Dean Freestone: April 22nd

Title: Data-driven mesoscopic computational modeling Abstract: The talk will focus on two types of data-driven mesoscopic modeling. The first is known as neural field modeling, and the second neural mass modeling. It has been demonstrated that it is possible to estimate fast changing state variables (population firing rates or mean membrane potentials) and slowly changing parameters (connectivity strengths, time constants, and firing thresholds) from real electrophysiological data. The ability accurately estimate such quantities provides an opportunity to visualize aspects of brain function that is normally hidden when performing in-vivo studies. The talk will provide an update on efforts to improve and simplify estimation algorithms such that these ideas are more useful to the wider community.