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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# hodgkin–huxley model"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import ipywidgets as widgets\n",
"plt.style.use(plt.style.available[0])\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the constants and the time step of ODE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The 4 equations of the Hodgkin-Huxley model \n",
"\n",
"($V$: Membrane Potential)\n",
"\n",
"* $C_M\\displaystyle \\frac{dV}{dt}=-g_{Na}\\;\\;(V-V_{Na})-g_k\\;\\;(V-V_K)-g_l\\;\\;(V-V_l)+I$ \n",
"\n",
"\n",
"($h$: Na Inactivation) \n",
"\n",
"* $\\displaystyle \\frac{dh}{dt}=\\alpha_h{(V)}\\;\\;(1-h)-\\beta_h(V)h$ \n",
"\n",
"\n",
"($m$: Na Activation) \n",
"\n",
"* $\\displaystyle \\frac{dm}{dt}=\\alpha_m{(V)}\\;\\;(1-m)-\\beta_m(V)m$ \n",
"\n",
"\n",
"($n$: K Activation) \n",
"\n",
"* $\\displaystyle \\frac{dn}{dt}=\\alpha_n{(V)}\\;\\;(1-n)-\\beta_n(V)n$ "
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {},
"outputs": [],
"source": [
"def dv(v, param, dt, par_dict):\n",
" \n",
" G_Na = par_dict['G_Na']\n",
" G_K = par_dict['G_K']\n",
" G_L = par_dict['G_L']\n",
" E_Na = par_dict['E_Na']\n",
" E_K = par_dict['E_K']\n",
" E_L = par_dict['E_L']\n",
" \n",
" \n",
" \n",
" h, m, n, i = param\n",
" gna = G_Na * h * (m**3)\n",
" gk = G_K * (n**4)\n",
" gl = G_L\n",
" ina = gna * (v - E_Na)\n",
" ik = gk * (v - E_K)\n",
" il = gl * (v - E_L)\n",
" return (- ina - ik - il + i) * dt / C_m\n",
"\n",
"def dh(h, param, dt, par_dict):\n",
" v = param\n",
" alph = 0.07 * np.exp(-v / 20)\n",
" beth = 1. / (np.exp((30 - v) / 10) + 1)\n",
" return (alph * (1 - h) - beth * h) * dt\n",
"\n",
"def dm(m, param, dt, par_dict):\n",
" v = param\n",
" alpm = 0.1 * (25 - v) / (np.exp((25 - v) / 10) - 1)\n",
" betm = 4. * np.exp(-v / 18)\n",
" return (alpm * (1 - m) - betm * m) * dt\n",
"\n",
"def dn(n, param, dt, par_dict):\n",
" v = param\n",
" alpn = 0.01 * (10 - v) / (np.exp((10 - v) / 10) - 1)\n",
" betn = 0.125 * np.exp(-v / 80)\n",
" return (alpn * (1 - n) - betn * n) * dt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Update the variables by Runge–Kutta method"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {},
"outputs": [],
"source": [
"def rk4(f, y, param, dt, par_dict):\n",
" k1 = f(y, param, dt, par_dict)\n",
" k2 = f(y + dt * k1 * 0.5, param, dt, par_dict)\n",
" k3 = f(y + dt * k2 * 0.5, param, dt, par_dict)\n",
" k4 = f(y + dt * k3, param, dt, par_dict)\n",
" return (k1 + 2*k2 + 2*k3 + k4) / 6\n",
"\n",
"def update(v, m, h, n, i, dt, par_dict):\n",
" v += rk4(dv, v, (h, m, n, i), dt, par_dict)\n",
" m += rk4(dm, m, v, dt, par_dict)\n",
" h += rk4(dh, h, v, dt, par_dict)\n",
" n += rk4(dn, n, v, dt, par_dict)\n",
" return v, m, h, n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initialize the variables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 216,
"metadata": {},
"outputs": [],
"source": [
"def run_simulation(\n",
" ### simulation parameters ###\n",
" \n",
" dt = 0.01, # time step (ms)\n",
" T = 70, # total running time (ms)\n",
" \n",
" \n",
" ### model paramters ###\n",
" C_m = 1., # membrane capacitance (micro F/cm**2)\n",
" \n",
" # Sodium Channel\n",
" G_Na = 120., # max Na conductance (mS/cm**2)\n",
" E_Na = 115., # Na reversal potential (mV)\n",
" \n",
" # Kalium Channel\n",
" G_K = 40., # max K conductance (mS/cm**2)\n",
" E_K = -12.0, # K reversal potential (mV)\n",
" \n",
" # Leak current \n",
" G_L = 0.24, # max leak onductance (mS/cm**2)\n",
" E_L = 10.613, # leak potential (mV)\n",
" ):\n",
" \n",
" par_dict = dict(\n",
" G_Na = G_Na,\n",
" E_Na = E_Na,\n",
" G_K = G_K,\n",
" E_K = E_K,\n",
" G_L = G_L,\n",
" E_L = E_L,\n",
" )\n",
" # initialize the simulation parameters\n",
" V_m = 0 # membrane potential (mV)\n",
" m_Na = 0 # Na channel activation (dimensionless)\n",
" h_Na = 0 # Na channel inactivation (dimensionless)\n",
" n_K = 0 # K channel activation (dimensionless)\n",
"\n",
" # total membrane current (microA/cm**2) for the time of the simulation\n",
" \n",
" I_input = np.array([0] * int(T/dt))\n",
" I_input[1500:1800] = 10\n",
" Time = [ii*dt for ii in range(int(T/dt))]\n",
"\n",
"\n",
" result_V_m, result_m_Na, result_h_Na, result_n_K = ([] for i in range(4))\n",
" for t in range(int(100 // dt)):\n",
" V_m, m_Na, h_Na, n_K = update(v=V_m, m=m_Na, h=h_Na, n=n_K, i=0, dt=dt, par_dict = par_dict)\n",
" for t in range(len(I_input)):\n",
" V_m, m_Na, h_Na, n_K = update(v=V_m, m=m_Na, h=h_Na, n=n_K, i=I_input[t], dt=dt, par_dict = par_dict)\n",
" result_V_m.append(V_m)\n",
" result_m_Na.append(m_Na)\n",
" result_h_Na.append(h_Na)\n",
" result_n_K.append(n_K)\n",
" return result_V_m, result_m_Na, result_h_Na, result_n_K, I_input, Time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define membrane current"
