Multielectrode-Arrays (MEA) allow the analysis of in-vitro neuronal networks with a high spatiotemporal resolution [Jon11]. For analyzing the signals recorded from a MEA and possible interneuronal connections, the spike positions are to be identified. In this thesis, it is assumed that the neuron-electrode interface and the electrode are characterized by a Linear-Time Invariant (LTI) system. The original neuronal signal passes through this system and is convoluted with an unknown transfer function. This alters the signal and will make spike detection more difficult. Hence, the original signal is to be reconstructed from the convoluted signal. The transfer function of the LTI system is unknown. Therefore, this process is called Blind Deconvolution. In this thesis, the Cepstrum of Bispectrum (CoB) algorithmn for blind signal reconstruction introduced by Shahid and others [SW08] is explained and implemented. This algorithm reconstructs the original signal from the convoluted signal. The reconstructed signal is used for spike detection afterwards. A simulated data set is developed. Therefor a model for the neuron-electrode interface and the electrode itself according to [Mar04] are implemented. For evaluation, this data set as well as a second set of simulated data [Qui04] and a real neuronal dataset are used. A Non-Linear Energy Operator (NEO) with and without CoB preprocessing and a wavelet detector are used for spike detection. The results of these three algorithmns are evaluated and compared. In case the data has passed a LTI system before, the results using CoB algorithmn are significantly better than results obtained without signal recontruction. For simulated data which has not passed an LTI-System as well as for the real dataset, this improvement is not observed. This leads to the conclusion that the model does not apply to real data which has been used. An examination of more real data sets and, if necessary, adjustment of the model are prospective tasks. The wavelet algorithm has a higher computational complexity than NEO algorithm without CoB preprocessing. Nevertheless, the average results achieved with wavelet detection are not significantly better than results produced by NEO. The three algorithmns possess an error between true spike positions and spike positions calculated by the three detectors. This error is dynamic and depends on the length of a single spike in samples. When using this computed spike positions for further analysis, this error must be taken into consideration.