Space-time signal binding in recurrent neural networks with controlled elements

The possibilities of signal binding in recurrent neural networks with controlled elements are investigated. It is shown that a variety of dynamic space-time structures with new associative properties can be formed in the framework of such networks. A comparative analysis of the properties of linear, spiral single-level and multilevel structures of recurrent neural networks is carried out. Special attention is paid to the possibilities of controlling the associative-spatial interaction of signals in recurrent neural networks. The models of impulse neurons interaction are refined. The results of modeling of associative-spatial signal binding in two-layer recurrent neural networks with different logical structures of the layers are presented.