The Capacity of Convergence-Zone Episodic Memory

The Capacity of Convergence-Zone Episodic Memory

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by Mark Moll Risto , Mark Moll , Risto Miikkulainen , Jonathan Abbey

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Datum	Value	Source
TITLE	The Capacity of Convergence-Zone Episodic Memory	SVM HeaderParse 0.1
AUTHOR NAME	Mark Moll Risto	user correction - Legacy Corrections
AUTHOR NAME	Mark Moll	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Sciences, The University of Texas at Austin	user correction - Legacy Corrections
AUTHOR NAME	Risto Miikkulainen	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Sciences, The University of Texas at Austin	user correction - Legacy Corrections
AUTHOR NAME	Jonathan Abbey	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Sciences, The University of Texas at Austin	user correction - Legacy Corrections
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 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. I. Introduction Human episodic memory is characterized by an extremely high capacity. New memories are formed every few seconds, and many of those persist...	user correction - Legacy Corrections
CITATIONS	16 found	ParsCit 1.0