Fundamental bound on the persistence and capacity of short-term memory stored as graded persistent activity

  1. Onur Ozan Koyluoglu  Is a corresponding author
  2. Yoni Pertzov
  3. Sanjay Manohar
  4. Masud Husain
  5. Ila R Fiete
  1. University of California, United States
  2. Hebrew University of Jerusalem, Israel
  3. University of Oxford, United Kingdom
  4. The University of Texas at Austin, United States

Abstract

It is widely believed that persistent neural activity underlies short-term memory. Yet, as we show, temporal degradation in such networks behaves differently from human short-term memory performance. We build a more general framework where the memory is viewed as a problem of passing information through noisy channels that represent analog persistent activity networks. Rather than directly storing information, such memory networks might hold information encoded to achieve robustness against noise. We derive a fundamental lower-bound on memory recall precision, which declines with storage duration and number of stored items. We show that human performance, though inconsistent with models involving direct (uncoded) storage in persistent activity networks, can be well-fit by the theoretical bound. This finding is consistent with the view that if the brain stores information in patterns of persistent activity, it might use codes that minimize the effects of noise, motivating the search for such codes in the brain.

Article and author information

Author details

  1. Onur Ozan Koyluoglu

    Department of Electrical and Computer Science, University of California, Berkeley, United States
    For correspondence
    ozan.koyluoglu@berkeley.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8512-4755
  2. Yoni Pertzov

    Department of Psychology, Hebrew University of Jerusalem, Jerusalem, Israel
    Competing interests
    The authors declare that no competing interests exist.
  3. Sanjay Manohar

    Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0735-4349
  4. Masud Husain

    Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Ila R Fiete

    Center for Learning and Memory, The University of Texas at Austin, Austin, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Science Foundation (IIS-1464349)

  • Onur Ozan Koyluoglu

Israeli Science Foundation (1747/14)

  • Yoni Pertzov

National Institute for Health Research (Oxford Biomedical Centre)

  • Masud Husain

Wellcome Trust

  • Masud Husain

National Science Foundation (IIS-1148973)

  • Ila R Fiete

Simons Foundation

  • Ila R Fiete

Howard Hughes Medical Institute (Faculty Scholar Award)

  • Ila R Fiete

MRC Clinician Scientist Fellowship (MR/P00878X)

  • Sanjay Manohar

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: The study reported here conform to the Declaration of Helsinki and all procedures were approved by the ethics committee of the National Hospital for Neurology and Neurosurgery (NHNN) prior to the study commencing. Research Ethics Committee number (ERC) 04/Q0406/60. Personal information about individuals was password protected and saved in compliance to the Data Protection Act 1998 (DPA).

Copyright

© 2017, Koyluoglu et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,551
    views
  • 395
    downloads
  • 30
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Onur Ozan Koyluoglu
  2. Yoni Pertzov
  3. Sanjay Manohar
  4. Masud Husain
  5. Ila R Fiete
(2017)
Fundamental bound on the persistence and capacity of short-term memory stored as graded persistent activity
eLife 6:e22225.
https://doi.org/10.7554/eLife.22225

Share this article

https://doi.org/10.7554/eLife.22225

Further reading

    1. Biochemistry and Chemical Biology
    2. Computational and Systems Biology
    Shinichi Kawaguchi, Xin Xu ... Toshie Kai
    Research Article

    Protein–protein interactions are fundamental to understanding the molecular functions and regulation of proteins. Despite the availability of extensive databases, many interactions remain uncharacterized due to the labor-intensive nature of experimental validation. In this study, we utilized the AlphaFold2 program to predict interactions among proteins localized in the nuage, a germline-specific non-membrane organelle essential for piRNA biogenesis in Drosophila. We screened 20 nuage proteins for 1:1 interactions and predicted dimer structures. Among these, five represented novel interaction candidates. Three pairs, including Spn-E_Squ, were verified by co-immunoprecipitation. Disruption of the salt bridges at the Spn-E_Squ interface confirmed their functional importance, underscoring the predictive model’s accuracy. We extended our analysis to include interactions between three representative nuage components—Vas, Squ, and Tej—and approximately 430 oogenesis-related proteins. Co-immunoprecipitation verified interactions for three pairs: Mei-W68_Squ, CSN3_Squ, and Pka-C1_Tej. Furthermore, we screened the majority of Drosophila proteins (~12,000) for potential interaction with the Piwi protein, a central player in the piRNA pathway, identifying 164 pairs as potential binding partners. This in silico approach not only efficiently identifies potential interaction partners but also significantly bridges the gap by facilitating the integration of bioinformatics and experimental biology.

    1. Computational and Systems Biology
    2. Neuroscience
    Brian DePasquale, Carlos D Brody, Jonathan W Pillow
    Research Article Updated

    Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here, we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions—the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS)—while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal’s choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.