Using brain-computer interfaces, one can both read data from and transmit them to the brain. However, these data are only a set of sensor system signals and not the knowledge or experience. Neural networks are a basis for cognitive activity and can simulate processes similar to learning in vitro. In this work we tested the hypothesis of a neural network’s ability to learn by detecting deviations from its stereotypical activity and modifying them in a way that allows it to get rid of external electrical stimulation. Spontaneous activity of several neuronal cultures in vitro was analyzed by clustering method. The results showed that activity of untrained cultures remained stable for a long time, and external electrical stimulation led to switching between various spontaneous activity patterns.