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Merge pull request #22 from irahulsonkar/master
Test on development
2 parents 78b75f9 + 7445d9f commit c2250e2

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Lines changed: 282 additions & 34 deletions

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‎.gitignore‎

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@@ -35,3 +35,4 @@ Tutorial/Source/*json
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/Tutorial/Source/*_brian.py
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*code.gen.*
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*_eden.py
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**/.ipynb_checkpoints/

‎Tutorial/Source/HodgkinHuxley.py‎

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class HodgkinHuxley():
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"""Full Hodgkin-Huxley Model implemented in Python"""
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C_m = 1.0
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"""membrane capacitance, in uF/cm^2"""
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g_Na = 120.0
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"""Sodium (Na) maximum conductances, in mS/cm^2"""
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g_K = 36.0
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"""Postassium (K) maximum conductances, in mS/cm^2"""
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g_L = 0.3
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"""Leak maximum conductances, in mS/cm^2"""
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E_Na = 50.0
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"""Sodium (Na) Nernst reversal potentials, in mV"""
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E_K = -77.0
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"""Postassium (K) Nernst reversal potentials, in mV"""
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E_L = -54.387
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"""Leak Nernst reversal potentials, in mV"""
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t = np.arange(0.0, 450.0, 0.01)
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""" The time to integrate over """
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""" __init__ uses optional arguments """
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""" when no argument is passed default values are used """
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def __init__(self, C_m=1, g_Na=120, g_K=36, g_L=0.3, E_Na=50, E_K=-77, E_L=-54.387, t_0=0, t_n=450, delta_t=0.01, I_inj_max=0, I_inj_width=0, I_inj_trans=0):
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self.C_m = C_m
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""" membrane capacitance, in uF/cm^2 """
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self.g_Na = g_Na
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""" Sodium (Na) maximum conductances, in mS/cm^2 """
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self.g_K = g_K
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""" Postassium (K) maximum conductances, in mS/cm^2 """
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self.g_L = g_L
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""" Leak maximum conductances, in mS/cm^2 """
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self.E_Na = E_Na
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""" Sodium (Na) Nernst reversal potentials, in mV """
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self.E_K = E_K
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""" Postassium (K) Nernst reversal potentials, in mV """
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self.E_L = E_L
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""" Leak Nernst reversal potentials, in mV """
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self.t = np.arange(t_0, t_n, delta_t)
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""" The time to integrate over """
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""" Advanced input - injection current (single rectangular pulse only) """
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self.I_inj_max = I_inj_max
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""" maximum value or amplitude of injection pulse """
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self.I_inj_width = I_inj_width
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""" duration or width of injection pulse """
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self.I_inj_trans = I_inj_trans
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""" strart time of injection pulse or tranlation about time axis """
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def alpha_m(self, V):
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"""Channel gating kinetics. Functions of membrane voltage"""
@@ -98,7 +114,19 @@ def I_inj(self, t):
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| step up to 35 uA/cm^2 at t>300
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| step down to 0 uA/cm^2 at t>400
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"""
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return 10*(t>100) - 10*(t>200) + 35*(t>300) - 35*(t>400)
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""" running standalone python script """
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if __name__ == '__main__':
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return 10*(t>100) - 10*(t>200) + 35*(t>300) - 35*(t>400)
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#""" running jupyterLab notebook """
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#advanced input (if checkbox selected)
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elif self.I_inj_width>0:
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return self.I_inj_max*(t>self.I_inj_trans) - self.I_inj_max*(t>self.I_inj_trans+self.I_inj_width)
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#basic input
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else:
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return 10*(t>100) - 10*(t>200) + 35*(t>300) - 35*(t>400)
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@staticmethod
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def dALLdt(X, t, self):
@@ -131,32 +159,34 @@ def Main(self):
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ik = self.I_K(V, n)
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il = self.I_L(V)
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plt.figure()
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plt.figure(figsize=[15,10])
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ax1 = plt.subplot(4,1,1)
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plt.title('Hodgkin-Huxley Neuron')
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plt.xlim([np.min(self.t),np.max(self.t)]) #for all subplots
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plt.title('Hodgkin-Huxley Neuron', fontsize = 20)
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plt.plot(self.t, V, 'k')
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plt.ylabel('V (mV)')
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plt.ylabel('V (mV)', fontsize = 15)
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plt.subplot(4,1,2, sharex = ax1)
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plt.plot(self.t, ina, 'c', label='$I_{Na}$')
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plt.plot(self.t, ik, 'y', label='$I_{K}$')
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plt.plot(self.t, il, 'm', label='$I_{L}$')
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plt.ylabel('Current')
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plt.legend()
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plt.ylabel('Current', fontsize = 15)
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plt.legend(bbox_to_anchor=(1.1, 0.5),loc='center right', fontsize = 15, borderaxespad=0)
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plt.subplot(4,1,3, sharex = ax1)
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plt.plot(self.t, m, 'r', label='m')
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plt.plot(self.t, h, 'g', label='h')
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plt.plot(self.t, n, 'b', label='n')
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plt.ylabel('Gating Value')
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plt.legend()
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plt.ylabel('Gating Value', fontsize = 15)
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plt.legend(bbox_to_anchor=(1.1, 0.5),loc='center right', fontsize = 15, borderaxespad=0)
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plt.subplot(4,1,4, sharex = ax1)
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i_inj_values = [self.I_inj(t) for t in self.t]
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plt.plot(self.t, i_inj_values, 'k')
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plt.xlabel('t (ms)')
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plt.ylabel('$I_{inj}$ ($\\mu{A}/cm^2$)')
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plt.xlabel('t (ms)', fontsize = 15)
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plt.ylabel('$I_{inj}$ ($\\mu{A}/cm^2$)', fontsize = 15)
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plt.ylim(-1, 40)
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plt.tight_layout()

