In this paper we studied the dynamic behavior of neural networks consisting of discrete populations of formal neurons. Neurons are assumed to have the same probability of connection with other neurons carrying the same type of chemical marker and divided in such a way to neural subpopulations due to the chemical affinity of markers carried by the individual cells. The dynamics of isolated networks as well as the ones that receive sustained external inputs show multiple stability and hysteresis phenomena which lead to multiple memory domains.