Extensive neuroscience research on the hippocampus has identified its crucial role in memory formation and recall. Specifically, associative binding of the components comprising an episodic memory has been identified as one of the functions performed by the hippocampus. Based upon neuroanatomical function we have devised a computational cortical-hippocampal architecture using variants of adaptive resonance theory (ART) artificial neural networks. This computational model is capable of processing multi-modal sensory inputs and capturing qualitative memory phenomena such as auto-association and recall. Model performance is assessed both qualitatively and quantitatively. From a quantitative standpoint, we have applied the mathematics of information theory to quantify the similarity between recalled images yielded by the model and the unaltered original inputs. Thus in this paper we present a neurologically plausible computational architecture as well as a quantitative assessment of model performance.