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.