Real-time stabilization of neurons into clusters

Abstract

Deep brain stimulation (DBS) is a widespread method of combating tremors associated with Parkinson's disease, but whose mechanisms are not fully understood. One hypothesis, supported experimentally, is that some symptoms of Parkinson's are associated with pathological synchronization of neurons in the basal ganglia. For this reason, there has been interest in recent years in finding efficient ways to desynchronize neurons that are both fast-acting and low-power. Recent results on coordinated reset and periodically forced oscillators suggest that forming distinct clusters of neurons may prove to be more effective than achieving complete desynchronization by promoting plasticity effects that might persist after stimulation is turned off. Existing proposed methods for achieving clustering frequently require either multiple input sources or precomputing the control signal. We propose here a control strategy for clustering, based on an analysis of the reduced phase model for a set of identical neurons, that allows for real-time, single-input control of a population of neurons with low-amplitude, low total energy signals.