Causal pattern inference from neural spike train data

Electrophysiological recordings are a valuable tool for neuroscience in order to monitor the activity of multiple or even single neurons. Significant insights into the nervous system have been gained by analyses of resulting data; in particular, many findings were gained from spike trains whose correlations can give valuable indications about neural interplay. But detecting, specifying, and representing neural interactions is mathematically challenging. Further, recent advances of recording techniques led to an increase in volume of collected data, which often poses additional computational problems. These developments call for new, improved methods in order to extract crucial information. The matter of this thesis is twofold: It presents a novel method for the analysis of neural spike train data, as well as a generic framework in order to assess the new and related techniques. The new computational method, the Snap Shot Score, can be used to inspect spike trains with respect to temporal dependencies, which are visualised as an information flow network. These networks can specify the relationships in the data, indicate changes in dependencies, and point to causal interactions. The Snap Shot Score is demonstrated to reveal plausible networks both in a variety of simulations and for real data, which indicate its value for understanding neural dynamics. Additional to the Snap Shot Score, a neural simulation framework is suggested, which facilitates the assessment of neural network inference techniques in a highly automated fashion. Due to a new formal concept to rate learned networks, the framework can be used to test techniques under partial observability conditions. In the presence of hidden units quantification of results has been a tedious task that had to be done by hand, but which can now be automated. Thereby high throughput assessments become possible, which facilitate a comprehensive simulation-based characterisation of new methods