Abstract- Human 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. This paper presents a computationalmodel of episodic memory inspired by Damasio'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 stored pattern. A worst-case analysis shows that with realistic-size layers, the memory capacity of the model is severaltimes larger than the numberof units in the model, and could account for the large capacity of human episodic memory.