1. Computational and Systems Biology
  2. Neuroscience
Download icon

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
Research Article
  • Cited 1
  • Views 1,342
  • Annotations
Cite this article as: eLife 2020;9:e55714 doi: 10.7554/eLife.55714
Voice your concerns about research culture and research communication: Have your say in our 7th annual survey.


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
    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


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

Publication 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)


© 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.


  • 1,342
    Page views
  • 237
  • 1

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

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)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Computational and Systems Biology
    Michael A Petr et al.
    Research Article Updated

    Aging is associated with distinct phenotypical, physiological, and functional changes, leading to disease and death. The progression of aging-related traits varies widely among individuals, influenced by their environment, lifestyle, and genetics. In this study, we conducted physiologic and functional tests cross-sectionally throughout the entire lifespan of male C57BL/6N mice. In parallel, metabolomics analyses in serum, brain, liver, heart, and skeletal muscle were also performed to identify signatures associated with frailty and age-dependent functional decline. Our findings indicate that declines in gait speed as a function of age and frailty are associated with a dramatic increase in the energetic cost of physical activity and decreases in working capacity. Aging and functional decline prompt organs to rewire their metabolism and substrate selection and toward redox-related pathways, mainly in liver and heart. Collectively, the data provide a framework to further understand and characterize processes of aging at the individual organism and organ levels.

    1. Computational and Systems Biology
    2. Ecology
    Tristan Walter, Iain D Couzin
    Tools and Resources Updated

    Automated visual tracking of animals is rapidly becoming an indispensable tool for the study of behavior. It offers a quantitative methodology by which organisms’ sensing and decision-making can be studied in a wide range of ecological contexts. Despite this, existing solutions tend to be challenging to deploy in practice, especially when considering long and/or high-resolution video-streams. Here, we present TRex, a fast and easy-to-use solution for tracking a large number of individuals simultaneously using background-subtraction with real-time (60 Hz) tracking performance for up to approximately 256 individuals and estimates 2D visual-fields, outlines, and head/rear of bilateral animals, both in open and closed-loop contexts. Additionally, TRex offers highly accurate, deep-learning-based visual identification of up to approximately 100 unmarked individuals, where it is between 2.5 and 46.7 times faster, and requires 2–10 times less memory, than comparable software (with relative performance increasing for more organisms/longer videos) and provides interactive data-exploration within an intuitive, platform-independent graphical user-interface.