Neural Spike Sorting Using Unsupervised Adversarial Learning

Abstract

During the analysis of neurobiological signals acquired during neurosurgical procedures such as Deep Brain Stimulation, one focuses mainly on the signal’s background and spiking activity. Spikes, the electrical impulses generated by neuron cells, are events lasting about 1.5 ms with voltage typically well below 200 V. As the shape of spike derives from the physical construction of the neuron’s cell membrane and its distance from the recording point, one can infer that spikes of different shapes were generated by different neuron cells. This information can be beneficial during the analysis of the acquired signals, and the procedure of grouping spikes according to their shapes, is called spike sorting. Typically in this procedure, there are defined various attributes characterizing spike shape, and subsequently, the standard clustering method is used to produce groups of spikes of different shapes. This may cause some difficulties that arise both from the definition of good discriminating attributes as well as from the fact that one does not know beforehand how many clusters of shapes to expect. The approach presented in this paper is different, using the fully unsupervised and explainable AI, it generates groups of spike shapes in a fully automatic way. No attributes have to be defined nor the expected number of shape clusters provided. Spike waveforms are fed into the autoencoder that has defined additional constraints on its latent layers. After training, data obtained from those layers can be used for the purpose of spike shape clustering. Beside shape class, one also receives information on how much given spike shape differs from the mean shape of its class.