A model for the neocortex aggregating elements from complex networks theory and distributed algorithms is considered and analyzed in an approach based on the methods applied on the field of artificial life. The model has its structural component given by a graph whose vertices correspond to neurons and whose edges correspond to synapses. Its dynamics are given by an asynchronous distributed algorithm built from local rules, executed by each vertex of the graph in order to simulate the events of action potential firing and the phenomenon of synaptic plasticity. Initially, the evolution of the synaptic weight distribution is analyzed by means of extensive simulations. This evolution leads to a result in excellent accordance to data from neuroscience, which is interpreted as a validation of the model. Afterwards, properties related to information integration and neuronal synchronization are studied and quantified. A measure which tries to characterize the efficiency of the model with respect to integrated information, in contrast to information generated in an independent manner by its components, is then proposed. The results obtained from new simulations allow for the conclusion that graphs generated according to the model developed here are efficient in this regard. Aspects related to neuronal synchronization present in the model are studied next, through the definition of indicators capable of characterizing the presence of synchronized behavior. The indicators are applied in simulations analogous to the ones previously executed, whose results demonstrate that the algorithm developed here is capable of generating dynamic behavior that leads to the occurrence of synchronization.