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The main result of this thesis is the development of a novel connectivityestimation method, called Total Spiking Probability Edges (TSPE). Based oncross-correlation and edge filtering at different time scales this method isproposed and the theoretical framework is outlined in this work. TSPE enablesthe classification between inhibitory and excitatory connections by usingrecorded action potentials. To compare this method learning about state of theart algorithms to estimate connectivity is necessary. After a research,promising algorithms are implemented and evaluated for further research topics,among others in the biomems lab of UAS Aschaffenburg. To evaluate thesealgorithms in silico networks are used, because of their known connectivity.This makes it possible to validate the correctness of our algorithm results.Therefore, a biophysically representative neuronal network simulation is neededfirst. Datasets were simulated in different ways and analysed in order todevelop an evaluation framework. After a successful evaluation with in siliconetworks, in vitro experiments and their analyses complete this project.

Connectivity estimation of high dimensional data recorded from neuronal cells