]
},
{
"cell_type": "code",
"execution_count": 226,
"metadata": {},
"outputs": [],
"source": [
"def plot_current(Time, Current):\n",
" plt.subplots(figsize = [12,5])\n",
" plt.xlabel('$t$ (ms)')\n",
" plt.ylabel('$I$ $\\mathrm{(\\mu A/cm^2)}$')\n",
" # plt.xlim(-1, 30)\n",
" plt.ylim(-2, 12)\n",
" plt.plot(Time, Current)\n",
" plt.title('Input Current $I$')\n",
" plt.savefig('1current.png', dpi=144)\n",
" \n",
"def plot_membrane_potential(Time, Voltage):\n",
"\n",
" plt.subplots(figsize = [12,5])\n",
" plt.xlabel('$t$ (ms)')\n",
" plt.ylabel('$V$ (mV)')\n",
" plt.ylim(-20, 120)\n",
" plt.plot(Time, Voltage, color = '#bd0026')\n",
" title = plt.title('Membrane Potential $V$')"
]
},
{
"cell_type": "code",
"execution_count": 227,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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ErEpGXvkgq7yQVz7Iqmw0LZiqIrciTLymZVKPeIrMqmDklQ+yygt55YOsykbTAowp+fSw6mODj1wCAAAYC00LUJNkD1r657SwpgUAABSKpgUY02BNS7IKqoX49CwAAKBQNC3AmBpzTkuSuwMAAEweTQumqtPppC6hfonXtAzU/KSlyKwKRl75IKu8kFc+yKpsNC2Yqm63m7qE2vWfcKRa0zKpc1pKzKpk5JUPssoLeeWDrMpG0wKMazA9LM3t+/et+5wWAACApqBpAca0uBA/+QQxAACAItG0AGNKfk5Lf8tjnrQAAIBC0bQANUk2PWyw5TFdCwAAKBNNC6aq1WqlLqF2gy2PE91/8XDJepWYVcnIKx9klRfyygdZlY2mBVPVbrdTl1C7wZqWRI9a+neteyF+iVmVjLzyQVZ5Ia98kFXZaFqAcfXXtCR71LKkEAAAgMLQtABjSr4Qf7CmJVEBAAAAE0bTAoytf05Loulh/XNaktwdAABg8mhagDEtntOSGE9aAABAoWhaMFWdTid1CbVbSLymZXBOS81dS4lZlYy88kFWeSGvfJBV2WhaMFXdbjd1CfVLvKalf+e6dw8rMquCkVc+yCov5JUPsiobTQswpoXEa1q2Sz4vDQAAYLJoWoAxReLpYYOF+GwfBgAACkXTAtSEBx4AAACTQdMCjKn/hCPZOS3mnBYAAFC2Nav9AtsPl/SLkp4gaTdJd0m6SdK3IuK6estDaVqtVuoSJibZOS3Vx7oX4pecVYnIKx9klRfyygdZlW3FTYvtgyS9SdJaSRdLukHS9yXtJGkPSSfZbks6JyLOnECtKEC73U5dQu1Sr2mZlBKzKhl55YOs8kJe+SCrsq2oabH965J2lvSWiLhvG2OfYfutkj4cEffUUCPQaP2T6JNPD+N0SQAAUKiVPmn5dkT8eCUDI+K7tuck7SmJpgXl669pSdW1DHYPS3R/AACACVvRQvyVNixD4x+IiJtGK+nBbLdtn2X7+7bX2352HdcF6rIwOFwy0Tkt/U9oWgAAQKG22bTYXmf7k7bfbXtn20+w/bZpFFf5kKQvRsTPS3qKpPVTvDewTf1eIfk5LXQtAACgUCt50vIiSa+X9M+STlRvp7DDJllUn+3dJD1X0qmSFBEbI6IzjXtjMjqdcuNLtw6/unPNPUvJWZWIvPJBVnkhr3yQVdlW0rRcJunAiFgfEe+VdLikaW3P8DhJt0j6B9vfs/0x27tM6d6YgG63m7qE2i0kXtOy3WR6liKzKhl55YOs8kJe+SCrsq1kIf6lkl4i6XxJioj/sL3q811GtEbSOklvjojzbH9I0smS/nTpwPn5+c2+uNVqLbv9XafTWfZ/2Ixn/Cjj77rrbknSPXffrd7u39OtZ9OmTZKkDbfcovntb6/9+oxnPOMZz3jGM57xdY4fhaOmY7RtvyEi/qaWiy1e82ckfSci9q9e/7KkkyPiRcPjZmdnY926dXXeGhMyPz+v/fbbL3UZtXrX7LX6xrUdve3I/XXY43af+v3f85/X6txrOjrliP10xOM3b5pGVWJWJSOvfJBVXsgrH2SVj7m5Oc3MzKxqjsqKdg9byvZv2Z6zfU3161pJfzHKtbam2oHsJ7YPqN6akXRF3fcBxjE4XDLR/d0/p4V1+AAAoFCjTvPaS9LhETGYi2L7nfWUtJk3S/qE7bWSrpH02xO6DzCi/pqWdEvxJc5pAQAA5Rq1abl+uGGpfG7cYpYTERdJevokro3pG3UeY5MtJH7Sst2EblxiViUjr3yQVV7IKx9kVbZRm5ZLbZ8h6UpJD6j397UXSPqFugpDmZZbqJW75Oe0VB8Xap4fVmJWJSOvfJBVXsgrH2RVtlGblj+Q9C1Jw1sCsM8cHpr6T1rSLWpJdGMAAIDpGLVp+UJEnDH8hu2La6gHyM7gnJZEE8T6u2mwpAUAAJRqpN3DJG2w/QLbj7X9s7Z/VtLr6ywMyE2y6WHVfVmIDwAASjXqk5YzJa1Xbz1L389L+t2xKwIyk3oh/uAJD3seAwCAQo36pOUPIuK5EXFE/5ekk+osDGXqdDqpS6hdDLY8TnP/wZOWmq9bYlYlI698kFVeyCsfZFW2kZqWiPinZd47c/xyULput9z9GlKtaemr+0FLyVmViLzyQVZ5Ia98kFXZRmpabL/O9t8NvX6x7WfVVxaQj4XEu4dN6pwWAACAphh1etiOkv66/yIiPifpgFoqAjITDVnTUvc5LQAAAE0xatOyU0R8b8l7jx+3GCBPade0JJ6VBgAAMHGj7h52l+3/J+mb6v2V6XD1dhQDHnIWdw9LdE4Lm4cBAIDCjdS0RMRf275U0q+p97TmXRHxjVorQ5FarVbqEiYm2e5h1ce6p4eVnFWJyCsfZJUX8soHWZVtxU2L7QMk3RERN0hSRHxTvSctwIq12+3UJdQu+TktE+qWSsyqZOSVD7LKC3nlg6zKtponLTdIOs72vpK+L+nsiNg4mbKAfCye05KmbVl80pLk9gAAABO34qYlIu6QdKok2X6ipLfYXiPpnIg4f0L1Ac2XeMvjftdCzwIAAEo16pqWH0h6v3v/tHyU7bdLulXSv0TETXUWCDRd6ulhgy0AWYkPAAAKNeruYZKkiAhJX5L0Jdst9aaP7S3pkoj4tzoKBHKRbCF+deOFNLcHAACYuFHPadlMRHQj4u8j4p2SLqnruihLp9NJXULt+rt2pdryeHDXmh+0lJhVycgrH2SVF/LKB1mVrbamxfYL+59HxDV1XRdl6Xa7qUuoXb9XSPek5cF11KXErEpGXvkgq7yQVz7Iqmy1NC22D5H0tjquBeQq8Tr82s9pAQAAaIqR17TYPlTSyyQdK6klaW1dRQE5GUwPS7ymBQAAoFSralpsP13Sy9VrVG6XdKOk4yLiUtuvmUB9QOPFYMtjzmkBAACYhBVPD7P9VUlflvSApJdGxDpJX4+ISyUpIk6bTIlAHpI97+CcFgAAULjVHC55hO1nS3qipINt36ahvyfZdrUFMrBFrVYrdQm1S35Oy2Alfr0/fiVmVTLyygdZ5YW88kFWZVvV9LCI+Lakb0uDqWJ32j5O0rWSTpH0a7VXiKK02+3UJUxA4jUt1ce6p4eVmVW5yCsfZJUX8soHWZVt5IX4EXGBpAskyfYzJT2prqKAnCw+aUm0poV1+AAAoHC1bHkcEedL+uM6rgXkJvk5LUvqAAAAKM1IT1psHyjpqoi43/bB6j1luaDWyoBcDHYPS3T/6sac0wIAAEo16pOW35V0ke1zJb1SvR3FDqurKCAng3NaEt1/8ENMzwIAAAo1UtMSEb8fEYdIeoWki9Q7t+VLdRaGMnU6ndQl1G5xeljaNS0LNV+3xKxKRl75IKu8kFc+yKpsY61piYibIuJfJB0v6Sn1lISSdbvd1CXULhJveTxQ8/SwErMqGXnlg6zyQl75IKuyjbqm5WuSbpb0X5LOl3SvpH1qrGvp/bZXb83M9RHx4kndBxhF6oX4/XNamB0GAABKNVLTEhGH2X6ypOdK+gNJvyxp3vZukr4YEZfVWKMknShpvaTdar4uUIP+mpZE08Oqj3Wf0wIAANAUI08Pi4hLIuIjEfHyiPgZSSdIukPSG2qrTpLtfSW9SNLH6rwuUJeFxLuHcU4LAAAo3TabFtuPtf2KbY2LiO9HxN9Keoft19dSXc9fSXqr6l9nDNQi9ZqWwTktbHkMAAAKtc3pYRFxrW3Zfr+kn0j6qqQrYuhvSLZ3kfRMSc+T9FP1Go2x2X6xpA0RcaHtw7c2dn5+frP3Wq2W2u32Zu93Op1lF2sxfvLjW61Wo+qpY/z9998vSbrjjtu19257Tr2ee++9V5LUvf32B/0cjHv9Vqs1lfoZX8948spnfD+rptTDePIqZfxwVk2oh/FbHj8Kr+ZfZ6t1LC+R9AxJO0naXtImSRskfU