  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 13 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.
