A functional model based on single unit recordings from Parkinsonian brain

Abstract

Artificial neuronal clusters are arranged and linearly filtered to generate signals similar to those recorded from the mid-brain regions of patients with Parkinson's disease. The goal of the research is to construct a model containing information about several aspects of recording from a neuronal cluster in-vivo. In particular, these include: number (or size) of significant neurons in the cluster, effective filtering characteristics of brain tissue between the recording electrode and each neuron, and spiking frequency of each neuron. Furthermore, models of varying size are generated based on single-unit recordings from the human brain. Results of simulations are presented and compared.