3S2RFx20iVLH+/90r6DUn3S3qYemtaPhMRrx4eNzs7G+vWravrtsCqvOpTl+mWuzbpn379YO2169qp3/8zl23Q33znev3qwXvqd5+979TvDwAAsBpzc3OamZlZ1SSVVS3Ej4hLJF2yqqrGEBGnSDpFkqonLX+0tGEBUku9exgL8QEAQOnGOqcFwNCalmQL8QerWtIUAAAAMGEjbXmcQkScK+ncxGUAm4nBlsdp0LIAAIDS8aQFGNfgSUuic1qq2zI9DAAAlGpVTYvtEyZVCB4aOp1O6hJqt9CQLY/rftRSYlYlI698kFVeyCsfZFW21T5p+T3bx9neayLVoHjLbX2Xu8FC/ET37z/hiZq7lhKzKhl55YOs8kJe+SCrsq22ablN0qskrbd9ue2P2D7W9iNsv24C9QHZSH0yPdPDAABAqVbbtLw7Io6R9EhJJ0j6saTXSbpW0gdqrg3IwkK1fViqNS3bJW6WAAAAJm2157R8o/q4IOmC6tef295B0l/UXx6Qj9RrWhZWcVAsAABATmrZPSwiNkn65zquBeRmoTHntAAAAJSpti2PI+K7dV0L5Wq1WqlLqF1E4nNa3K+j3uuWmFXJyCsfZJUX8soHWZWNc1owVe12O3UJE5PsnJbq40LN1y05qxKRVz7IKi/klQ+yKhtNCzCm5Oe09G/MmhYAAFAomhZgTINzWlKtaVH/nBYAAIAy0bQAY4rEWx73b8s5LQAAoFQ0LcCYBk9aEt2fvcMAAEDpaFowVZ1OJ3UJ9Uu+5XHvY93ntBSZVcHIKx9klRfyygdZlY2mBVPV7XZTl1C7hdRbHvfvXPP0sBKzKhl55YOs8kJe+SCrstG0AGNaXIifdk0LS1oAAECpaFqAmqRe08JCfAAAUCqaFmBMC8nXtAwOaklTAAAAwITRtABjiKHF76mftHC2JAAAKBVNC6aq1WqlLqFWw31C6jUtCzVft7SsSkde+SCrvJBXPsiqbDQtmKp2u526hFr1n26kPCtlUrPDSsuqdOSVD7LKC3nlg6zKRtMC1CDVehZpccvjus9pAQAAaAqaFmAMqc9okdI2TAAAANNA0wKMIfUZLdLQQvxkFQAAAEwWTQswjsTbHQ/fm+lhAACgVDQtmKpOp5O6hFr1d+xKOj1sQncvLavSkVc+yCov5JUPsiobTQumqtvtpi6hVtGgNS11P2gpLavSkVc+yCov5JUPsiobTQswhsGWxw1Y07LA7DAAAFAomhZgDIsL8dPVsHhvuhYAAFAmmhagBk1Y08KTFgAAUKpGNy22H2P7q7bX277c9ompawKGDc5pSTk9jHNaAABA4dakLmAb7pf0hxExZ3tXSRfaPicirkhdGEbTarVSl1CrwZqWhDUMzmmp+UlLaVmVjrzyQVZ5Ia98kFXZGv2kJSJujIi56vM7JK2XtE/aqjCOdruduoRaNWNNSzU9rOY1LaVlVTryygdZ5YW88kFWZWt00zLM9v6SnirpvLSVAIuatOUx6/ABAECpsmhabD9c0r9KOikibk9dD9C3+KSFLY8BAAAmpelrWmR7B/Ualk9ExGe2NG5+fn6z91qt1rKPCjudzrIHEDGe8asd371vQZK0sPCAOp1OknruuvNOSdJ99933oJ+DJvz3YTzjGc94xjOe8YxfOn4UjrpX79bIvX++/rikWyPipC2Nm52djXXr1k2vMKDy07s36fhPXqbdd1qjM1/1pCQ1XHHzXTrpP36gAx+1sz700gOS1AAAALBSc3NzmpmZWdU0laZPD3uOpN+QdKTti6pfL0xdFEbX6XRSl1Cv/u5hDThcsu7pYcVlVTjyygdZ5YW88kFWZWt00xIR34wIR8STI+LQ6tfnU9eF0S33mDBn/R27nHAp/qTuXFpWpSOvfJBVXsgrH2RVtkY3LUDTNeKclurmDZ7pCQAAMBaaFmAM0YjpYdU5LXQtAACgUDQtwBiiPz0sZdOS7tYAAABTQdMCjGFwTksD1rRwTgsAACgVTQumatS9uZuqGdPD+p/V27WUllXpyCsfZJUX8soHWZWNpgVTtdzhQzlrxEL86u51L2kpLavSkVc+yCov5JUPsiobTQswhsU1LQmnh/XPaUlWAQAAwGTRtABjaMSTlv7NWdMCAAAKRdMCjGGwEL8Bu4fRswAAgFLRtABjiOpRS9onLZzTAgAAykbTgqnqdDqpS6gBs4ZYAAAMsklEQVTV4pOW9Fse1620rEpHXvkgq7yQVz7Iqmw0LZiqbrebuoRaNWnL47rPaSktq9KRVz7IKi/klQ+yKhtNCzCGRizEZ1ULAAAoHE0LMIbBlscJa+g/aWFJCwAAKBVNCzCGxelhDTinhaYFAAAUiqYFGEOTtjwGAAAoFU0LpqrVaqUuoVaDpiVhDf01LVHzmpbSsiodeeWDrPJCXvkgq7LRtGCq2u126hJqNTinpcDdw0rLqnTklQ+yygt55YOsykbTAoxhcfew9Gta2DwMAACUiqYFGEOT1rQs0LUAAIBC0bQAY2jEOS2DPY8TFgEAADBBNC3AGAbntDTgSQs9CwAAKBVNC6aq0+mkLqFWTVrTUvdC/NKyKh155YOs8kJe+SCrstG0YKq63W7qEmrVpDUtdSstq9KRVz7IKi/klQ+yKhtNCzCGJq1p6W+/DAAAUBqaFmAMi+e0JJwe1q8lWQUAAACTRdMCjGEwPSxhDYPNw+haAABAoWhagDEMpocl7Fq2q26+QNcCAAAKRdOCqWq1WqlLqFUTtjyelNKyKh155YOs8kJe+SCrstG0YKra7XbqEmrViIX41ce6n7OUllXpyCsfZJUX8soHWZWNpgUYw+KWx+Wd0wIAANAUjW9abB9t+0rbV9s+OXU9wLBGPGlhJT4AAChco5sW29tL+qikF0g6SNLxtg9KWxWwqAlrWtjyGAAAlK7RTYukZ0q6OiKuiYiNks6QdEzimoCBxSct6aeH8aAFAACUak3qArZhH0k/GXp9naRnLTfwro0PTKUgjKfb7Ra1u8c9mxYkNeNJy0JErT8HpWVVOvLKB1nlhbzyQVZla3rTstxfBZf99+Rf/cdLJlwKsGUp17T0z2l5IPg5AAAAzfe+dav/mqY3LddJeszQ630l3bDcwJ3WLP/XxuV2dYqtzKNh/GTHb21sinrqGL+dpQN2fUCdTmfZ7RY7nY663e5m77darVrG33F7V0/ba62u+Ommkerf0viIkO3G//dn/OI48spjfD+rptTD+K2PJ698xg9n1YR6GL+1v/Otfk67t/WXyJRsr5H0A0kzkq6X9F1Jr4yIy4fHzc7Oxrp1I7RsmLr5+Xntt99+qcvACpBVXsgrH2SVF/LKB1nlY25uTjMzM6uaqNLoJy0Rcb/tN0n6kqTtJZ22tGEBAAAAULZGNy2SFBGfl/T51HUAAAAASKPpWx6jMOzqkQ+yygt55YOs8kJe+SCrstG0YKqWW0iOZiKrvJBXPsgqL+SVD7IqG00LAAAAgEajaQEAAADQaDQtAAAAABqNpgUAAABAo9G0YKo6nU7qErBCZJUX8soHWeWFvPJBVmWjacFUdbvd1CVghcgqL+SVD7LKC3nlg6zKRtMCAAAAoNFoWgAAAAA0Gk0LAAAAgEajaQEAAADQaDQtmKpWq5W6BKwQWeWFvPJBVnkhr3yQVdloWjBV7XY7dQlYIbLKC3nlg6zyQl75IKuy0bQAAAAAaDSaFgAAAACNRtMCAAAAoNFoWgAAAAA0Gk0LpqrT6aQuAStEVnkhr3yQVV7IKx9kVTaaFkxVt9tNXQJWiKzyQl75IKu8kFc+yKpsNC0AAAAAGo2mBQAAAECj0bQAAAAAaDSaFgAAAACNRtOCqWq1WqlLwAqRVV7IKx9klRfyygdZlY2mBVPVbrdTl4AVIqu8kFc+yCov5JUPsiobTQsAAACARqNpAQAAANBoNC0AAAAAGo2mBQAAAECj0bRgqjqdTuoSsEJklRfyygdZ5YW88kFWZWts02L7A7a/b/sS22fbZkuIAnS73dQlYIXIKi/klQ+yygt55YOsytbYpkXSOZIOiYgnS/qBpFMS1wMAAAAggcY2LRHx5Yi4v3r5HUn7pqwHAAAAQBqNbVqWeI2kL6QuAgAAAMD0rUl5c9tfkfQzy/zW2yLi36sxb5N0v6RPTLM2AAAAAM3giEhdwxbZPkHSGyTNRMTdWxo3Ozvb3G8CAAAAwIPMzMx4NeMb27TYPlrSByUdFhG3pK4HAAAAQBpNblqulrSjpJ9Wb30nIt6QsCQAAAAACTS2aQEAAAAAKZ/dw5Zl+2jbV9q+2vbJqevBg9k+zfYG25cNvbeH7XNsX1V93D1ljeix/RjbX7W93vbltk+s3ievhrH9MNvn2764yurPqvcfa/u8Kqszba9NXSt6bG9v+3u2P1e9JquGsv0j25favsj2BdV7/DnYQLbbts+qDiJfb/vZZNVMtg+ofqb6v263fdJq88q2abG9vaSPSnqBpIMkHW/7oLRVYYnTJR295L2TJc1GxBMkzVavkd79kv4wIg6U9AuS3lj9PJFX89wn6ciIeIqkQyUdbfsXJL1f0l9WWd0m6bUJa8SDnShp/dBrsmq2IyLi0Ih4evWaPweb6UOSvhgRPy/pKer9jJFVA0XEldXP1KGSnibpbklna5V5Zdu0SHqmpKsj4pqI2CjpDEnHJK4JQyLi65JuXfL2MZI+Xn3+cUm/MtWisKyIuDEi5qrP71DvD/99RF6NEz13Vi93qH6FpCMlnVW9T1YNYXtfSS+S9LHqtUVWueHPwYaxvZuk50o6VZIiYmNEdERWOZiR9MOImNcq88