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 computational model 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 binding pattern 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 several times larger than the number of units in the model, and could account for the large capacity of human episodic memory.
|Title of host publication||Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)|
|Place of Publication||Piscataway, NJ|
|Publication status||Published - 1994|
|Event||1994 IEEE International Conference on Neural Networks, ICNN 1994 - Orlando, United States|
Duration: 27 Jun 1994 → 2 Jul 1994
|Conference||1994 IEEE International Conference on Neural Networks, ICNN 1994|
|Period||27/06/94 → 2/07/94|