Title: Measuring edge importance for random processes on graphs
Speaker: Deena Schmidt (Postdoctoral Scholar, Case Western Reserve University)
Abstract: Many neural systems can be represented as a stochastic process on a graph. Recently, Schmandt and Galan introduced the “stochastic shielding approximation” as a fast, accurate method for generating approximate sample paths from a stochastic process in which only a subset of states are observable. We conducted a rigorous analysis of this stochastic shielding heuristic, deriving a new quantitative measure of the contribution of individual edges in the graph to the accuracy of the approximation. In this talk, I will discuss our analysis and our extension of this method for a broad class of random graph models and for the Hodgkin-Huxley ion channel model. I will show how these results shed new light on the contributions of different ion channel transitions to the variability of neural systems. This approach can be applied to a variety of biological networks and has led to many challenging questions.