This paper describes a formalism for modeling neural networks based on networks of finite automata. We assume the behavior of a network, i.e., its reaction (output) to an external stimulus (input), to be represented uniquely by the spatio-temporal, dynamic activation process which occurs in the network caused by the external stimulus. For a restricted class of activation processes, we are able to determine the resultant activation process caused by simultaneous or successive stimuli from the activation processes representing the single stimuli. Thus, the reaction of the system to a complex input, consisting of a set of simultaneous or successive stimuli, can be inferred from its reactions to the single stimuli. The model was used for the construction and simulation of small networks demonstrating learning and regulating features as well as for the investigation of the behavior of large neural assemblies, which is the main topic of this paper.