Hebbian learning and temporary storage in the convergence-zone model of episodic memory

The convergence-zone model shows how sparse, random memory patterns can lea d to one-shot storage and high capacity in the hippocampal component of the episodic memory system. This paper presents a biologically more realistic version of the model, with continuously weighted connections and storage th rough Hebbian learning and normalization. In contrast to the gradual weight adaptation in many neural network models, episodic memory turns out to req uire high learning rates. Normalization allows earlier patterns to be overw ritten, introducing time-dependent forgetting similar to the hippocampus. ( C) 2000 Published by Elsevier Science B.V. All rights reserved.