Convolutional networks can be defined by a set of layers being respectively made up by a two-dimensional lattice of neurons. Each layer--with the exception of the last layer--represents a source layer for respectively following target layer. A plurality of neurons of a source layer called a source sub-area respectively share the identical connectivity weight matrix type. Each connectivity weight matrix type is represented by a scalar product of an encoding filter and a decoding filter. For each source layer a source reconstruction image is calculated on the basis of the corresponding encoding filters and the activities of the corresponding source sub-area. For each connectivity weight matrix type, each target sub-area and each target layer the input of the target layer is calculated as a convolution of the source reconstruction image and the decoding filter. For each target layer the activities are calculated by using the non-linear local response function of the neurons of the target layer and the calculated input of the target layer.