The Capacity of Convergence-Zone Episodic Memory

Abstract

Abstract- Human episodic memory pro-vides a seemingly unlimited storage for ev-eryday experiences, and a retrieval system that allows us to access the experiences with partial activation of their components. This paper presents a computational model of epi-sodic memory inspired by Damasio's idea of Convergence Zones. The model consists of a layer of perceptual feature maps and a bind-ing layer. A perceptual feature pattern is coarse coded in the binding layer, and stored on the weights between layers. A partial ac-tivation of the stored features activates the binding pattern which in turn reactivates the entire stored pattern. A worst-case analy-sis shows that with realistic-size layers, the memory capacity of the model is several times larger than the number of units in the model, and could account for the large capacity of human episodic memory. I.