Parallel Algorithms for Neuronal Spike Sorting

Abstract

Neurons communicate through electrophysiological signals, which may be recorded using electrodes inserted into living tissue.When a neuron emits a signal, it is referred to as a spike, and an electrode can detect these from multiple neurons.Neuronal spike sorting is the process of classifying the spike activity based on which neuron each spike signal is emitted from.Advances in technology have introduced better recording equipment, which allows the recording of many neurons at the same time.However, clustering software is lagging behind.Currently, spike sorting is often performed semi-manually by experts, with computer assistance, in a drastically reduced feature space.This makes the clustering prone to subjectivity.Automating the process will make classification much more efficient, and may produce better results.Implementing accurate and efficient spike sorting algorithms is therefore increasingly important.We have developed parallel implementations of superparamagnetic clustering, a novel clustering algorithm, as well as k-means clustering, serving as a useful comparison.Several feature extraction methods have been implemented to test various input distributions with the clustering algorithms. To assess the quality of the results from the algorithms, we have also implemented different cluster quality algorithms.Our implementations have been benchmarked, and found to scale well both with increased problem sizes and when run on multi-core processors.The results from our cluster quality measurements are inconclusive, and we identify this as a problem related to the subjectivity in the manually classified datasets.To better assess the utility of the algorithms, comparisons with intracellular recordings should be performed.