Pii: S0893-6080(97)00016-6

Hunrun episodic memory provides a seemingly unlimited storage for everyday experiences, and a retrieval system that allows us to access the experiences with partial activation of their components. The system is believed to consist of afast, temporary storage in the hippocampus,and a slow, long-term storage within the neocortex. Thispaper presents a neural network model of the hippocampal episodic memory inspired by Darnusio’s idea of Convergence Zones. The model consists of a layer of perceptual feature maps and a binding layer. A perceptual feature pattern is coarse coded in the binding layer, and stored on the weights between layers. A partial activation of the stored features activates the bindingpattern, which in turn reactivates the entire storedpattem. For manyconfigurationsof the model, a theoretical lower boundfor the memory capacity can be derived, and it can bean order of magnitudeor higher than the number of all units in the model, and several orders of magnitude higher than the number of binding-layer units. Computational simulationsfirther indicate that the average capacity is an order of magnitude larger than the theoretical lower bound, and making the connectivity between layers sparser causes an even further increase in capacity. Simulationsalso show that ifntore descriptive bindingpatterns are used, the errors tend to be rnoreplausible (patterns are confused with other similar patters), with a slight cost in capacity. The convergence-zone episodic memory therefore accounts for the immediate storage and associative retrieval capability and large capacity of the hippocampal memory, and shows why the memory encoding areas can be much smaller than the perceptual maps, consist of rather coarse computational units, and are only sparsely connected to the perceptual maps. 01997 Elsevier Science Ltd. Keywords-Convergence zones,Episodicmemory, Associative memory, Long-termmemory, Content-addressable memory, Memorycapacity, Retrieval errors,Hippocarnpus.