Efficient micro-electrode recording modeling using a filtered point process
In this paper we present an eﬃcient model of the neuronal potentials recorded by a deep brain stimulation microelectrode (DBS MER) in the subthalamic nucleus. It is shown that a computationally eﬃcient ﬁltered point process consisting of 10,000 neurons, including extracellular ﬁltering closely matches recordings from 14 Parkinson’s disease patients. The recordings were compared using their voltage amplitude distributions, power spectral density estimates and phase synchrony. It was found that interspike interval times modeled using a Weibull distribution with a shape parameter of 0.8, slightly non-Poisosnian, gave the best ﬁt of the simulations to patient recordings. These results indicate that part of the ‘background activity’ present in an DBS MER can be considered to be a very local ﬁeld potential due to the surrounding neuronal activity.Therefore, the statistics of the interspike interval times modify the structure of the background activity.