Rules of forming receptive fields of formal neurons during unsupervised learning processes

In the paper two kinds of unsupervised learning processes occurring in formal neurons are analysed. The relationship between these processes and the supervised ones is discussed too. In the first case of unsupervised learning processes the neuron is considered as a filter that passes signals most frequently occurring in the learning sequence {xn]}. In the second case it is considered as a detector of rareness which, after a finite number of steps operates as a filter passing only signals which rarely occur in the learning sequence {xn]}. These two approaches result in different types of receptive fields of formal neurons. On the basis of the results obtained, it is possible to advance a hypothesis on the role of neurons with various types of receptive fields in information processing by a complex neuronal network.