A Parametric Simulation of Neuronal Noise from Microelectrode Recordings

Abstract

In this paper we present an efficient model of microelectrode recordings (MER) from the subthalamic nucleus acquired during deep brain stimulation (DBS) surgery. The model shows how changes in the 'noise' relate to the neuronal spike time statistics. A top-down approach is used with analysis-by-synthesis of the MER power spectra. The model is built around a sum of filtered point processes consisting of thousands of neurons and including extracellular filtering. The quality of the model is demonstrated through comparisons to recordings from eight individuals (both hemispheres in six) who have undergone DBS implantation for the treatment of Parkinson's disease. The simulated recordings were compared using their voltage amplitude distributions, power spectral density estimates and phase synchrony while varying only one free parameter (the shape of the inter-spike interval distribution). Through this simple model, we show that the noise present in a DBS MER contains properties that match that of patient recordings when a Weibull distribution with shape parameter of 0.8 is used for the inter-spike interval.