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

 Download zip file 
Help downloading and running models
Accession:260971
"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."
Reference:
1 . Mäki-Marttunen T, Iannella N, Edwards AG, Einevoll GT, Blackwell KT (2020) A unified computational model for cortical post-synaptic plasticity. Elife [PubMed]
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:
Receptor(s):
Gene(s):
Transmitter(s): Glutamate; Norephinephrine; Acetylcholine;
Simulation Environment: NEURON; NeuroRD;
Model Concept(s): Long-term Synaptic Plasticity;
Implementer(s): Maki-Marttunen, Tuomo [tuomomm at uio.no];
Search NeuronDB for information about:  I Calcium; Acetylcholine; Norephinephrine; Glutamate;
/
synaptic
L23PC
L23_PC_cADpyr229_5
hoc_recordings
mechanisms
morphology
python_recordings
synapses
README *
biophysics.hoc *
cellinfo.json *
CHANGELOG *
constants.hoc *
creategui.hoc *
createsimulation.hoc *
current_amps.dat *
init.hoc *
LICENSE *
morphology.hoc *
mosinit.hoc *
ringplot.hoc *
run.py *
run_hoc.sh *
run_py.sh *
run_RmpRiTau.py *
run_RmpRiTau_py.sh *
template.hoc
VERSION *
                            
#!/usr/bin/env python

"""Python script to run cell model"""


"""
/* Copyright (c) 2015 EPFL-BBP, All rights reserved.

THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE BLUE BRAIN PROJECT
BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

This work is licensed under a
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc-sa/4.0/legalcode or send a letter to
Creative Commons, 171 Second Street, Suite 300,
San Francisco, California, 94105, USA.
"""

"""
 * @file run.py
 * @brief Run simulation using pyneuron
 * @author Werner Van Geit @ BBP
 * @date 2015
"""

# pylint: disable=C0325, W0212, F0401, W0612, F0401

import os
import neuron
import numpy
import sys


def create_cell(add_synapses=True):
    """Create the cell model"""
    # Load morphology
    neuron.h.load_file("morphology.hoc")
    # Load biophysics
    neuron.h.load_file("biophysics.hoc")
    # Load main cell template
    neuron.h.load_file("template.hoc")

    # Instantiate the cell from the template

    print("Loading cell cADpyr229_L23_PC_c2e79db05a")
    cell = neuron.h.cADpyr229_L23_PC_c2e79db05a(1 if add_synapses else 0)
    return cell


def create_stimuli(cell, step_number):
    """Create the stimuli"""

    print('Attaching stimulus electrodes')

    stimuli = []
    step_amp = [0] * 3

    with open('current_amps.dat', 'r') as current_amps_file:
        first_line = current_amps_file.read().split('\n')[0].strip()
        hyp_amp, step_amp[0], step_amp[1], step_amp[2] = first_line.split(' ')

    iclamp = neuron.h.IClamp(0.5, sec=cell.soma[0])
    iclamp.delay = 700
    iclamp.dur = 2000
    iclamp.amp = float(step_amp[step_number - 1])
    print('Setting up step current clamp: '
          'amp=%f nA, delay=%f ms, duration=%f ms' %
          (iclamp.amp, iclamp.delay, iclamp.dur))

    stimuli.append(iclamp)

    hyp_iclamp = neuron.h.IClamp(0.5, sec=cell.soma[0])
    hyp_iclamp.delay = 0
    hyp_iclamp.dur = 3000
    hyp_iclamp.amp = float(hyp_amp)
    print('Setting up hypamp current clamp: '
          'amp=%f nA, delay=%f ms, duration=%f ms' %
          (hyp_iclamp.amp, hyp_iclamp.delay, hyp_iclamp.dur))

    stimuli.append(hyp_iclamp)

    return stimuli


def create_recordings(cell):
    """Create the recordings"""
    print('Attaching recording electrodes')

    recordings = {}

    recordings['time'] = neuron.h.Vector()
    recordings['soma(0.5)'] = neuron.h.Vector()

    recordings['time'].record(neuron.h._ref_t, 0.1)
    recordings['soma(0.5)'].record(cell.soma[0](0.5)._ref_v, 0.1)

    return recordings


def run_step(step_number, plot_traces=None):
    """Run step current simulation with index step_number"""

    cell = create_cell(add_synapses=False)
    stimuli = create_stimuli(cell, step_number)
    recordings = create_recordings(cell)

    # Overriding default 30s simulation,
    print('Setting simulation time to 3s for the step currents')
    neuron.h.tstop = 3000

    print('Disabling variable timestep integration')
    neuron.h.cvode_active(0)

    print('Running for %f ms' % neuron.h.tstop)
    neuron.h.run()

    time = numpy.array(recordings['time'])
    soma_voltage = numpy.array(recordings['soma(0.5)'])

    recordings_dir = 'python_recordings'

    soma_voltage_filename = os.path.join(
        recordings_dir,
        'soma_voltage_step%d.dat' % step_number)
    numpy.savetxt(
            soma_voltage_filename,
            numpy.transpose(
               numpy.vstack((
                    time,
                    soma_voltage))))

    print('Soma voltage for step %d saved to: %s'
          % (step_number, soma_voltage_filename))

    if plot_traces:
        import pylab
        pylab.figure()
        pylab.plot(recordings['time'], recordings['soma(0.5)'])
        pylab.xlabel('time (ms)')
        pylab.ylabel('Vm (mV)')
        pylab.gcf().canvas.set_window_title('Step %d' % step_number)


def init_simulation():
    """Initialise simulation environment"""

    neuron.h.load_file("stdrun.hoc")
    neuron.h.load_file("import3d.hoc")

    print('Loading constants')
    neuron.h.load_file('constants.hoc')


def main(plot_traces=True):
    """Main"""

    # Import matplotlib to plot the traces
    if plot_traces:
        import matplotlib
        matplotlib.rcParams['path.simplify'] = False

    init_simulation()

    for step_number in range(1, 4):
        run_step(step_number, plot_traces=plot_traces)

    if plot_traces:
        import pylab
        pylab.show()

if __name__ == '__main__':
    if len(sys.argv) == 1:
        main(plot_traces=True)
    elif len(sys.argv) == 2 and sys.argv[1] == '--no-plots':
        main(plot_traces=False)
    else:
        raise Exception(
            "Script only accepts one argument: --no-plots, not %s" %
            str(sys.argv))

Loading data, please wait...