Using information theory to analyze statistically causal influences in networks of simultaneously recorded neurons

In a number of scientific disciplines, researchers are able to observe multiple processes simultaneously, such as stock prices, ecological networks, or neuronal activity, but do not have robust methods to identify how the processes causally influence each other. This talk will examine some recent work in estimating directed information, a quantity shown to characterize statistically causal influences between stochastic processes. A provably good parametric estimation scheme will be shown, and results from an analysis on communication patterns of simultaneously recorded neurons in a primate study will be discussed.