Our environment is full of visual, tactile and acoustic impressions. At any time stimuli act on the brain. To process these diverse stimuli, the nerve cells of the brain form functional units. These so-called neural networks enable efficient routing, processing and storage of sensory impressions, so that they form the basis of human learning. In order to understand the structure and communication architecture of these highly complex networks, it is necessary to examine the signals emitted by the neurons. Firstly, this bachelor thesis compares methods for spike detection. Furthermore, a program is developed in LabVIEW, which allows to record spikes from a variety of channels while detecting and saving spikes simultaneously. The bases for the detection of spikes are algorithms by threshold analysis and continuous wavelet transformation.