Scaling laws of associative memory retrieval

Authors:
Sandro Romani;Itai Pinkoviezky;Alon Rubin;Misha Tsodyks
Affiliations:
-;-;-;-
Venue:
Neural Computation
Year:
2013

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Abstract

Most people have great difficulty in recalling unrelated items. For example, in free recall experiments, lists of more than a few randomly selected words cannot be accurately repeated. Here we introduce a phenomenological model of memory retrieval inspired by theories of neuronal population coding of information. The model predicts nontrivial scaling behaviors for the mean and standard deviation of the number of recalled words for lists of increasing length. Our results suggest that associative information retrieval is a dominating factor that limits the number of recalled items.