Volker Pernice: March 27th 

Title: Correlations and connectivity in populations of neurons

Abstract: Due to their ubiquity in experiments and their importance for neural network dynamics and function, covariances between neural spike trains have been studied extensively. Their origin and their relation to the structure of the network of synaptic connections can be understood in the framework of linearly interacting point processes. This model also approximately describes the covariances in networks of leaky integrate-and-fire neurons. Because direct as well as indirect connections generate covariances, the solution to the inverse problem of inferring network structure from covariances is not unique. However, the ambiguity can partly be resolved under the assumption of a sparse network.