A model of ASIC1a and synaptic cleft pH modulating wind-up in wide dynamic range neurons (Delrocq)

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We introduce a model of ASIC1a homomeric (and heteromeric) ion channel inserted into a pre-existing model of wide dynamic range (WDR) neuron of the spinal cord together with a novel synaptic cleft acidification mechanism. This computational model shows a dual contribution of the ASIC1a channels to wind-up, a facilitation mechanism of WDR neurons, which has been verified experimentally: inhibiting or maximally activating ASICs reduce wind-up. The wind-up inhibition by activation of ASICs is likely mediated by calcium influx and calcium-activated potassium channels.
Model Information (Click on a link to find other models with that property)
Model Type: Channel/Receptor; Synapse; Neuron or other electrically excitable cell; Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Wide dynamic range neuron;
Channel(s): I Calcium; I Potassium; I Sodium;
Gap Junctions:
Simulation Environment: NEURON; Python;
Model Concept(s): Facilitation; Detailed Neuronal Models; Ion Channel Kinetics; Pain processing;
Search NeuronDB for information about:  I Sodium; I Calcium; I Potassium;
import os
import numpy as np
import sys

from base import name_from_pars, runmodl

if __name__ == "__main__":

    ind = int(sys.argv[1])

    # read the correct line (given by ind) of the args file
    args_list = np.loadtxt('args.txt', delimiter=' ')
    if ind >= len(args_list):
    print("=" * 10, ind + 1, "/", len(args_list), "=" * 10)
    from neuron import h

    args = args_list[ind]

    dur = 1
    folder = "sim_outputs"   # name of folder in which to save results
    b0 = 22
    if not os.path.exists(folder):

    q0, tau, cond, ca = args

    name = os.path.join(folder, name_from_pars(b0, q0, tau, dur, ca, cond, acti=False)[:-7])
    runmodl(h, asic_cond=cond, ca_ratio=ca, b0=b0, q0=q0, tau_pH=tau,
            duration=dur, name=name, save_pH=True,
            save_vars=((q0, tau) in [(1., 0.01), (0.05, 1.), (0.3, 0.1)]))   # only save the (heavy) evolution of
            # variables over time for the chosen model (and alternatives 1 and 2)