‎notebook/equivalentCircuit.PNG‎

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‎notebook/notebook.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": []
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},
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"source": [
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"# Interactive Tutorial for Hodgkin Huxley Model on Juypter Lab"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<font size=\"5\">HH Model for Single Neuron</font>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This model relies on a basic equivalence between a biological membrane plus embedded ion channels, and an electrical circuit.\n",
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"\n",
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"<font size=\"5\">Basic inputs to the Model</font>\n",
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"\n",
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"1) Membrane capacitance, $\\mu{A}/cm^2$\n",
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"2) Maximum Conductances, $mS/cm^2$\n",
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"3) Nernst Reverasal Potentials, $mV$\n",
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"4) Simulation Parameters (time), $ms$\n",
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"\n",
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"<img src=\"equivalentCircuit.PNG\" width=\"500\"/>\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<font size=\"5\">Execute Hodgkin Huxley Model</font>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "0069befeceae4a93a70e238bd4db6770",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(HBox(children=(HTML(value=\"<b><font color='blue'>Membrane Capacitance, uF/cm^2</b>\"),)), HBox(c…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "f778c8dc95a342e3a889af3c1ce47463",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(Checkbox(value=False, description=\"<b><font color='blue'>Advanced Input - Incjection Cur…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import ipywidgets\n",
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"import ui_widget\n",
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"from importlib.machinery import SourceFileLoader\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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" \n",
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"# imports the module from the given path\n",
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"HHmodel = SourceFileLoader(\"HodgkinHuxley.py\",\"../Tutorial/Source/HodgkinHuxley.py\").load_module()\n",
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"\n",
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"#function to call python script as a module\n",
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"def runHH(C_m, g_Na, g_K, g_L, E_Na, E_K, E_L, t_0, t_n, delta_t, I_inj_max, I_inj_width, I_inj_trans):\n",
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" runner = HHmodel.HodgkinHuxley(C_m, g_Na, g_K, g_L, E_Na, E_K, E_L, t_0, t_n, delta_t, I_inj_max, I_inj_width, I_inj_trans)\n",
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" runner.Main()\n",
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"\n",
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"#function to handle checkbox event\n",
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"def checkboxEvent(checkboxStatus):\n",
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" if checkboxStatus:\n",
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" wid_inj=ipywidgets.interact(ui_widget.injectorCurrent,amplidute=ui_widget.slider_amplitude,t_width=ui_widget.slider_width,t_translation=ui_widget.slider_translation);\n",
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" display(wid_plotArea)\n",
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" else:\n",
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" display(wid_plotArea)\n",
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"\n",
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"#create checkbox widget\n",
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"wid_checkbox=ipywidgets.interactive(checkboxEvent,checkboxStatus=ui_widget.cb);\n",
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"\n",
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"#create plot area widget and interact with HHmodel\n",
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"wid_plotArea=ipywidgets.interactive_output(runHH,{'C_m':ui_widget.slider_capacitance,\n",
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" 'g_Na':ui_widget.slider_cond_Na, 'g_K':ui_widget.slider_cond_K, 'g_L':ui_widget.slider_cond_L, \n",
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" 'E_Na':ui_widget.slider_pot_Na, 'E_K':ui_widget.slider_pot_K, 'E_L':ui_widget.slider_pot_L,\n",
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" 't_0':ui_widget.time_start, 't_n':ui_widget.time_end, 'delta_t':ui_widget.time_step, \n",
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" 'I_inj_max':ui_widget.slider_amplitude,'I_inj_width':ui_widget.slider_width,'I_inj_trans':ui_widget.slider_translation})\n",
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" \n",
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"display(ui_widget.basicInputs,wid_checkbox)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<font size=\"5\">Description of the Plots</font>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"1) Starting from the bottom, the first (bottom-most) plot shows two currents injected into the cell membrane at times 100ms and 300ms.\n",
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"2) The second plot from the bottom shows the activation/inactivation parameters of the ion channels in the neuron. \n",
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"3) Third plot from the bottom (the current/time plot) makes this more concrete, showing the influx (negative y-axis) and outflux (positive y-axis) of ions passing through each type of ion channel being modeled.\n",
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"4) The top plot, which shows neural membrane voltage activity. The spikes here are called “action potentials” and correspond directly to the current/time plot. "
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎notebook/ui_widget.py‎

