Spike sorting for polytrodes: a divide and conquer approach

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).

A Fully Automated Approach to Spike Sorting

“…Although fully-automated spike sorting has been of interest for many years (Abeles and Goldstein, 1977), and despite prior efforts to automate sorting algorithms (Calabrese and Paninski, 2011; Carlson et al, 2014; Einevoll et al, 2012; Ekanadham et al, 2014; Franke et al, 2010; Franke et al, 2015; Kadir et al, 2014; Lewicki, 1998; Marre et al, 2012; Pillow et al, 2013; Quiroga et al, 2004; Rodriguez and Laio, 2014; Swindale and Spacek, 2014; Takekawa et al, 2012), the majority of laboratories still rely heavily on manual intervention. In this work, we set out to develop a fully automated spike sorting algorithm having error rates that are comparable to or lower than those of existing manual and semi-manual approaches, and with runtimes faster than acquisition times.…”

YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina

“…To address these challenges, we combine several key ideas: Divide-and-conquer. The first step is to try to divide the dataset into smaller subsets before applying any expensive clustering algorithms (Swindale and Spacek (2014)). This "divide-and-conquer" approach is critical for both statistical and computational reasons.…”

State-of-the-art MEMS and microsystem tools for brain research

“…This same study also found as many as 24 single units in a virtual tetrode in the relatively sparse visual cortex. More recently, software algorithms that utilize precise spatial information as part of the sorting logic show great promise for improving both speed and accuracy 30,[80][81][82] . Investigating the optimal microelectrode size and pitch for speed and accuracy in spike sorting is an important research area.…”

“…where I i is the current of one point source, σ is the conductivity of the extracellular space, and r i is the distance from the source to the electrode (a dipole assumption results in a more complex isopotential and an amplitude proportional to 1/r 2 , but arguably the ratio is neither a monopole or dipole [28][29][30][31] ). Given the close spacing of tetrodes, multiple spike signals can be used to triangulate and localize a specific cell in space 32 .…”

“…Thermal, electrical, and biological noise sources can combine to severely limit the signalto-noise ratio 76,77 and, when combined with spiking variability, can result in false spike detection, missed detection, and erroneous classifications 78,79 . Despite 450 years of advances and a plethora of algorithms for addressing issues of accuracy and speed, neuroscientists, mathematicians, and technologists are still far from reaching the goal of real-time automated spike sorting 30,80 . The stereotrode 27 and tetrode 22 were hardware solutions designed to help address the problem of spike sorting accuracy.…”