Mirror Neuron (Antunes et al 2017)

 Download zip file 
Help downloading and running models
Accession:229276
Modeling Mirror Neurons Through Spike-Timing Dependent Plasticity. This script reproduces Figure 3B.
Reference:
1 . Antunes G, Faria da Silva SF, Simoes de Souza FM (2018) Mirror Neurons Modeled Through Spike-Timing-Dependent Plasticity are Affected by Channelopathies Associated with Autism Spectrum Disorder. Int J Neural Syst 28:1750058 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s):
Channel(s): I Calcium; I M; I h; I Potassium; I Sodium;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s): Cav1.2 CACNA1C; Cav1.3 CACNA1D;
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Detailed Neuronal Models; STDP;
Implementer(s): Simoes-de-Souza, Fabio [fabio.souza at ufabc.edu.br];
Search NeuronDB for information about:  AMPA; NMDA; I M; I h; I Sodium; I Calcium; I Potassium; Glutamate;
/
Final
hoc_recordings
mechanisms
morphology
python_recordings
synapses
README
.provenance.json
biophysics.hoc
cellinfo.json
CHANGELOG
constants.hoc *
creategui.hoc
createsimulation.hoc
current_amps.dat
init_super.hoc
LICENSE *
morphology.hoc
mosinit.hoc
ringplot.hoc *
run.py
run_hoc.sh
run_RmpRiTau.py
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 init.hoc                                                           
 * @brief Initialisation                                
 * @author Werner Van Geit @ BBP                                                 
 * @date 2015                                                                    
*/        

//modified by Fabio Simoes de Souza @ UFABC, 2016

//----------------------------------------------------------------------------
//  define a panel to run the different demos
//----------------------------------------------------------------------------

load_file("nrngui.hoc")
load_file("constants.hoc")
load_file("creategui.hoc")
load_file("createsimulation.hoc")


// By default, enable synapses
synapses_enabled = 1

use_mcell_ran4(1) //MCell random number generator

// Set up simulation

create_cell(synapses_enabled,RANDOM_SEED,NUMBER_NEURONS) //create cells (100 pre_synaptic and 100 post_synaptic cells)


//Move cell location
XORIGIN0 = 0
YORIGIN0 = 0
ZORIGIN0 = 0

XORIGIN1 = 0
YORIGIN1 = -3000
ZORIGIN1 = 0

for i=0, NUMBER_NEURONS-1 {

//Move cell location
cell0[i].position(XORIGIN0+1000*i,YORIGIN0,ZORIGIN0)
cell1[i].position(XORIGIN1+1000*i,YORIGIN1,ZORIGIN1)
}


// Start the GUI
make_mainwindow(cell1[0])


//Create Current Stimulus to Presynaptic neurons
create_current_stimulus(NUMBER_NEURONS) 


// Save the windows that already exist before this initialisation
pwmcnt = PWManager[0].count


//Create synapses
objectvar sinapse0[1] //[NUMBER_NEURONS] //STDP1
objectvar sinapse1[1] //[NUMBER_NEURONS] //STDP2
objectvar sinapse2[1] //[NUMBER_NEURONS] //ExpSyn

//parameters
fator= 10 
CONNECTION_THRESHOLD=0
CONNECTION_DELAY=6.7 // ms
CONNECTION_WEIGHT=fator*5e-6 // max conductance uS

for i=0, NUMBER_NEURONS-1 {

access cell1[i].apic(0.5)

cell1[i].sinapse0[0]=new ExpSynSTDP_triplet(0)
cell1[i].sinapse0[0].e=0
cell1[i].sinapse0[0].tau=10
cell1[i].sinapse0[0].factor=1e3//potentiation depotentiation factor
cell1[i].sinapse0[0].gw=fator*5e-6 //uS max conductance

cell1[i].sinapse1[0]=new ExpSynSTDP_triplet(0)
cell1[i].sinapse1[0].e=0
cell1[i].sinapse1[0].tau=10
cell1[i].sinapse1[0].factor=1e3//potentiation depotentiation factor
cell1[i].sinapse1[0].gw=fator*5e-6 //uS max conductance

cell1[i].sinapse2[0]=new ExpSyn(0.5)
cell1[i].sinapse2[0].tau=10
cell1[i].sinapse2[0].e=0

//STDP parameters (Pfister)
cell1[i].sinapse0[0].A2mais=4.6e-3 /1
cell1[i].sinapse0[0].A2menos=3e-3 /1
cell1[i].sinapse0[0].A3mais=9.1e-3 /1
cell1[i].sinapse0[0].A3menos=7.5e-9 /1
cell1[i].sinapse0[0].Taumais=16.8
cell1[i].sinapse0[0].Taumenos= 33.7
cell1[i].sinapse0[0].Tauy=47
cell1[i].sinapse0[0].Taux=575

//STDP parameters (Abbott)
cell1[i].sinapse1[0].A2mais=5e-3  /1
cell1[i].sinapse1[0].A2menos=5.1e-3 /1
cell1[i].sinapse1[0].A3mais=2e-4 /1
cell1[i].sinapse1[0].A3menos=1e-3 /1
cell1[i].sinapse1[0].Taumais=16.8
cell1[i].sinapse1[0].Taumenos= 33.7
cell1[i].sinapse1[0].Tauy=47
cell1[i].sinapse1[0].Taux=575

//synaptic connections
access cell0[i].soma
cell0[i].soma cell1[i].nclist.append(new NetCon(&cell0[i].soma.v(0.5),cell1[i].sinapse0[0],CONNECTION_THRESHOLD,CONNECTION_DELAY,CONNECTION_WEIGHT))

cell0[i].soma cell1[i].nclist.append(new NetCon(&cell0[i].soma.v(0.5),cell1[i].sinapse1[0],CONNECTION_THRESHOLD,CONNECTION_DELAY,CONNECTION_WEIGHT))
}

//Show results
objref grafico[7]

grafico[0]=new Graph()
grafico[0].size(0,tmax ,-100,50)
grafico[0].beginline()
grafico[0].addvar("Pre",&cell0[0].soma.v(0.5),1,1)
grafico[0].addvar("Post",&cell1[0].soma.v(0.5),3,1)

grafico[0].flush()

graphList[0].append(grafico[0])


objref graficoshape
graficoshape=new PlotShape()
graficoshape.variable("soma","v(0.5)")
//graficoshape.view(-594.956, -98.0373, 1260.25, 1188.42, 573, 0, 505.92, 592)
graficoshape.exec_menu("Show Diam")
graficoshape.exec_menu("Shape Plot")
fast_flush_list.append(graficoshape)


//Synapse control menu
nrncontrolmenu()


//setting up to save data to file
create_recording(NUMBER_NEURONS)


//Start Simulation
//simulate()
cvode.active(0)
init()


//Synaptic Stimulation Sequencies

create_synaptic_stimulus(condi, tmax)


///////////////////////
//Save Data to Files//
//////////////////////

save_recording()


/*
/** Procedure linked to the Init & Run button */ 
proc restart() {
    cleanup()	

    // make_plottingpanel()
    create_stimulus(stepcurrent)

    cell.synapses.update_synapses(synapse_plot)
    simulate()

    save_recording()
}


Loading data, please wait...