Page MenuHomec4science
Files F82149692

plot_gating_kinetics.py
No OneTemporary
Actions

File Metadata

Created
Mon, Sep 9, 21:01

plot_gating_kinetics.py
View Options

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author: Theo Lemaire
# @Date: 2016-10-11 20:35:38
# @Email: theo.lemaire@epfl.ch
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2019-03-14 23:37:42
''' Plot the voltage-dependent steady-states and time constants of activation and inactivation
gates of the different ionic currents involved in the neuron's membrane dynamics. '''
import numpy as np
import matplotlib.pyplot as plt
from argparse import ArgumentParser
from PySONIC.utils import logger
from PySONIC.neurons import getNeuronsDict
# Default parameters
defaults = dict(
neuron='RS'
)
def plotGatingKinetics(neuron, fs=15):
''' Plot the voltage-dependent steady-states and time constants of activation and
inactivation gates of the different ionic currents involved in a specific
neuron's membrane.
:param neuron: specific channel mechanism object
:param fs: labels and title font size
'''
# Input membrane potential vector
Vm = np.linspace(-100, 50, 300)
xinf_dict = {}
taux_dict = {}
logger.info('Computing %s neuron gating kinetics', neuron.name)
names = neuron.states
for xname in names:
Vm_state = True
# Names of functions of interest
xinf_func_str = xname.lower() + 'inf'
taux_func_str = 'tau' + xname.lower()
alphax_func_str = 'alpha' + xname.lower()
betax_func_str = 'beta' + xname.lower()
# derx_func_str = 'der' + xname.upper()
# 1st choice: use xinf and taux function
if hasattr(neuron, xinf_func_str) and hasattr(neuron, taux_func_str):
xinf_func = getattr(neuron, xinf_func_str)
taux_func = getattr(neuron, taux_func_str)
xinf = np.array([xinf_func(v) for v in Vm])
if isinstance(taux_func, float):
taux = taux_func * np.ones(len(Vm))
else:
taux = np.array([taux_func(v) for v in Vm])
# 2nd choice: use alphax and betax functions
elif hasattr(neuron, alphax_func_str) and hasattr(neuron, betax_func_str):
alphax_func = getattr(neuron, alphax_func_str)
betax_func = getattr(neuron, betax_func_str)
alphax = np.array([alphax_func(v) for v in Vm])
if isinstance(betax_func, float):
betax = betax_func * np.ones(len(Vm))
else:
betax = np.array([betax_func(v) for v in Vm])
taux = 1.0 / (alphax + betax)
xinf = taux * alphax
# # 3rd choice: use derX choice
# elif hasattr(neuron, derx_func_str):
# derx_func = getattr(neuron, derx_func_str)
# xinf = brentq(lambda x: derx_func(neuron.Vm, x), 0, 1)
else:
Vm_state = False
if not Vm_state:
logger.error('no function to compute %s-state gating kinetics', xname)
else:
xinf_dict[xname] = xinf
taux_dict[xname] = taux
fig, axes = plt.subplots(2)
fig.suptitle('{} neuron: gating dynamics'.format(neuron.name))
ax = axes[0]
ax.get_xaxis().set_ticklabels([])
ax.set_ylabel('$X_{\infty}$', fontsize=fs)
for xname in names:
if xname in xinf_dict:
ax.plot(Vm, xinf_dict[xname], lw=2, label='$' + xname + '_{\infty}$')
ax.legend(fontsize=fs, loc=7)
ax = axes[1]
ax.set_xlabel('$V_m\ (mV)$', fontsize=fs)
ax.set_ylabel('$\\tau_X\ (ms)$', fontsize=fs)
for xname in names:
if xname in taux_dict:
ax.plot(Vm, taux_dict[xname] * 1e3, lw=2, label='$\\tau_{' + xname + '}$')
ax.legend(fontsize=fs, loc=7)
return fig
def main():
ap = ArgumentParser()
# Stimulation parameters
ap.add_argument('-n', '--neuron', type=str, default=defaults['neuron'],
help='Neuron name (string)')
# Parse arguments
args = ap.parse_args()
neuron_str = args.neuron
# Plot gating kinetics variables
if neuron_str not in getNeuronsDict():
logger.error('Unknown neuron type: "%s"', neuron_str)
return
neuron = getNeuronsDict()[neuron_str]()
plotGatingKinetics(neuron)
plt.show()
if __name__ == '__main__':
main()

Event Timeline

c4science · Help