An unsupervised method for realtime spike sorting

A large amount of neurophysiological research is based on the study of biological neural populations. The data is gathered from extra-cellular recordings with multi-electrode arrays (MEAs). The signal is a stream containing an unknown number of neural sources superpositioned with non-stationary noise components. In order to analyze the recorded spike trains, a prior separation into its individual components is required. The increasing number of sensors on a MEA surface demand an automatic spike sorting procedure. In this article the proposed spike sorting method replaces the manual steps with artificial neural networks to enable an unsupervised separation of mixed signal components. Furthermore the artificial neural network based feature extraction allows realtime processing and ensures high flexbibility and adaptivity for unknown signals with non-stationary noise components.