Biochemically detailed model of LTP and LTD in a cortical spine (Maki-Marttunen et al 2020)

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"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."
1 . Mäki-Marttunen T, Iannella N, Edwards AG, Einevoll GT, Blackwell KT (2020) A unified computational model for cortical post-synaptic plasticity. Elife [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex spiking regular (RS) neuron;
Channel(s): I Calcium;
Gap Junctions:
Transmitter(s): Glutamate; Norephinephrine; Acetylcholine;
Simulation Environment: NEURON; NeuroRD;
Model Concept(s): Long-term Synaptic Plasticity;
Implementer(s): Maki-Marttunen, Tuomo [tuomomm at];
Search NeuronDB for information about:  I Calcium; Acetylcholine; Norephinephrine; Glutamate;
biophysics.hoc *
cellinfo.json *
constants.hoc *
creategui.hoc *
createsimulation.hoc *
current_amps.dat *
init.hoc *
morphology.hoc *
mosinit.hoc *
ringplot.hoc * * * * * *
Running NEURON model packages ME-type models

The instructions below are based on the instructions for running models from
ModelDB ( They assume you have installed
a recent NEURON package (>7.3 is known to work), available at

Linux / Unix / Mac
1. Download one of the model packages from the Downloads section, or from an
me-type fact sheet.  For this example we will download the package named (i.e. m-type L5_DBC, e-type cAC, exemplar 3)

2. Unzip into the appropriate location

user@machine:~/dev/sim/neuron$ unzip
user@machine:~/dev/sim/neuron$ cd L5_DBC_cACint209_3

3. Launch the hoc GUI

user@machine:~/dev/sim/neuron/L5_DBC_cACint209_3$ sh

4. Read the 'How to use the ME-type model GUI' section below

1. Download one of the model packages from the Downloads section, or from an
me-type fact sheet.  For this example we will download the package named

2. Unzip the downloaded package

3. Run mknrndll and point it to the 'mechanisms' directory inside the unzipped
cell package directory

4. Run nrngui and from using 'File->working dir' menu option change to the
'mechanisms' directory. Then load the 'mosinit.hoc' script from the root of the
unzipped cell package directory

5. The GUI should load automatically. Read the 'How to use the ME-type model
GUI' section below

Running the Python version of the model package on Linux / Unix / Mac OS X
Both Python 2.6+ as 3.4+ are supported.
If you have installed Neuron with Python support, you can run a python version
of the model. The python script depends on numpy and matplotlib, so first 
install these packages with e.g. 'pip install numpy matplotlib'
The script will run without a GUI, and will produce traces for
step current 1, 2 and 3 in the directory python_recordings

user@machine:~/dev/sim/neuron/L5_DBC_cACint209_3$ sh

How to use the ME-type model GUI

To start simulating the model, press the Init & Run button. Initially there are
no stimuli present, which means that the cell will stay at its resting membrane

There are three panels that show the output of the model. One is a graph with
the membrane voltage recorded in the soma (units: ms and mV), the two other
panels have a representation of the cell's morphology. The sections in the
first morphology change color dependending on their membrane voltage during the
simulation. When synaptic input is present, the second morphology will show the
location of the activated synapses.

Two cell can be stimulated in two ways: step currents (current clamp) and
synaptic input.

Step currents can be selected by choosing 'Step current x' using the radio
buttons. Every step corresponds with the same amplitude as was used to generate
the plots on the website.

Synaptic inputs can be enabled by pressing one of the buttons from the list of
presynaptic m-types. When a certain m-type is selected, all the synapses that
cells from the specified m-type make on the simulated cell (in the neocortical
microcircuit model) will become active. Every presynaptic cell will be
represented by a Poisson spike train, and the synapses will receive input from
these virtual cell. The default firing rate of these virtual cells is set to 10
Hz. The user can change these value by changing the input field next to the
respective m-type activation button.

Installing Neuron on Mac OS X using Homebrew

In our experience the Homebrew package manager is the most convenient way to 
get a commandline version of Neuron that has both X11 and Python support 
running on your Mac.

1. Install Homebrew by following the instruction on, or:

ruby -e "$(curl -fsSL")

2. Tap the Science Homebrew repo from 

by executing:

brew tap homebrew/science

3. Now you can install Neuron:

brew install neuron

This will install all the necessary software dependencies 
like InterViews, X11, ...

You can test the installation by executing:


which should start the Neuron GUI

To test the Neuron python module:

python -c 'import neuron'