Biophysically realistic neuron models for simulation of cortical stimulation (Aberra et al. 2018)

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Accession:241165
This archive instantiates the single-cell cortical models used in (Aberra et al. 2018) and sets up extracellular stimulation with either a point-current source, to simulate intracortical microstimulation (ICMS), or a uniform E-field distribution, with a monophasic, rectangular pulse waveform in both cases.
Reference:
1 . Aberra AS, Peterchev AV, Grill WM (2018) Biophysically realistic neuron models for simulation of cortical stimulation. J Neural Eng 15:066023 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Axon;
Brain Region(s)/Organism: Neocortex; Barrel cortex;
Cell Type(s): Myelinated neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Action Potential Initiation; Detailed Neuronal Models;
Implementer(s): Aberra, Aman [aman.aberra at duke.edu];
/
AberraEtAl2018
cells
L5_TTPC2_cADpyr232_1
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.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.                                
                                                                                 
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OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN           
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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
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*/ 

/*                                                                               
 * @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 = -0.286011
step_amp1 = 0.5930628
step_amp2 = 0.6424847
step_amp3 = 0.6919066

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

proc create_cell() { local synapses_enabled
    synapses_enabled = $1
    cell = new cADpyr232_L5_TTPC2_8052133265(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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