In the present article we will consider a class of associative machines with dynamic structure where the entrance signal exerts direct impact on the mechanism of association of output signals of experts. At the same time we are interested in such group of expert decisions at which separate expert responses unite not linearly through hierarchically organized lock networks. Hierarchical mixture of opinions of experts, along with simple mixture are examples of modular networks: neural network of a module if the calculations executed by it can be distributed on several subsystems processing different entrance signals and not crossed in the work. Output signals of these subsystems unite the integrative module which exit does not possess feedback with subsystems. In fact, the integrative module makes the decision as output signals of subsystems are grouped in the general output signal of system, and identifies what examples are samples for training of concrete modules. The most general definition of modular neural network: any set of algorithms of data processing, including algorithms of the artificial neural networks grouped for the solution of some uniform task. Automatically determine the class of associative machines with dynamic structure where the entrance signal exerts direct impact on the mechanism of association of output signals of experts, at the same time group of expert decisions at which separate expert responses unite not linearly through hierarchically organized lock networks is considered.