Nanoconnectomic upper bound on the variability of synaptic plasticity

  1. Thomas M Bartol  Is a corresponding author
  2. Cailey Bromer
  3. Justin P Kinney
  4. Micheal A Chirillo
  5. Jennifer N Bourne
  6. Kristen M Harris
  7. Terrence J Sejnowski
  1. Howard Hughes Medical Institute, Salk Institute for Biological Studies, United States
  2. Massachusetts Institute of Technology, United States
  3. The University of Texas at Austin, United States
  4. University of Colorado Denver, United States

Abstract

Information in a computer is quantified by the number of bits that can be stored and recovered. An important question about the brain is how much information can be stored at a synapse through synaptic plasticity, which depends on the history of probabilistic synaptic activity. The strong correlation between size and efficacy of a synapse allowed us to estimate the variability of synaptic plasticity. In an EM reconstruction of hippocampal neuropil we found single axons making two or more synaptic contacts onto the same dendrites, having shared histories of presynaptic and postsynaptic activity. The spine heads and neck diameters, but not neck lengths, of these pairs were nearly identical in size. We found that there is a minimum of 26 distinguishable synaptic strengths, corresponding to storing 4.7 bits of information at each synapse. Because of stochastic variability of synaptic activation the observed precision requires averaging activity over several minutes.

Article and author information

Author details

  1. Thomas M Bartol

    Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, United States
    For correspondence
    bartol@salk.edu
    Competing interests
    The authors declare that no competing interests exist.
  2. Cailey Bromer

    Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Justin P Kinney

    Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Micheal A Chirillo

    Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Jennifer N Bourne

    University of Colorado Denver, Denver, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Kristen M Harris

    Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Terrence J Sejnowski

    Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.

Reviewing Editor

  1. Sacha B Nelson, Brandeis University, United States

Version history

  1. Received: August 11, 2015
  2. Accepted: November 29, 2015
  3. Accepted Manuscript published: November 30, 2015 (version 1)
  4. Version of Record published: January 20, 2016 (version 2)

Copyright

© 2015, Bartol 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

  • 36,861
    views
  • 3,499
    downloads
  • 212
    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. Thomas M Bartol
  2. Cailey Bromer
  3. Justin P Kinney
  4. Micheal A Chirillo
  5. Jennifer N Bourne
  6. Kristen M Harris
  7. Terrence J Sejnowski
(2015)
Nanoconnectomic upper bound on the variability of synaptic plasticity
eLife 4:e10778.
https://doi.org/10.7554/eLife.10778

Share this article

https://doi.org/10.7554/eLife.10778

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Sara Ibañez, Nilapratim Sengupta ... Christina M Weaver
    Research Article

    Normal aging leads to myelin alterations in the rhesus monkey dorsolateral prefrontal cortex (dlPFC), which are positively correlated with degree of cognitive impairment. It is hypothesized that remyelination with shorter and thinner myelin sheaths partially compensates for myelin degradation, but computational modeling has not yet explored these two phenomena together systematically. Here, we used a two-pronged modeling approach to determine how age-related myelin changes affect a core cognitive function: spatial working memory. First, we built a multicompartment pyramidal neuron model fit to monkey dlPFC empirical data, with an axon including myelinated segments having paranodes, juxtaparanodes, internodes, and tight junctions. This model was used to quantify conduction velocity (CV) changes and action potential (AP) failures after demyelination and subsequent remyelination. Next, we incorporated the single neuron results into a spiking neural network model of working memory. While complete remyelination nearly recovered axonal transmission and network function to unperturbed levels, our models predict that biologically plausible levels of myelin dystrophy, if uncompensated by other factors, can account for substantial working memory impairment with aging. The present computational study unites empirical data from ultrastructure up to behavior during normal aging, and has broader implications for many demyelinating conditions, such as multiple sclerosis or schizophrenia.

    1. Neuroscience
    Nicholas GW Kennedy, Jessica C Lee ... Nathan M Holmes
    Research Article

    How is new information organized in memory? According to latent state theories, this is determined by the level of surprise, or prediction error, generated by the new information: a small prediction error leads to the updating of existing memory, large prediction error leads to encoding of a new memory. We tested this idea using a protocol in which rats were first conditioned to fear a stimulus paired with shock. The stimulus was then gradually extinguished by progressively reducing the shock intensity until the stimulus was presented alone. Consistent with latent state theories, this gradual extinction protocol (small prediction errors) was better than standard extinction (large prediction errors) in producing long-term suppression of fear responses, and the benefit of gradual extinction was due to updating of the conditioning memory with information about extinction. Thus, prediction error determines how new information is organized in memory, and latent state theories adequately describe the ways in which this occurs.