To unveil the intricate structure of neuronal networks is one of the major goals of neuroscience. In most cases it is still unclear how structural features and network activity interact and determine one another. Neuroanatomists have been tracing neuronal connectivity patterns with increasing precision since the days of Ramón y Cajal, while optical imaging methods and advanced electrophysiological techniques allow for more and more insights into the activity dynamics under various circumstances, e.g. in acute slices and even in awake behaving animals. Observed activity patterns, such as spike trains or local field potentials, will depend on the structure of the system itself, but also on features of external inputs. So what can we learn about the underlying neuronal network by observing activity dynamics? And is it possible to dissect activation patterns due to specific stimulus features from those that arise mainly due to the network structure alone?

In my talk I will take two different approaches to address these questions from a theoretical neuroscience point of view. In the main part of the talk I will focus on correlations between neurons arising from their coupling structure in a statistical framework. I will show how the distribution of correlation coefficients of inputs and spikes estimated from randomly chosen cell pairs can provide information about the network topology. In the last part I will outline an approach to directly assess the detailed coupling matrix of neuronal networks from their spike patterns by providing a set of stimuli and analyzing the network response.