A unified computational model for cortical post-synaptic plasticity

  1. Tuomo Mäki-Marttunen  Is a corresponding author
  2. Nicolangelo Iannella
  3. Andrew G Edwards
  4. Gaute T Einevoll
  5. Kim T Blackwell
  1. Simula Research Laboratory, Norway
  2. University of Oslo, Norway
  3. Norwegian University of Life Sciences, Norway
  4. George Mason University, United States

Abstract

Signalling pathways leading to post-synaptic plasticity have been examined in many types of experimental studies, but a unified picture on how multiple biochemical pathways collectively shape neocortical plasticity is missing. We built a biochemically detailed model of post-synaptic plasticity describing CaMKII, PKA, and PKC pathways and their contribution to synaptic potentiation or depression. We developed a statistical AMPA-receptor-tetramer model, which permits the estimation of the AMPA-receptor-mediated maximal synaptic conductance based on numbers of GluR1s and GluR2s predicted by the biochemical signalling model. We show that our model reproduces neuromodulator-gated spike-timing-dependent plasticity as observed in the visual cortex and can be fit to data from many cortical areas, uncovering the biochemical contributions of the pathways pinpointed by the underlying experimental studies. Our model explains the dependence of different forms of plasticity on the availability of different proteins and can be used for the study of mental disorder-associated impairments of cortical plasticity.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Simulation scripts can be found at http://modeldb.yale.edu/260971. Password "synaptic" required during peer-review.

Article and author information

Author details

  1. Tuomo Mäki-Marttunen

    Computational Physiology, Simula Research Laboratory, Fornebu, Norway
    For correspondence
    tuomo@simula.no
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7082-2507
  2. Nicolangelo Iannella

    Department of Biosciences, University of Oslo, Oslo, Norway
    Competing interests
    The authors declare that no competing interests exist.
  3. Andrew G Edwards

    Computational Physiology, Simula Research Laboratory, Fornebu, Norway
    Competing interests
    The authors declare that no competing interests exist.
  4. Gaute T Einevoll

    Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5425-5012
  5. Kim T Blackwell

    Bioengineering Department, George Mason University, Fairfax, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4711-2344

Funding

Research council of Norway (248828)

  • Tuomo Mäki-Marttunen
  • Andrew G Edwards
  • Gaute T Einevoll

European Union Horizon 2020 Research and Innovation (785907)

  • Gaute T Einevoll

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

Reviewing Editor

  1. Harel Z Shouval, University of Texas Medical School at Houston, United States

Version history

  1. Received: February 3, 2020
  2. Accepted: July 29, 2020
  3. Accepted Manuscript published: July 30, 2020 (version 1)
  4. Version of Record published: August 13, 2020 (version 2)
  5. Version of Record updated: January 28, 2021 (version 3)

Copyright

© 2020, Mäki-Marttunen 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. Tuomo Mäki-Marttunen
  2. Nicolangelo Iannella
  3. Andrew G Edwards
  4. Gaute T Einevoll
  5. Kim T Blackwell
(2020)
A unified computational model for cortical post-synaptic plasticity
eLife 9:e55714.
https://doi.org/10.7554/eLife.55714

Share this article

https://doi.org/10.7554/eLife.55714

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