Scaling laws of associative memory retrieval

Sandro Romani; Itai Pinkoviezky; Alon Rubin; Misha Tsodyks

doi:10.1162/NECO_a_00499

Scaling laws of associative memory retrieval

Sandro Romani, Itai Pinkoviezky, Alon Rubin, Misha Tsodyks

Department of Brain Sciences

Weizmann Institute of Science

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	2523-2544
Number of pages	22
Journal	Neural Computation
Volume	25
Issue number	10
DOIs	https://doi.org/10.1162/NECO_a_00499
State	Published - 2013

All Science Journal Classification (ASJC) codes

Arts and Humanities (miscellaneous)
Cognitive Neuroscience

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10.1162/NECO_a_00499

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title = "Scaling laws of associative memory retrieval",

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.",

author = "Sandro Romani and Itai Pinkoviezky and Alon Rubin and Misha Tsodyks",

note = "We are indebted to Bennet Murdock for the data used in Figure 4, and to D. J. Murray for reprints of his papers. M.T. thanks Michael Kahana and Adi Shamir for fruitful discussions of the work. We are grateful to Dov Sagi, Stefano Fusi, and Omri Barak for comments on an earlier version of the manuscript. This work is supported by the Israeli Science Foundation. S.R. is supported by a Human Frontier Science Program long-term fellowship. We declare no conflicts of interest. S.R. and M.T. wish to dedicate this work to the memory of Daniel Amit, mentor and friend.",

year = "2013",

doi = "10.1162/NECO_a_00499",

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N1 - We are indebted to Bennet Murdock for the data used in Figure 4, and to D. J. Murray for reprints of his papers. M.T. thanks Michael Kahana and Adi Shamir for fruitful discussions of the work. We are grateful to Dov Sagi, Stefano Fusi, and Omri Barak for comments on an earlier version of the manuscript. This work is supported by the Israeli Science Foundation. S.R. is supported by a Human Frontier Science Program long-term fellowship. We declare no conflicts of interest. S.R. and M.T. wish to dedicate this work to the memory of Daniel Amit, mentor and friend.

PY - 2013

Y1 - 2013

N2 - 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.

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DO - 10.1162/NECO_a_00499

M3 - مقالة

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Scaling laws of associative memory retrieval

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