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

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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;
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synaptic
L23PC
L23_PC_cADpyr229_2
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 *
                            
/*                                                                               
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 createsimulation.hoc                                                           
 * @brief Set up the simulation                                
 * @author Werner Van Geit @ BBP                                                 
 * @date 2015                                                                    
*/        

load_file("nrngui.hoc")
load_file("import3d.hoc")

load_file("morphology.hoc")
load_file("biophysics.hoc")
load_file("template.hoc")

//======================== settings ===================================

hyp_amp = -0.067261
step_amp1 = 0.1692192
step_amp2 = 0.1833208
step_amp3 = 0.1974224

//=================== creating cell object ===========================
objref cell

proc create_cell() { local synapses_enabled
    synapses_enabled = $1
    cell = new cADpyr229_L23_PC_8ef1aa6602(synapses_enabled)
}
//==================== recording settings ==========================

objref time, voltage
proc create_recording() {
    voltage = new Vector()
    time = new Vector()

    access cell.soma
    time.record(&t, 0.1)
    voltage.record(&v(0.5), 0.1)

}

//==================== stimulus settings ===========================

objref hypamp_stimulus
objref step_stimulus

proc create_stimulus() {
    strdef stepcurrent 
    stepcurrent = $s1
    hypamp_stimulus = new IClamp(0.5)
    hypamp_stimulus.dur = tstop
    hypamp_stimulus.del = 0

    cell.soma hypamp_stimulus

    step_stimulus = new IClamp(0.5)
    step_stimulus.dur = 2000
    step_stimulus.del = 700
    if (strcmp(stepcurrent, "stepcurrent1") == 0) { 
        step_stimulus.amp = step_amp1
        hypamp_stimulus.amp = hyp_amp
    } else if (strcmp(stepcurrent, "stepcurrent2") == 0) {
        step_stimulus.amp = step_amp2
        hypamp_stimulus.amp = hyp_amp
    } else if (strcmp(stepcurrent, "stepcurrent3") == 0) {
        step_stimulus.amp = step_amp3
        hypamp_stimulus.amp = hyp_amp
    } else {
        step_stimulus.amp = 0.0
        hypamp_stimulus.amp = 0.0
    }

    cell.soma step_stimulus
}

//============================= simulation ================================

proc simulate() {
    cvode.active(0)

    run()
}

//============================= saving results ============================

proc save_recording() { localobj timevoltage, fh
    timevoltage = new Matrix(time.size(), 2)
    timevoltage.setcol(0, time)
    timevoltage.setcol(1, voltage)

    fh = new File()
    fh.wopen("hoc_recordings/soma_voltage.dat")
    timevoltage.fprint(0, fh, "%e ")
    fh.close()
}

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