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.

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

  • 38,392
    views
  • 3,666
    downloads
  • 231
    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. Neuroscience
    Roshani Nhuchhen Pradhan, Craig Montell, Youngseok Lee
    Research Article

    The question as to whether animals taste cholesterol taste is not resolved. This study investigates whether the fruit fly, Drosophila melanogaster, is capable of detecting cholesterol through their gustatory system. We found that flies are indifferent to low levels of cholesterol and avoid higher levels. The avoidance is mediated by gustatory receptor neurons (GRNs), demonstrating that flies can taste cholesterol. The cholesterol-responsive GRNs comprise a subset that also responds to bitter substances. Cholesterol detection depends on five ionotropic receptor (IR) family members, and disrupting any of these genes impairs the flies' ability to avoid cholesterol. Ectopic expressions of these IRs in GRNs reveals two classes of cholesterol receptors, each with three shared IRs and one unique subunit. Additionally, expressing cholesterol receptors in sugar-responsive GRNs confers attraction to cholesterol. This study reveals that flies can taste cholesterol, and that the detection depends on IRs in GRNs.

    1. Genetics and Genomics
    2. Neuroscience
    Tanya Wolff, Mark Eddison ... Gerald M Rubin
    Research Article

    The central complex (CX) plays a key role in many higher-order functions of the insect brain including navigation and activity regulation. Genetic tools for manipulating individual cell types, and knowledge of what neurotransmitters and neuromodulators they express, will be required to gain mechanistic understanding of how these functions are implemented. We generated and characterized split-GAL4 driver lines that express in individual or small subsets of about half of CX cell types. We surveyed neuropeptide and neuropeptide receptor expression in the central brain using fluorescent in situ hybridization. About half of the neuropeptides we examined were expressed in only a few cells, while the rest were expressed in dozens to hundreds of cells. Neuropeptide receptors were expressed more broadly and at lower levels. Using our GAL4 drivers to mark individual cell types, we found that 51 of the 85 CX cell types we examined expressed at least one neuropeptide and 21 expressed multiple neuropeptides. Surprisingly, all co-expressed a small molecule neurotransmitter. Finally, we used our driver lines to identify CX cell types whose activation affects sleep, and identified other central brain cell types that link the circadian clock to the CX. The well-characterized genetic tools and information on neuropeptide and neurotransmitter expression we provide should enhance studies of the CX.