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

Publication 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

  • 33,638
    Page views
  • 3,200
    Downloads
  • 131
    Citations

Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.

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

Further reading

    1. Neuroscience
    Andrea Merseburg et al.
    Research Article

    De novo mutations in voltage- and ligand-gated channels have been associated with an increasing number of cases of developmental and epileptic encephalopathies, which often fail to respond to classic antiseizure medications. Here, we examine two knock-in mouse models replicating de novo sequence variations in the HCN1 voltage-gated channel gene, p.G391D and p.M153I (Hcn1G380D/+ and Hcn1M142I/+ in mouse), associated with severe drug-resistant neonatal- and childhood-onset epilepsy, respectively. Heterozygous mice from both lines displayed spontaneous generalized tonic-clonic seizures. Animals replicating the p.G391D variant had an overall more severe phenotype, with pronounced alterations in the levels and distribution of HCN1 protein, including disrupted targeting to the axon terminals of basket cell interneurons. In line with clinical reports from patients with pathogenic HCN1 sequence variations, administration of the antiepileptic Na+ channel antagonists lamotrigine and phenytoin resulted in the paradoxical induction of seizures in both mouse lines, consistent with an effect to further impair inhibitory neuron function. We also show that these variants can render HCN1 channels unresponsive to classic antagonists, indicating the need to screen mutated channels to identify novel compounds with diverse mechanism of action. Our results underscore the necessity of tailoring effective therapies for specific channel gene variants, and how strongly validated animal models may provide an invaluable tool towards reaching this objective.

    1. Neuroscience
    Danilo Menicucci et al.
    Research Article

    Sleep and plasticity are highly interrelated, as sleep slow oscillations and sleep spindles are associated with consolidation of Hebbian-based processes. However, in adult humans, visual cortical plasticity is mainly sustained by homeostatic mechanisms, for which the role of sleep is still largely unknown. Here we demonstrate that non-REM sleep stabilizes homeostatic plasticity of ocular dominance induced in adult humans by short-term monocular deprivation: the counter-intuitive and otherwise transient boost of the deprived eye was preserved at the morning awakening (>6 hours after deprivation). Subjects exhibiting a stronger boost of the deprived eye after sleep had increased sleep spindle density in frontopolar electrodes, suggesting the involvement of distributed processes. Crucially, the individual susceptibility to visual homeostatic plasticity soon after deprivation correlated with the changes in sleep slow oscillations and spindle power in occipital sites, consistent with a modulation in early occipital visual cortex.