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import ipywidgets
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import numpy as np
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import pylab as plt
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def resetTodefault(_):
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slider_capacitance.value = 1
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slider_cond_Na.value = 120
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slider_cond_K.value = 36
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slider_cond_L.value = 0.3
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slider_pot_Na.value = 50
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slider_pot_K.value = -77
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slider_pot_L.value = -54.387
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time_start.value = 0
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time_end.value = 450
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time_step.value = 0.01
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slider_amplitude.value = 50
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slider_width.value = 0
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slider_translation.value = 50
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cb.value = False
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#function to define injector current parameters and plot
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def injectorCurrent(amplidute=10, t_width=100, t_translation=100):
23+
t = np.arange(0, 450, 5) #range for plotting only not used in simulation
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for i in t:
25+
I_inj = amplidute*(t>t_translation) - amplidute*(t>t_translation+t_width)
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plt.figure()
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plt.plot(t, I_inj)
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plt.xlabel('t (ms)', fontsize = 15)
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plt.ylabel('$I_{inj}$ ($\\mu{A}/cm^2$)')
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plt.show()
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#display(plt.show(),wid_plotArea)
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header_capacitance = ipywidgets.HTML(value=f"<b><font color='blue'>Membrane Capacitance, uF/cm^2</b>")
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header_conductance = ipywidgets.HTML(value=f"<b><font color='blue'>Maximum Conductances, mS/cm^2</b>")
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header_potential = ipywidgets.HTML(value=f"<b><font color='blue'>Nernst Reverasal Potentials, mV</b>")
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header_simTime = ipywidgets.HTML(value=f"<b><font color='blue'>Simulation Time, ms</b>")
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slider_capacitance = ipywidgets.FloatSlider(value=1,min=0,max=3,step=0.1,description='Capacitance',readout_format='.1f',continuous_update=False)
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slider_cond_Na = ipywidgets.FloatSlider(value=120,min=80,max=160,step=0.1,description='Sodium',readout_format='.1f',continuous_update=False)
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slider_cond_K = ipywidgets.FloatSlider(value=36,min=0,max=80,step=0.1,description='Potassium',readout_format='.1f',continuous_update=False)
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slider_cond_L = ipywidgets.FloatSlider(value=0.3,min=0,max=1,step=0.1,description='Leak',readout_format='.1f',continuous_update=False)
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slider_pot_Na = ipywidgets.FloatSlider(value=50,min=-100,max=100,step=0.1,description='Sodium',readout_format='.1f',continuous_update=False)
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slider_pot_K = ipywidgets.FloatSlider(value=-77,min=-100,max=100,step=0.1,description='Potassium',readout_format='.1f',continuous_update=False)
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slider_pot_L = ipywidgets.FloatSlider(value=-54.387,min=-100,max=100,step=0.1,description='Leak',readout_format='.1f',continuous_update=False)
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time_start = ipywidgets.FloatText(value=0,description='Start Time',disabled=True)
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time_end = ipywidgets.FloatText(value=450,description='Total Time',disabled=False)
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time_step = ipywidgets.FloatText(value=0.01,description='Time Step',disabled=False)
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reset_button = ipywidgets.Button(description="Reset All")
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reset_button.on_click(resetTodefault)
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h1=ipywidgets.HBox([header_capacitance])
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h2=ipywidgets.HBox([slider_capacitance])
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h3=ipywidgets.HBox([header_conductance])
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h4=ipywidgets.HBox([slider_cond_Na,slider_cond_K,slider_cond_L])
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h5=ipywidgets.HBox([header_potential])
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h6=ipywidgets.HBox([slider_pot_Na,slider_pot_K,slider_pot_L])
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h7=ipywidgets.HBox([header_simTime])
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h8=ipywidgets.HBox([time_start,time_end,time_step])
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h9=ipywidgets.HBox([reset_button])
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basicInputs=ipywidgets.VBox([h1,h2,h3,h4,h5,h6,h7,h8,h9])
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#Advanced Inputs
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cb=ipywidgets.Checkbox(value=False,description=f"<b><font color='blue'>Advanced Input - Incjection Current</b>",indent=False)
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slider_amplitude = ipywidgets.FloatSlider(value=50,min=-100,max=100,step=1,description='Amplitude',readout_format='d',continuous_update=False)
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slider_width = ipywidgets.FloatSlider(value=0,min=0,max=500,step=1,description='Width',readout_format='d',continuous_update=False)
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slider_translation = ipywidgets.FloatSlider(value=50,min=0,max=250,step=1,description='Start at',readout_format='d',continuous_update=False)

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