrodes, these are spaced sufficiently closely that signals from single neurons will usually be recorded on several adjacent sites. Although this offers a better chance of distinguishing neurons with similarly shaped spikes, sorting is difficult in such cases because of the high dimensionality of the space in which the signals must be classified. This report details a method for spike sorting based on a divide and conquer approach. Clusters are initially formed by assigning each event to the channel on which it is largest. Each channel-based cluster is then sub-divided into as many distinct clusters as possible. These are then recombined on the basis of pairwise tests into a final set of clusters. Pairwise tests are also performed to establish how distinct each cluster is from the others. A modified gradient ascent clustering (GAC) algorithm is used to do the clustering. The method can sort spikes with minimal user input in times comparable to real time for recordings lasting up to 45 min. Our results illustrate some of the difficulties inherent in spike sorting, including changes in spike shape over time. We show that some physiologically distinct units may have very similar spike shapes. We show that RMS measures of spike shape similarity are not sensitive enough to discriminate clusters that can otherwise be separated by principal components analysis (PCA). Hence spike sorting based on least-squares matching to templates may be unreliable. Our methods should be applicable to tetrodes and scalable to larger multi-electrode arrays (MEAs).

Highlights

A classical technique for studying the brain is to record electrical signals with a microelectrode placed near the cell body of a neuron
Specific parameters used for the results reported here were σ1 = 5 μV, with σm increasing by 10% on successive iterations and terminating with a value for which K = 1; the merge distance ε = σm; the % change threshold θN = 5%; the clustering score threshold θc = 8 and the minimum cluster size Nmin = 50
Not specific to polytrodes and limiting, is the lack of a reliable, fast, algorithm for detecting clusters in the features extracted from the spike waveforms

Summary

Introduction

A classical technique for studying the brain is to record electrical signals with a microelectrode placed near the cell body of a neuron. Larger multi-electrode arrays (MEAs; Litke et al, 2004; Segev et al, 2004; Frey et al, 2009) designed for recording from retinal patches or brain slices may have hundreds or even thousands of sites. All of these types of electrode are designed so that a given neuron will produce a characteristic pattern of voltage change on a number of adjacent recording sites depending on the position of the unit relative to the sites. Additional factors that make sorting difficult are (a) variability in spike shape of single units over time (Fee et al, 1996a,b; Quirk and Wilson, 1999); (b) similarity in spike shapes between neurons; (c) the frequently non-Gaussian nature of the noise in the clusters (Fee et al, 1996a) and (d) the large amount of data (hours of recording and millions of spikes leading to file sizes of several GB) that may have to be processed

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