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.

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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.

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  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. Developmental Biology
    2. Neuroscience
    Emily L Heckman, Chris Q Doe
    Research Advance

    The organization of neural circuits determines nervous system function. Variability can arise during neural circuit development (e.g. neurite morphology, axon/dendrite position). To ensure robust nervous system function, mechanisms must exist to accommodate variation in neurite positioning during circuit formation. Previously we developed a model system in the Drosophila ventral nerve cord to conditionally induce positional variability of a proprioceptive sensory axon terminal, and used this model to show that when we altered the presynaptic position of the sensory neuron, its major postsynaptic interneuron partner modified its dendritic arbor to match the presynaptic contact, resulting in functional synaptic input (Sales et al., 2019). Here we investigate the cellular mechanisms by which the interneuron dendrites detect and match variation in presynaptic partner location and input strength. We manipulate the presynaptic sensory neuron by (a) ablation; (b) silencing or activation; or (c) altering its location in the neuropil. From these experiments we conclude that there are two opposing mechanisms used to establish functional connectivity in the face of presynaptic variability: presynaptic contact stimulates dendrite outgrowth locally, whereas presynaptic activity inhibits postsynaptic dendrite outgrowth globally. These mechanisms are only active during an early larval critical period for structural plasticity. Collectively, our data provide new insights into dendrite development, identifying mechanisms that allow dendrites to flexibly respond to developmental variability in presynaptic location and input strength.

    1. Epidemiology and Global Health
    2. Neuroscience
    Lorenza Dall'Aglio, Hannah H Kim ... Henning Tiemeier
    Research Article Updated

    Background:

    Associations between attention-deficit/hyperactivity disorder (ADHD) and brain morphology have been reported, although with several inconsistencies. These may partly stem from confounding bias, which could distort associations and limit generalizability. We examined how associations between brain morphology and ADHD symptoms change with adjustments for potential confounders typically overlooked in the literature (aim 1), and for the intelligence quotient (IQ) and head motion, which are generally corrected for but play ambiguous roles (aim 2).

    Methods:

    Participants were 10-year-old children from the Adolescent Brain Cognitive Development (N = 7722) and Generation R (N = 2531) Studies. Cortical area, volume, and thickness were measured with MRI and ADHD symptoms with the Child Behavior Checklist. Surface-based cross-sectional analyses were run.

    Results:

    ADHD symptoms related to widespread cortical regions when solely adjusting for demographic factors. Additional adjustments for socioeconomic and maternal behavioral confounders (aim 1) generally attenuated associations, as cluster sizes halved and effect sizes substantially reduced. Cluster sizes further changed when including IQ and head motion (aim 2), however, we argue that adjustments might have introduced bias.

    Conclusions:

    Careful confounder selection and control can help identify more robust and specific regions of associations for ADHD symptoms, across two cohorts. We provided guidance to minimizing confounding bias in psychiatric neuroimaging.

    Funding:

    Authors are supported by an NWO-VICI grant (NWO-ZonMW: 016.VICI.170.200 to HT) for HT, LDA, SL, and the Sophia Foundation S18-20, and Erasmus University and Erasmus MC Fellowship for RLM.