A coarse-coding regulatory model that facilitates neural heterogeneity through a morphogenetic process is presented. The model demonstrates cellular and tissue extensibility through ontogeny, resulting in the emergence of neural heterogeneity, use of gated memory and multistate functionality in a Artificial Neural Tissue framework. In each neuron, multiple networks of proteins compete and cooperate for representation through a coarse-coding regulatory scheme. Intracellular competition and cooperation is found to better facilitate evolutionary adaptability and result in simpler solutions than does the use of homogeneous binary neurons. The emergent use of gated memory functions within this cell model is found to be more effective than recurrent architectures for memory-dependent variants of the unlabeled sign-following robotic task.