Modeling thalamocortical relay neuron, Parkinsonian network and deep brain stimulation
Tuesday, April 23, 2013 - 3:15pm - 4:30pm
Drexel University, Department of Mathematics
We study a data-driven model of thalamocortical (TC) relay neuron to examine the TC relay responses to an excitatory input train, under inhibitory signals. We first incorporate recording data as inhibitory signals to the TC model to investigate the mechanism underlying deep brain stimulation (DBS) which has been proven clinically effective to relieve motor symptoms for Parkinsonian patients. Then we explore the closed-loop stimulation paradigm using a parkinsonian network model of the basal-ganglia thalamocortical circuit. Our computational results show that the type of stimulation, based on a filtered version of the local field potential, significantly improves the fidelity of thalamocortical (TC) relay. To further understand the different scenarios of TC relay responses, we analyze the entrainment of the TC neuron to periodic signals that alternate between ‘on’ and ‘off’, respectively. By exploiting invariant sets of the system and their associated invariant fiber bundles that foliate the phase space, we reduce the 3D TC model to a 2D map. Based on this map, we reproduce the possible scenarios of TC relay responses observed in the data-driven model.