q5adlH0k+GXl9XvYdm2ysibpR6f1GW9KjE9WAJ2/tLeqqk80RejVRNN7pI0gZJ50j6oaRORNxfDeHPw+b4K0lvlbRQvX6EyKrJQtKXbV9o+3eq9/hzsHkeJ+kWSf9QTb38mO1dRFY5eIWkT1WfryqvnJuW5fZ2ZlcBYAy2Hy7pXyWdFBG3p64Hy4uIB6rH7Puq99T5wOWGTbcqLGX7xZI2RMSFw28vM5SsmuM5EbFOvannb7T93NQFYVlrJK2T9NcR8VRJd4mpYI1Xrd97qaR/GeXrc25arpP0mKHX+0q6IVEtWLmbbe8tSdXHDYnrQcX2Duo1LJ+IiM9Ub5NXg1XTIc5Vbx1S2/aa6rf487AZniPppbZ/pN4U5iPVe/JCVg0VETdUHzeoN+f+meLPwSa6TtJ1EXFe9fos9ZoYsmq2F0iai4ibq9eryivnpuW7kp5Q7cKyVr3HTZ9NXBO27bOSTqg+P0HSvyesBZVqnv2pktZHxAeHfou8Gsb2nrbb1ec7SXqeemuQvirpZdUwsmqAiDglIvaNiP3V+/+o/4yIV4msGsn2LrZ37X8u6ShJl4k/BxsnIm6S9BPbB1RvzUi6QmTVdMdrcWqYtMq8sj6nxfYL1ftXq+0lnRYR705cEobY/pSkwyU9UtLNkt4u6d8kfVrSz0r6saSXR8TSxfqYMtu/JOkbki7V4tz7P1FvXQt5NYjtJ6u3YHF79f7h6dMR8U7bj1PvX/P3kPQ9Sa+OiPvSVYphtg+X9EcR8WKyaqYql7Orl2skfTIi3m37EeLPwcaxfah6G1yslXSNpN9W9WeiyKpxbO+s3lr0x0VEt3pvVT9bWTctAAAAAMqX8/QwAAAAAA8BNC0AAAAAGo2mBQAAAECj0bQAAAAAaDSaFgAAAACNRtMCAAAAoNFoWgAAAAA0Gk0LAAAAgEajaQEAJGP7vbZPqvma59s+uM5rAgDSckSkrgEA8BBke09JF0n6uYi4p8brHifp1yPi2LquCQBIiyctAIBUfkvS5+tsWCqflXSE7b1rvi4AIBGaFgDAxNj+T9trt/DbL5D0taGxP7L9v2xfYvsu26fa3sv2F2zfYfsrtncfGv/Htq+vfu9K2zOSFBH3SrpQ0lGT/N4AANND0wIAmAjb+6g3DXnjFoY8SdKVS947VtLzJT1R0kskfUHSn0h6pHr/n/X71bUPkPQmSc+IiF0l/Q9JPxq6znpJT6nlGwEAJEfTAgCone3nS/pLSTfZ/s0tDGtLumPJex+OiJsj4npJ35B0XkR8LyLuk3S2pKdW4x6QtKOkg2zvEBE/iogfDl3njur6AIAC0LQAAGoXEeeo11h8MCL+cQvDbpO065L3bh76/J5lXj+8uv7Vkk6S9A5JG2yfYfvRQ2N3ldQZ+RsAADQKTQsAoHa2rd5TkbmtDLtEvWlgI4mIT0bEL0naT1JIev/Qbx8o6eJRrw0AaBaaFgDAJBwkaX1EPGD7mC2M+bykw0a5uO0DbB9pe0dJ96r3FOaB6vd2lPQ0SeeMcm0AQPPQtAAAJuFWSV3br5T0rS2M+UdJL7S90wjX31HS+yT9t6SbJD1KvQX7kvRSSedGxA0jXBcA0EAcLgkASMb2eyRtiIi/qvGa50l6bURcVtc1AQBp0bQAAAAAaDSmhwEAAABoNJoWAAAAAI1G0wIAAACg0WhaAAAAADQaTQsAAACARqNpAQAAANBoNC0AAAAAGo2mBQAAAECj/X+yN18gdvJbywAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"a,b,c,d,e,t = run_simulation()\n",
"\n",
"plot_membrane_potential(Time = t, Voltage = a)\n",
"plot_current(Time = t, Current = e)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x2574e018c88>"
]
},
"execution_count": 231,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize = [12,5])\n",
"colors = ['#a6bddb','#74a9cf','#2b8cbe','#045a8d']\n",
"for color, G_Na in zip(colors,[20,70, 120, 170]):\n",
" V_membrane,_,_,_,_,Time = run_simulation(G_Na = G_Na)\n",
" ax.plot(Time, V_membrane, color = color, label = f\"{G_Na}\")\n",
" \n",
" \n",
"ax.set_xlabel('$t$ (ms)')\n",
"ax.set_ylabel('$V$ (mV)')\n",
"ax.set_ylim(-20, 120)\n",
"title = plt.title('Membrane Potential $V$')\n",
"ax.legend(title = \"G_Na in mS/cm**2\" )"
]
},
{
"cell_type": "code",
"execution_count": 230,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x2574fc11048>"
]
},
"execution_count": 230,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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nnAkyuMli+fLldtGiRWEvQ0REREQiaMOGDcyZMyfsZYyLp556imXLlvG9730v7KVUVO1rv2LFChYvXlx531sV2k4moUqlUmEvQUZIsXKHYuUWxcsdipU7pmqs3v3ud0c2gRlrSmIkVB0dHWEvQUZIsXKHYuUWxcsdipU7xjJWy5cv55hjjhnw59xzzx2z95edo54YEREREZEqFi9ePOyoZZl4qsSIiIiIiIhTlMSIiIiIiIhTlMSIOGJHT45Vr3fTly2EvRQRERGRUCmJkVAlk8mwl+CMVzb3snZrmr+82hnKx1es3KFYuUXxcodi5Q7FavJTEiOham1tDXsJzmjvygKQ6s6F8vEVK3coVm5RvNyhWLlDsZr8lMSIOGJaQ/8wwUxOW8pERERksDVr1rDHHnvw2c9+tnjt7rvv5pBDDmGvvfbi4x//ONu3bw9xhWNDSYyIg8KqxoiIiEi0ff3rX+ewww4rPl61ahVf/epXuemmm1i9ejUNDQ1cfPHFIa5wbCiJEXFEwdriv4OtZSIiIiKBX/7ylySTSY455pjitbvvvpv3ve99vOc976G5uZnLLruMe++9l87OcHpsx4oOuxRxRKFkB1mHKjEiIiKR88Bftu3ye5x82Iydet2OHTu45ppr+PWvf82dd95ZvL569WoOP/zw4uN58+aRSCRYs2YNb3/723d5vWFRJUZClUqlwl6CM0orMT2Z/IR/fMXKHYqVWxQvdyhW7piKsbr66qv5+Mc/zl577TXgend3Ny0tLQOutbS00NXVNZHLG3OqxEioOjo6NEFkhEpyGPqylnzBEo+ZCfv4ipU7FCu3KF7uUKzcEVasdraKsquef/55HnvsMR577LFBzzU1NQ3aOtbZ2Ulzc/NELW9chJbEGGNuBU4FtlhrF/rX2oBfAHOB14CPWGu3G2MM8G/APwI9wBJr7Yow1i0SlkLBDnjcmynQXB8nX7Bk8gUaauMhrUxERETC9OSTT7J+/XoOOeQQwKu+5PN5XnzxRRYvXszKlSuL97722mv09fUxf/78sJY7JsLcTnYbcHLZtUuB5dba/YDl/mOA9wP7+X8uAP73BK1RJDKCHKaxzvu27e3ztpTd8fQm/v3xN+jqU5+MiIjIVHTeeefxzDPPFKsxS5Ys4cQTT+Tuu+/mjDPO4IEHHuCPf/wj3d3dfPvb3+bUU09l2rRpYS97l4SWxFhrHwfayy6fBtzu//t24PSS63dYz1NAqzFmj4lZqUg0BD0xzfVexaU343X6b9yRIVew/H1Lb2hrExERkfA0NjYya9as4p+mpibq6+uZOXMmBx54IN/73ve44IILOOCAA+jq6uK6664Le8m7LGo9MbOstRsBrLUbjTG7+9f3BNaX3Pe6f23jBK9PJDTBdLKmujiQpSeTx5Y0yqzdnmbR3m7/VkVERER23aWXXjrg8RlnnMEZZ5wR0mrGhyvTySp1L9sK18QxyWQy7CU4I6jENJVUYvIl3wWbOzPj+vEVK3coVm5RvNyhWLlDsZr8olaJ2WyM2cOvwuwBbPGvvw7sXXLfXsCGam+ydu3aQdeSyWTFKRWpVIqOjg7dH9L9oHiN5H5roWC9KSKd27cCjfT25cmXNPvvSOfZvn07O3bsGJf1tLa2Rvbro/sH3h/8Oyrr0f1D3694uXN/6euisB7dX/3+8teU3p/PT/wxBeJJp9MDfu4b6ufD4ZjS7SgTzRgzF7i3ZDrZtcA2a+01xphLgTZr7SXGmFOAL+JNJ3sX8ENr7eGV3nP58uV20aJFE7J+kYlSsJbfPduOAY5dOJ1HVm6nJm44YkGSf3vs9eJ9F713LxoTmlImIiJSzYYNG5gzZ07Yy5iSqn3tV6xYweLFi0d1bkSYI5Z/DhwLzDTGvA5cDlwDLDPGfApYB5zp334fXgLzMt6I5fMnfMEiIQr6YWIxSNQY4jHI5S3pskMvd6TzSmJERERk0gstibHWfqzKU4sr3GuBpeO7IpHoCvphYsZgjKEhEacrnae7rzDgvh3pHLNbEmEsUURERGTCuNLYLzKlBa0vMb/Q2pDwvnW7MwPPhtmR1lkxIiIiMvkpiZFQpVKpsJfghIKfxcT8LKahztsy1pMur8SMX7OiYuUOxcotipc7FCt3KFaTn5IYCVWlySIyWFCJMX4lptGvxPSU9cR0jGMlRrFyh2LlFsXLHYqVOxSryU9JjIgDipUYP4tp8Jv3e7NeJSYY59E5jpUYERERkahQEiPigGJjv/8d21jn/aM34yUx0xu9GR3jWYkRERGRaFu3bh0f+chHmDdvHgsWLOCSSy4hl/N+Nnj++ec57rjj2HPPPTnuuON4/vnnQ17trlESI+IAW2zsH1iJSWeDJKYWgK6yAzBFRERk6rj44ovZbbfdWLVqFY899hh/+MMf+PGPf0wmk+Gcc87hzDPP5JVXXuGss87inHPOIZPJhL3knaYkRsQB5dPJauKG2rgh72c3ibihuS6OxUtkyr2wpZt7XtxGmIfbioiIyPhau3Ytp59+OvX19cyaNYvFixezevVqnnzySfL5PJ///Oepq6vjs5/9LNZaHn/88bCXvNNCOydGBCCZTIa9BCeUTycDaKiLYbu9f8djhmR9nK6+PDvSOZINA7+1P3fvSwBMq4tz7NzWnVqDYuUOxcotipc7FCt3hBWrqx9au8vvcdmJ++z0az/3uc/xq1/9iiOPPJJUKsXvf/97LrvsMlavXs1BBx2EMf0/Rxx88MGsXr2aE044YZfXHAZVYiRUra079wP1VNN/2GX/tcaEV3kBqIkZWuq9xGWoMcuPvbbzIycVK3coVm5RvNyhWLljqsbqPe95D6tXr2afffZh4cKFHHbYYZxyyil0d3fT0tIy4N6Wlha6urpCWumuUyVGxAGFsp4Y8A68DJKbeEkSM1Rz/wtbesZvkSIiIlPcrlRRdlWhUOCMM85gyZIlPPDAA3R3d/OlL32JK664glmzZtHZ2Tng/s7OTpqbm0Na7a5TJUbEAf0jlvuvNdTFiw3/XiXGa/bfMUQSs7UnO25rFBERkfBs376dN954g8985jPU1dXR1tbG2WefzUMPPcSCBQt44YUXBvTGvvDCCyxYsCDEFe8aJTEiDihWYkqymMZEjIL/73iMIbeT1fqvyxWsppeJiIhMQjNmzGCfffbh1ltvJZfL0dHRwV133cXChQs56qijiMfj3HzzzfT19XHLLbcAcMwxx4S86p2nJEbEAZV6YuoTseJvVIarxBRKfvOyXWfJiIiITEp33HEHy5cvZ7/99uMf/uEfiMfjXHXVVSQSCe68805+8YtfMG/ePH76059y5513kkgkwl7yTlNPjIQqlUpN2ea70Sj4JZfSnpi62pJKjKne2J8rWPIlxZdtPVlm+ufKjIZi5Q7Fyi2KlzsUK3dM1Vi97W1v45577qn43CGHHMIjjzwywSsaP6rESKg6OjrCXoITipWYklJMTcm/jYHG2hgxA+lcgVzJlrFsvkCpN3eyL0axcodi5RbFyx2KlTsUq8lPSYyIA8oPuwQwxhQf24L3uLHW21LWk+mvxmTyA3tgdjaJEREREYkKJTEiDgimk5UeUuU99p/3KzVNdd63dPeAJGZsKjEiIiIiUaEkRsQB/dPJyp7ws5igZ6Yp4VViuvv6ExdVYkRERGSyURIj4oD+6WQDKzEElRg/y2kMkpght5NpOpmIiIi4TUmMhCqZTIa9BCf0Tyer/Hy+rBLTM8R2sm07WYlRrNyhWLlF8XKHYuUOxWryUxIjoZqK4w93RqXpZABBjSXvV1uaEpV6YrznZjd7Y5V3djuZYuUOxcotipc7FCt3KFaTn5IYEQfYCtPJ/GcAyBWTmErbybxKzKymBDHjHXZZOoJZRERExDVKYkQcUK0nJshF8oOSmMGN/Q21Mab7B2Lu7JYyERERkShQEiPigGo9MUGFJpcvYK2lqa56JaY2FmNm465tKRMREZFouuWWWzj++OOZPXs2S5cuLV5/+umn+dCHPsS+++7Lfvvtx5IlS9i0aVPxeWstV1xxBfPnz2f+/PlcfvnlWBv9HRtKYkQcUK0nprgtzBgyOUtjbfWemETcFJMYVWJEREQml9mzZ/O1r32Nc845Z8D1VCrFeeedx7PPPstzzz1Hc3MzX/ziF4vP33777dx33308/vjjPPHEEzz44IPcdtttE7z60VMSI6FKpVJhL8EJhSo9MfkguQHS2UJxxHJvplBMfIJKTKJm1yoxipU7FCu3KF7uUKzcMRVj9YEPfIBTTjmFtra2AddPPPFETj/9dFpaWmhsbOQzn/kMf/7zn4vP//znP+cLX/gCe+65J3PmzGHp0qX87Gc/m+jlj1pN2AuQqa2jo0MTREYgOAemWiXGGOjLFkg21tBQG6M3W6A3W6ApER9QiZnRECQxoz8rRrFyh2LlFsXLHYqVO8KK1dG3PrvL7/HEJ98+Biup7r//+7854IADio9Xr17NwoULi48XLlzIiy++OK5rGAtKYkQcULUSEyQxGNJZr+LSmIjTmy3Q3ZenKRGnL6jExEsqMb3aTiYiIjLVvPDCC1x77bX89Kc/LV7r7u6mpaWl+LilpYWuri6stZjyQ7YjREmMiAOqTScLKjExvxID3lkx27r7+2KyJZWY3dTYLyIiMm7Gu4qyK1555RU+8pGP8O1vf5sjjjiieL2pqYnOzs7i487OTpqbmyOdwIB6YkScUJxOVvIda63FL7JgKE1ivL6YHn/MctATU1dSiVFjv4iIyNSxfv16PvShD3HxxRfz0Y9+dMBzCxYsYOXKlcXHK1euHLDdLKqUxIg4oFIlJl+yxcyY/u1k5QdelvbEtDX658RoO5mIiMikksvlSKfT5PN58vk86XSaXC7Hhg0bOO200/jUpz7F+eefP+h1Z511FjfeeCMbNmxg48aN3HDDDZx99tkhfAajE8ntZMaYrwCfxjuO/HngfGAP4C6gDVgBnGutzYS2SBkTyWQy7CU4oWSSclHQDxP3L/ZlqiUx/T0xLXU1GGBHOk+uYKkpb7IZgmLlDsXKLYqXOxQrd0zFWF133XV85zvfKT5etmwZl1xyCcYYXnvtNa699lquvfba4vPr168HYMmSJbz22mscddRRAJx77rksWbJkQte+MyKXxBhj9gS+DBxkre01xiwDzgL+Efi+tfYuY8xNwKeA/x3iUmUMaMrLyBSnk5VkMUE/TE3cu5bO9ffEQOVKTE3MkKyvIZXO0ZHOMcPfXjYSipU7FCu3KF7uUKzcMRVjdemll3LppZdWfO4b3/hG1dcZY7jyyiu58sorx2tp4yKq28lqgAZjTA3QCGwEjgfu9p+/HTg9pLWJTLjidLKS79igElMTMxggm7MUCrZ4Vkx3WU9MIu69uK3B+93F9t7Rj1kWERERiYLIJTHW2jeA64B1eMlLB/AMkLLWBj91vQ7sGc4KRSZW0A8DlSsx8Zihrtb7Vk5nCzTVBY39gysxAK31fhKTVhIjIiIibopcEmOMmQ6cBswD5gBNwPsr3GorXBOZdIqTyaqcEVNTksT0+QdcwuCemFo/iQkqMe1q7hcRERFHRa4nBjgBeNVauxXAGPMr4D1AqzGmxq/G7AVsqPYGa9euHXQtmUxW3B+ZSqXo6OjQ/bo/sve3b+8AmgFb/N92MpkkF2sAvEpMfW2MDqC9owsyOwDo6svz2mtr6ejqAbwRywDTG7w+mO29uUh+vrpf9+t+3a/7df943p/P5wc9LxMjnU4P+Dm9WrxGwlgbrYKGMeZdwK3AO4Fe4Dbg/wLHAL8saez/q7X2xvLXL1++3C5atGgCVyy7IpVKTcnmu9HoyxZ4ZOV2EjWG49/WVrz+eirNHU9vZs9kgnfuMY11b/axYM9G5u7ewHUPryOTt3z12L25+KE1PLepmx++fz6H7TGN/3xuMz96ZiNnv213Pv/OOSNeh2LlDsXKLYqXOxQrdwwVqw0bNjBnzsj//0/GTrWv/YoVK1i8ePGoTteM3HYya+2f8Br4V+CNV44BPwK+AXzVGPMyMAP4cWiLlDFT6bcoMlClM2IA/GFkA3piyg+87M7kyRZ7YgY29o92O5li5Q7Fyi2KlzsUK3coVpNfFLeTYa29HLi87PIrwOEhLEcK8UTJAAAgAElEQVQkVMWemLJfOZT2xNSXNPYDNCbibO/N0Z3Jl0wn85Kg6ZpOJiIiIo6LXCVGRAaqXokZPJ2svxLjPe7J5Eumk/k9MfV+T4ymk4mIiIijlMSIRFzxjJgq08niFSoxTSVnxZRXYnROjIiIiLhOSYxIxBX8ZCVWlsVUG7FsbemBl4MrMaXnxERtsIeIiIjsnFtuuYXjjz+e2bNns3Tp0gHP9fT0cPHFF/PWt76VffbZh1NOOaX4nLWWK664gvnz5zN//nwuv/xyJ34+iGRPjEwdyWQy7CVEXrVKTK4kiamJG+IxyBe85CY48LJST0xdTYym2hjd2QJdmTzT6kb2nwHFyh2KlVsUL3coVu6YirGaPXs2X/va13j44YdJp9MDnvvKV75CLpfjqaeeYvr06Tz//PPF526//Xbuu+8+Hn/8cYwxfPjDH2bu3Lmcf/75E/0pjIoqMRIqjaoc3kh6Yozpr8aks4ViT0ylSgz0nxXTPootZYqVOxQrtyhe7lCs3DEVY/WBD3yAU045hba2tgHXX3rpJe6//36+//3vM3PmTOLxOG9/+9uLz//85z/nC1/4AnvuuSdz5sxh6dKl/OxnP5vo5Y+aKjEiERdMJzNVemJq/BJNXW2Mnr4CfVnb3xOTLk1i+t+graGG13f00d6bY5+p9995ERGRcbH7F3+yy++x5fqxrYA888wz7L333lxzzTUsW7aMWbNm8Y1vfIMPfvCDAKxevZqFCxcW71+4cCEvvvjimK5hPKgSIxJxxUpMrFolxntcbO7PFIpJTGefdypxrV+tCUz3+2JSmlAmIiIyqW3YsIFVq1bR0tLC3/72N77zne+wdOnSYqLS3d1NS0tL8f6Wlha6uroi3xejSoxIxFWfTub9HTf9lRjwmvtntHjbxTr7vCSltAoD/WfFjPbASxEREalurKsoY6G+vp7a2louvvhiampqOPLIIznqqKN45JFHOOCAA2hqaqKzs7N4f2dnJ83NzQN++RlFqsSIRFxxOpmpMp3MT1BKxyzX1RjiBtIV+mEA2vyeGI1ZFhERmdwOPvjgIZ9fsGABK1euLD5euXIlBxxwwHgva5cpiZFQpVKpsJcQecVKTNl3a2ljPwysxBhjaEzEi1vRyisxpWOWR0qxcodi5RbFyx2KlTumYqxyuRzpdJp8Pk8+nyedTpPL5XjPe97DXnvtxfe///3ihLI//OEPHH/88QCcddZZ3HjjjWzYsIGNGzdyww03cPbZZ4f82QxPSYyEqqOjI+wlRN5w08lqzOBKDHgHXvqFmAqVmNFvJ1Os3KFYuUXxcodi5Y6pGKvrrruOOXPm8IMf/IBly5YxZ84crrvuOmpra7nzzjt56KGHmDt3LhdddBE33ngj+++/PwBLlizh5JNP5qijjuLII4/kpJNOYsmSJeF+MiOgnhiRiAumkw3uialeiQFoSsSqVmKCnhhtJxMREZkcLr30Ui699NKKzx144IH87ne/q/icMYYrr7ySK6+8cjyXN+ZUiRGJODvMdLKaCkmMtd6Bl4UqlZjp9X5PjKaTiYiIiIOUxIhEXPXpZAMrMfGYoTZusEAmZ/3tZH4lpmbgi9tUiRERERGHKYkRibhhp5OVfBeXVmO8xn7venklprE2RiJu6M0V6M3mx2nlIiIiIuNDSYyEKplMhr2EyOs/7HLg9fLpZDCwub8pEaOAd09dWU+MMaZ44OVIt5QpVu5QrNyieLlDsXKHYjX5KYmRULW2toa9hMjr305WXonx/q4pSWJKKzFNQ1RiYPRnxShW7lCs3KJ4uUOxcodiNfkpiRGJuP7tZAOvD1+JKemJiQ8+dbe1OGZZfTEiIjJ1xONxenp6wl7GlGKtZdu2bdTV1Y3Ze2rEskjE9R92WVaJsQOnkwHUJSr3xNSW70UD2nbiwEsRERHX7b777mzZsmVKHogZFmstyWSS5ubmMXtPJTEiERf0xJjySkx+6EpMY23/OTG1FSox/WfFjPzASxEREdcZY5g1a1bYy5BdpO1kIhFXtSemUiWmpCcmFjPFBKfSN/p0bScTERERRymJkVCplDu80fTE1JVUYsqfK9fmH3iZGuF2MsXKHYqVWxQvdyhW7lCsJj8lMRKqjo6OsJcQeVV7YgoVKjE1BgNkc5ZCwQ4ay1yqvxIzsu1kipU7FCu3KF7uUKzcoVhNfkpiRCKueE5M1UpM/zVjDIlgS1muUHyNDTKhEv09MdpOJiIiIm5REiMScQX/PJjSnhhrbcVzYgDqa73H6UwB47+mQg7DdH87maaTiYiIiGuUxIhEXLESU5Ks5IvN/hQTlUBpc3/xPQoMkqyPEzOwoy9frOqIiIiIuEBJjEjEFUoSlkClfphA6ZhlW3Z/qZgxtNZrzLKIiIi4R0mMhCqZTIa9hMizFaaTVZpMFiitxNji/RVKMcD0URx4qVi5Q7Fyi+LlDsXKHYrV5KckRkLV2toa9hIir9J0svwIkph0tlDcipbLV37vtga/L2YEzf2KlTsUK7coXu5QrNyhWE1+SmJEIq5/OtngJGao7WR92UIxAcrkK1diWnXgpYiIiDhISYxIxPVPJ+u/Vmm8cqC0EpP3E6Bs3mLt4L6YtlFsJxMRERGJikgmMcaYVmPM3caY1caYVcaYI4wxbcaYh4wxL/l/Tw97nSLjzVpb7GspHUIWVFjiZuhKTNa/0QDp3OBqTP9ZMWrsFxEREXdEMokB/g14wFq7ADgUWAVcCiy31u4HLPcfi0xqhSqjlIdq7K+JG2IG8gXvwEvv9YbuvupJjLaTiYiIiEsil8QYY1qAY4AfA1hrM9baFHAacLt/2+3A6eGsUMZSKpUKewmRVigM7oeBoRv7jTH91Zh8/2Sz7szg7v62URx4qVi5Q7Fyi+LlDsXKHYrV5Be5JAbYF9gK/MQY8xdjzH8YY5qAWdbajQD+37uHuUgZGx0dHWEvIdKCSowp+04dKokBqEt4L8j4lZg4pmISM5rtZIqVOxQrtyhe7lCs3KFYTX41YS+gghpgEfAla+2fjDH/xii3jq1du3bQtWQyWXHcXiqVqvg/dN0/Mfen02nFa6j7/XttPj/g69RhGwGoKcthgvfPZ+qAWvr82crVKjHxTA8AW7v6Bry/M18f3a/7db/u1/26X/dPuvtHwlSaWBQmY8xs4Clr7Vz/8dF4ScxbgWOttRuNMXsAj1prDyh//fLly+2iRYsmcsmyC9auXcs+++wT9jIiq7svzxN/S9GQiPHeg/tnWaze3MOv/rqV/Xdr4Iy3Dy5Krn6jm9e2pLn2r6+TyVtO3LOV985v5b1vHfgfimy+wPG3/5W4gYeXHDpo21opxcodipVbFC93KFbuUKzcsmLFChYvXlz9h5AKRlyJMcbMAk7Ca7RvBVLAc8BD1tpNo/mgQ7HWbjLGrDfGHGCtfRFYDPzN/3MecI3/92/G6mOKRFWxJ6Zs21gwOrnqdrLaGNZassP0xNTGY0xLxOnM5NnRl6e1PorFWREREZGBhu2JMcYcaIy5Gy+JOBeoBTb5f58LvOCPQz5oDNf1JeCnxpi/Am8HrsZLXk40xrwEnOg/FpnUSqeTlRquJ6a+NkYBsEDc+NPJKiQxoDHLIiIi4p6R/Nr1NuA64BxrbV/5k8aYBN7ksB8DR4zFoqy1zwLvqPDU4rF4f4mOZDIZ9hIirWCHnk5WM0QlJhjDXOufiNkzRBKzrqOP7ekc84ZYi2LlDsXKLYqXOxQrdyhWk9+wSYy19l3GmJi1dvAhE97zGeD/+H9ERmVnm7mmioL/XRerMp2sSg5DfW2suOUsEfdu6s5U/BYujlke7qwYxcodipVbFC93KFbuUKwmv5GOWH7DGPMdY8zbxnU1IjJA9UqM9/dIKjHFJKZvuO1kOvBSRERE3DDSJOZzwDzgz8aYFcaYC40xu43jukQEsFV6YnLDNPbHYwb8pxKxGDUxQ7ZgyeQHV2OUxIiIiIhrRpTEWGt/Y609E9gDuBk4E1hvjPmtMeafjDG147lIkamqOJ2sSk9MtSQGIOZXYGrjhkb/8MtK1Zg2fyJZe1qN/SIiIuKGkVZiALDWpqy1N1trjwIOBP4v8H1g43gsTmSqK04nq9ITM2QS4z9XYwxNiThQuS+mVZUYERERccyokpiAMaYOeCfwLmAW8PxYLkqmjlQqFfYSIi0/TCWmZojDKY3/3R2P9ScxlSaUtTV4hdThkhjFyh2KlVsUL3coVu5QrCa/USUxxpijjDE/AjYD/wo8BexvrT1uPBYnk19HR0fYS4i0QrH3ZeD1fJUKTakgv4kbQ1OwnaxSEuNvJ9s+zHYyxcodipVbFC93KFbuUKwmvxEdz22MuQLvYMs2vFHKp1hr/zCO6xIRSkYsj/KcGP9FgHfYZf92ssFJTLCdrL03h7UWM0R1R0RERCQKRpTEAO8Gvgn8l7U2PY7rEZESxRHLZclKbgQ9McF0sjiGxrrqPTGNtXHqa2KkcwV6cwUaa+NjsHIRERGR8TOiJMZae/J4L0REBhvusMv4UFUT0/9XU231SgzA9PoaNnZlaO/NKYkRERGRyBtpJQYAY0wS+DJwGNBc+py19qQxXJeIAHlbOVkZyXQyv22GGNBU52VBlRr7wTsrxktisuzVUrdrixYREREZZ6NKYvD6YeLAr4HesV+OTDXJZDLsJURaUIkpL7j098QM8Vo/jTEYGmqqN/YDtPl9MakhJpQpVu5QrNyieLlDsXKHYjX5jTaJeTcww1qrU/FkTLS2toa9hEjrn05WVonxyyxDVWKy+WAMc/8AgO6+wT0xANPrvTHL7enqSYxi5Q7Fyi2KlzsUK3coVpPfaM+JeRLvkEsRmQCFKufEjKSxP5MvFO8xeNWcdK5QrOKUmq4DL0VERMQho63ELAHuM8b8Ce+smCJr7f8cq0WJiKdQ5TyYkfTEZPL9Y5gzWUtjbZzuTJ6eTJ5p9QO/9ZXEiIiIiEtGm8RcBewNvAa0lFwf/KtdEdll1ZKVkUwn6/MrMTXGkM4WaErE6M7k6a6UxPiP24c58FJEREQkCkabxJwF7G+t3TgeixGRgYqVmPLG/mKvTPXXllZi+rIF/8DLbMWzYtpUiRERERGHjLYn5hVAv6qVMZNKpcJeQqQVe2IGVWK8v0fSE1Nj/CSmrvpZMdMbvMb+oZIYxcodipVbFC93KFbuUKwmv9EmMf8J/NYY8zFjzPGlf8ZjcTL5dXR0hL2ESAumkw2qxBT6qyzVlFZi0tkCjbXVxywH28m2D7GdTLFyh2LlFsXLHYqVOxSryW+028mW+n9fXXbdAvvu+nJEpFRQcdmV6WTFSkyjV4npqbCdbFpdnLiBrkyBvlyBuqEOoBEREREJ2aiSGGvtvPFaiIgMVqzE7ERjf1CJiccM6axlVqL6drKYMUxvqOXNniypdI5ZzYkxWb+IiIjIeNCvW0UirFDsfRl4fSSN/cFhl4mYIV+w1PvVle6+wUkMlG4pU3O/iIiIRNuwSYwx5svGmLph7qkzxnx57JYlIlDaE9NfcbHWjqqxv8HvhQn6ZypVYkBnxYiIiIg7RrKdbDbwsjHmPuAx4EWgE5gG7A8cC7wfuGOc1iiTWDKZDHsJkRZUYkoPuwzGLhszuFemVLCdrNHfRhYnSGIG98RA/5jl9t7Kzf2KlTsUK7coXu5QrNyhWE1+wyYx1trLjDHfA5YAnwLeBrQC24G/AvcBl1lrt43jOmWSam1tDXsJkVbcNlaSrBQnkw2RwED/YZeNiRjk+suuPdk81lpM2etb64euxChW7lCs3KJ4uUOxcodiNfmNqLHfWvsmcJ3/R0QmgLUWW1J1CYxkMhlAOuclMdPq4vTkCmTzXl9MOlegN1soVmgCbcFZMeqJERERkYgbcWO/MaZlPBciIgMF28ZihgFVk+JksmG+e3uzXhLT4ldY+rIFrypDtQMv1RMjIiIibhjNdLJ/GbdViMgghUKV8cp+cjPSSkzST07S2QJNQ4xZDqaTVeuJEREREYmK0SQx/2iM+ci4rUREBiitxJQq9sQMkcQUrKU32E5W7yUufdkCTXVBEjO4uT9o7Nd2MhEREYm60SQxpwN/MMZcZIx5y3gtSKaWVCoV9hIiK1+tElMYPHa5XFCFqa+JFXtfhq3EBD0xVbaTKVbuUKzconi5Q7Fyh2I1+Y04ibHWvmytfcNa+wPgncaYj47jumSK6OjoCHsJkRVUYuI7UYkJkpiGmhh1/jkxfdkCjf6/eyoceBlMJ+voyxU/RinFyh2KlVsUL3coVu5QrCa/0VRiiqy1vwQe86sy88Z4TQAYY+LGmL8YY+71H88zxvzJGPOSMeYXxpjEeHxckaio1hOTs8NPJwua+htqY8Rjhtq4wVqvMgOVt5PVxAzJujgFCzv6tKVMREREoms008nO8f+OGWPmAgcCXcBdxpgvjMPaLgRWlTz+X8D3rbX74Z1R86lx+JgikVGwlbeNjWQ6WZDEBElLUI2pjQUHXg6uxED/lrJ2TSgTERGRCBtNJea7xpiXgV7gGbwzY94HPAaM6TgjY8xewCnAf/iPDXA8cLd/y+14PToik1bBL5bEyr5L/TMsh67E+NvJgu1jQRITHJBZNYkZ5sBLERERkSgY0WGXvgeBfwNetdZuH6f1BH4AXAJM8x/PAFLW2uAnq9eBPcd5DSKhyg9TiakZorG/N+clKQ01XiN/vZ/EBHlPT9VKjCaUiYiISPSNJon5prX29XFbic8YcyqwxVr7jDHm2OByhVsHdx771q5dO+haMpmktbV10PVUKlWx+Uv3T8z98Xhc8apyf1CJ6Uv3snZt/+8NNnV6f5f3ypS+/7otGe89smnWrl2LzXu/DzA2qMQUsNbS0dExYD012V4A3mjfAfOnD1pjlL4+ur/6/clkMlLr0f1D3694uXN/EKuorEf3V7+/NFZRWI/uH939I2GsrZoLhMIY823gXCAH1AMtwK/xtq7NttbmjDFHAFdYa99X/vrly5fbRYsWTeSSRcbFxu19PPdaF7NbE7x93rTi9ZUbu/jtym0cPLuR0962W8XXPvByO1c9vo6T5k/nf7x3H9ZtTfO317vZa0Yd97/UTrZg+dpxe1NXM3Cv2h3PbuKWFZs4522787l3zhnXz09EREQEYMWKFSxevHjoU7zL7NR0svFkrf1na+1e1tq5wFnAw9bac4BHgDP8284DfhPSEkUmRNXpZIVRTCcra+wfeODlEGfFaDuZiIiIRFjkkpghfAP4qj9cYAbw45DXIzKu8n6RtDxXKTb2j+CwywY/eQl6YtLZAo2JYMzy4CSmze+J0XQyERERibLR9MRMOGvto8Cj/r9fAQ4Pcz0iE6laJSY/okpM0NgfVGK8e/uyBZoSfiWmb/BZMa3F6WRjOnBQREREZEy5VIkRmVL8XIX4oEqMP51sBCOW62u9hCXhV2IyOVusxFSaUNam6WQiIiLiACUxEqpUKhX2EiJruJ6Y8vNjSgVJTFCJiRlDXY33PnXx6tvJij0xvTnKh34oVu5QrNyieLlDsXKHYjX5KYmRUFUatyeeQrWeGDuCSkx2YE8MQJ1fgUnEqx94WV8To6EmRrZg6Sp7XrFyh2LlFsXLHYqVOxSryU9JjEhE5av2xHh/D9UTky6rxEB/c3+Q/HRnBvfEgLaUiYiISPQpiRGJqIJfcSmfQlZs7B9iOlnFSkyQxFC9EgMDt5SJiIiIRJGSGJGIKviFkvLelxGdE5PzEpT6CpWY4FWVGvsBptdrzLKIiIhEm5IYkYgKKjGxsopLxt8qFjTqVxJUYhpr4sVrQSXG+L021SoxwXaylLaTiYiISEQpiZFQJZPJsJcQWdUqMUG/S31JglKuN1d9O1k+b4kZ6MtZcnk76LXTiwdeDjwrRrFyh2LlFsXLHYqVOxSryU9JjISqtbU17CVEVrVKTDobnAFT/ds3qMRU2k6WyUFjcOBlpTHLxQMvB1ZiFCt3KFZuUbzcoVi5Q7Ga/JTEiERUvkrvS7ESM0QSkx6iEpPOFmjyk5hKfTHFxn5tJxMREZGIUhIjElHVzompVGUZ+Dpb3E5Wek9t3BAzXnLUWFv9wMu2BjX2i4iISLQpiRGJqEKFc2KstRWrLKX6ShKY0q1oxphiNSZIbiqdFRP0xGwv64kRERERiQolMSIRVTzUsqQSk8lbrIXamKk6Yrm3wkGXgWALWiJe/ayYYk+MtpOJiIhIRCmJkVClUqmwlxBZGT+LqS1JRna2qT8QVGJqY9W3kzUn4tTGDD3ZQrGqA4qVSxQrtyhe7lCs3KFYTX5KYiRUHR0dYS8hkqy1ZHPedrLakvNgRtLUX2m8ciBIYuL+U5WSGGNMxTHLipU7FCu3KF7uUKzcoVhNfkpiRCIo65/f4jXjlyQxwzT1Q8lBlxWSmCD5ieO9Z0+FnhjQljIRERGJNiUxIhGUqVCFgZFWYrzqylDbyYJ3rVSJgdLmfiUxIiIiEj1KYkQiKOsnK4myRGQklZjgnoaa+KDnismPX4Dp7quWxHhnxWjMsoiIiESRkhiRCAoqMYmySkxv1q+yDFGJ6RmqJybhXQvGN/dkCxSsHXRfm7aTiYiISIQpiZFQJZPJsJcQSRk/EamNl1ViKhxiWS49ghHLmZwtJjmV+mJaK5wVo1i5Q7Fyi+LlDsXKHYrV5KckRkLV2toa9hIiqVolZiQjlnf41ZPmxODtZPGYoSZusLa/8b+nQl9MW3E6WX8lRrFyh2LlFsXLHYqVOxSryU9JjEgEDdcTU2mrWGBTl1c9mdWcqPh8kAAFf1dq7m+r93piUtpOJiIiIhGkJEYkgjL5YaaTDbGdbHN3BoBZzbUVnw8mlNXFqycxrRUqMSIiIiJRoSRGJIIyw00nqx28VSywuctPYpoqV2KCJCYR9xKk7go9MW0VemJEREREokJJjEgE9W8nG905MdZaNvlJzOxhtpPVmODAy8GVmGRdDQbo6MuTKwyeXiYiIiISJiUxEqpUKhX2EiKpv7F/4Ldo7zDnxHT05enLW5oTMZoqNPZDfyUmboJKzOAkJh4zJP0xy0FfjGLlDsXKLYqXOxQrdyhWk5+SGAlVR0dH2EuIpOKI5ZJKjLW2f3xylUrMcFvJAOprvfcM3qHagZflW8oUK3coVm5RvNyhWLlDsZr8lMSIREy+YMkXwBioifUnMZm8xVqojRniMVPxtcUkpspWMuivxBh/l1ilnhiA6TrwUkRERCJKSYxIxBT7YeIGY/qTlZGcEbOpe+h+mNLXF/zcpdJ2MoDpmlAmIiIiEaUkRiRign6Y2vLJZMM09QNs9s+I2b2p8nhlgETwer8S05PJY+3g5v3pDd57bFcSIyIiIhETuSTGGLO3MeYRY8wqY8wLxpgL/ettxpiHjDEv+X9PD3utIuMhU20y2TBN/dC/nWyoSkzMGOpqDDFjqI0b8hb6coOTmDZtJxMREZGIilwSA+SAr1lrDwTeDSw1xhwEXAost9buByz3H4vjkslk2EuInGy+8mSynqy37WvISkz38D0x0N8XEwwIqLSlbHpZY79i5Q7Fyi2KlzsUK3coVpNf5JIYa+1Ga+0K/9+dwCpgT+A04Hb/ttuB08NZoYyl1tbWsJcQOdUqMe09XkUkSC4q2eRvJxuqEgP9SUxQ1RkqiQl6YhQrdyhWblG83KFYuUOxmvwil8SUMsbMBQ4D/gTMstZuBC/RAXYPb2Ui46daT8y2bi9BmVGl3yWdK5BK56iJmeJ45GrqE957J+LVz4ppC3pitJ1MREREIiaySYwxphn4JXCRtXZH2OsRmSjZKpWYIImZWSWJ2eJvJdu9qZaYqTyCORBUYmpjQSVm8Jjl4ohlNfaLiIhIxAz969qQGGNq8RKYn1prf+Vf3myM2cNau9EYswewpdrr165dO+haMpmsWFpMpVIVD0TS/bo/rPuDBv5ETax4v7WwtdO7t2f7ZlJm8Ptv8pv6k/HCoO+B8vUEfTXGn7O8YUs7awvtA+9vbgG8npiCtcSMicTXR/frft2v+3W/7tf9k/v+kTCVRquGyXgHY9wOtFtrLyq5fi2wzVp7jTHmUqDNWntJ+euXL19uFy1aNHELFhljj72wnd5MgSMXJJnmbwvbkc5x/RNv0FAb4yvH7l3xdfe8uI3v/GE9J791Ot88Zp8hP8bWHRmeWdNJTyHP6vZeDtuzmfcfNGPQfe+/8690ZQr8/+cspKUukr/zEBEREcetWLGCxYsXD72NpEwUt5MdCZwLHG+Medb/84/ANcCJxpiXgBP9x+K4VCoV9hIiJZcv0JspYAw01ceL14fbSgb945WHm0wGpRPOqvfEALTWex+vvTenWDlEsXKL4uUOxcoditXkF7lfrVprnyT4yWqwxRO5Fhl/HR0dmiBSorPXSyam1ccH9LW8OUxTP8DL7b0A7NVSN+zHqStuJ/MqsZV6YgDaGmp4fUcf23uzmIxi5Qp9X7lF8XKHYuUOxWryi2IlRmTK2uE30U8rmy725jCVmHzB8tzmLgAOm9087MepjRtiBoz/+4KeKpWY/rNi1NwvIiIi0aEkRiRCipWYhviA68ONV36pvZeuTIE50xIj2k5mjKGuNkZNbOjtZMGEsnaNWRYREZEIURIjEiGdfsWjpawSM1xPzIqNfhVmj+GrMIG62hhxAzEDmbwlmx+8pax4VowqMSIiIhIhSmJEIqJgbcVKTG82T3emQG3M0FIfr/jav2z05i8vGkUSU18bwxhDfc0QZ8VoO5mIiIhEkJIYCVUymQx7CZHR05enYL3koram/1uzdCuZqXCIZa5geW5TNwCL9pg24o8XNPf3JzGDt5QVD7xMZxUrhyhWblG83KFYuUOxmvyUxEioNDmkX7V+mOEmk734Zg+9uQJ7t9Qxs7H69LJywZjl2qAvpq9CEuNXYtp7c4qVQxQrtyhe7lCs3KFYTX5KYkQiIphM1tI4sB/m1W1pAGa3VG7YD/phRrOVDGOEpqUAACAASURBVPorMUFzf6UJZeqJERERkShSEiMSEZ09gysx2XyBl7d6578s2L2x4utW+P0wo2nqh/4kJm6CCWUVemKK28mUxIiIiEh0KIkRiQBrbXEyWekZMWve7CVbsMxpSZBsGHw27Zs92WI/zGiTmGA7WfAfgUo9MY21MRJxQzpXoCdbeQyziIiIyERTEiMSAds6s/TlLLU1hsZE/7flqs09ACyYVbkK85/PbSZbsLx3brK49WukgkoM1vurUhJjjKHNT55S2lImIiIiEaEkRkKVSqXCXkLorLX8faOXrMzbvaE4gSybL/Dym/5WsllNg163uSvDPS9uwwCfPGz2qD9uTdx4f8xwB156ydG6rdtH/TEkHPq+covi5Q7Fyh2K1eSnJEZC1dHREfYSQrelI8uOnjyJGsNbZtYXr695M002b9mjJUFrha1kd/7Vq8IcN6+Vfac37NTHrquN9U8nq9ATA/0Tyt7Y3rlTH0Mmnr6v3KJ4uUOxcodiNfkpiREJkbWWl/wqzL6zGqiJ958Ds2qz1+tyYIWtZJu6Mtz793YMcP5OVGEC9bWxIaeTQX8SsyNjd/rjiIiIiIwlJTEiIdqUytCVzlNfG2PvkirMcFvJ7nh2M7mC5YR9pzO3tX7Q8yNVVxujxhgM0JstkC8MTlSCCWUdVSo1IiIiIhNNSYxISArW8rJfhZk/u4F4rL8Ks3Jjd9WtZH/f1sN9L20jZmDJYbN2aQ31tTGMMST8ClBvhQlkwcAAVWJEREQkKpTEiIRkY3sf3X0FGhIx9pxRV7ze0Ztj+d+9Jvp3vmXagNesae/lqw+sIW/hlP1m8JbkzldhAOpqveQlEff+UzDUWTGqxIiIiEhUKImRUCWTybCXEIpCwfLyJm+72FtnNxLzJ4RZa7nnhTfJ5C0H7N7IwbP7t5K9uj3NRQ+soaMvzxF7t3DREXvu8jqCs2Jq/UpMd9/gSkzQE9Nd0H8uXDFVv69cpXi5Q7Fyh2I1+emnEglVa2tr2EsIxevtffRmCjTVxZnTlihef3pdJ+u299GYiHHygW3FcctrU2kueuBlUukch+85jW8dN7dYPdkVwVkxQ41ZDs6J6czu8oeTCTJVv69cpXi5Q7Fyh2I1+SmJEZlg+YJljV+F2W+P/nNhtnZleORlbxvZPx44g6ZEHID1HX1ceP/LtPfmeMecZq5ePI+6mrH51g2SmLj/uFIS0+qfE7Ndh12KiIhIRCiJEZlg699M05ctMK0hzqxWrwqTL1juWbmNfAEOndPE/rt7Y5Xf2OElMNt6cxw2u5lvn7DvmCUw0J/EePPJKvfEJOvjxA10ZvJk8+qLERERkfApiRGZQLm85ZXNQRWmsViF+cOrHWzqzJCsj3PCAW0AbOz0EpitPVkOndXE/zpxHvVjmMAAxIwhUWOKB15WOismZgxJv7l/e1rVGBEREQmfkhiRCbRua5pMzpJsrGG3Fm+b1oaOPv7wqney8KkHz6SuJsbmrgwX3r+Gzd1ZFu7exHdO2peG2vhQb73TSg+8rLSdDPonlGlLmYiIiESBkhgJVSqVCnsJEyabL/DqloG9MNl8gd+ufBNr4V37tLBPWz1bujNceP/LbOzKcODMRq47aV8axymBgf4DL6F6EhOcFaMkxg1T6ftqMlC83KFYuUOxmvyUxEioOjo6wl7ChHltS5ps3jK9uYYZ07yk4JGXUrT35JjZVMt757fyZk+WC+9fwxudGQ6Y0cB337dvscG/VF82z7Nr3+SlTSlyu9inUl8bK24nq9QTA/1jltvTGlHmgqn0fTUZKF7uUKzcoVhNfjXD3yIiuyqTK/DaljTQ3wvz6rZe/u/6TmIGPrhwBjsyOS66/2Ve39HHfm0NfO/k+Uyrq8Fay7ptXTzz2laeeW0LK157k+df30Ym5yUciZoY83dPcuAe0zlgj1Y+9I59mTtz2lDLGaCuZDtZTyaPtbbYqxPQdjIRERGJEiUxIhPg1c295AuWGdNqaWuuJZ0tcO8L2wA4at8kdbUxvnz/GtZ29LHv9Hq+f/J84liuu/9ZfvL4arZ29g54P2Ng/9lJ0tk867Z1sWrDdlZt8MYz3/zICzx86WnMmd40aB2V1CdixIyhJmbIFSy92QKNZdWfoBKjJEZERESiQEmMyDjryxZYuzWowjQA8LsX2+nsyzMnmeCg2U1c9MAaXkulmdtaz3dPmsd9f3mVb9+7gk0dPQDMaK7jH+buzj/M3Y1/mLcbh71lJtMavPHMXeksf9+UYvXG7fz0jy/x9Ctb+MLtj/PrC08eVFGpJBjZnIh7SUxPZnASE/TEtGs6mYiIiESAkhiRcfbK5l4KFnZP1tLaVMvqzd2s3NhNTcxw7Ftb+drv1vDK9jT7JOv46PxGzvzhA7zwRjsAh+49gys+9E7es9/sqglJc30ti+buxqK5u3HSwrdw9FW/4r9f3sTvVq7nfW97y7Drq094SUxpc/9MagfcE2wnS6kSIyIiIhGgxn4JVTKZDHsJ46o3k2fdm/29MF19ee5f5SUo757XwreeWMfL7Wmmx/P0bljHJ29ZzgtvtLPn9CZuPO8YHvz6Bzhy/z1GVFEBmDmtnovedygAV9+zgkLBDvua4MDLuP8xuipMKCs29veqsd8Fk/37arJRvNyhWLlDsZr8VImRULW2toa9hHH1yqZerIXZrQma6+Pc/exWerMF5iQT/OfKzbz4Zg+Z9q38ed0GCtbSXF/LRScdwmeOPYiGxM59e5531AHc9PALrNqwnf9a8Soffse+Q95fGzfETEklpm9wEtPWoMMuXTLZv68mG8XLHYqVOxSryU+VGPl/7d15kCRneefx71NVWUffc0tidFigFToAAQs2YYwNA1jceL3LYa+NsSN8LA6bZb1rjP/A3gjbsOtzWeONDYNtvOZar2WOQCwyt9cgMJIASYOkkTSja2Z6rq7urrsqn/0js6qyqqt6enpmuqpav09ET+Xx5ptv1juVVU++75spF0m51uKxUzUAnnbpFHcfLfHAyQqpFPzj0WUOniizcvQJDh95HDN46w89ndvf/eP8ysufuekABiAfZPi1V9wEwHs+fQeNs9yC2czIJW6zPKglZqHdnazaJPSzt+6IiIiIXEwTF8SY2c1mdp+ZHTKzd466PCLDHDpWwYHLdubAnNvuO0MzdA4WK3zvRIkzjz7C0WOL5IM0H/r5A7z3jS9gz2zhguz7jd//NJ66d47DJ1f4yNceOGv65LNiVge0xATpFLPZNKFDsTr4gZgiIiIiW2WighgzSwN/CrwCuB54s5ldP9pSiay1Wm3yxOkaBjx1X55bD55mtd7i7qUyh06VWDz8MCdOnmY2H/Cxt72cl914+QXdfyad4p2vfg4Af/DZu6jU1+8GlnxWzKAgBpJdyjQuRkREREZrooIY4PnAIXd/yN3rwEeB1424TCJrHDoaPddl/64ch8/UuH+xzLdPl3isWObYQw+xtLTM7pk8f/+rr+AFT7vkopThNTddxY37d3J0qcxffPV766ZNdicrDehOBrCgZ8WIiIjImJi0IOYpwKOJ+cfiZTKhlpaWRl2EC2653OTYUp2UwSU7s3zuvtM8slrjieUyjx86xMrKKvt3TPOpf/9KnnH5rotWjlTKeNdrotaY//a577BSqQ9Ne7buZAAz6WgsjIKY8bcdP1fbmeprcqiuJofqavubtCBm0H1mNcp4ghWLxVEXYV3ujrsTuhOG0V8r/mu2nGYrpNEKaTRD6vHfA0ejB1Tu35Xjiw8ucbLc4N5TKzxy3/1UyhWu2TfPp9/xKp667+Lf/vHA9fv5/qfu43Spxv/44j1D0+WzKTJmGFBphLQG3Jp5yqLg5rS6k429cf9cSS/V1+RQXU0O1dX2N2m3WH4MSA4e2A880Z/o0MOPRBPefZmdnWV2bg68vTiaWF5ZYWVltbNt+6fbzMwMMzMz0Xwin9WVVVZLpd70DtPT00xPT/dEVO5QKpUoleP0bp31U1MFClPTcTG6W5XLZcrlSiL/KG7L5/MUCoWefTpQqVSoVqtryp/L5cnn83in7NEG1VqNWq3WSdxOn83lyGWzPXkD1Gp16vX6mvyDIEsQBD3LcKg36jQazTXpM5kMmUzQc7wOlMs5Di8vrilPOp0mnU535tvH0Wq1aCXuttVen0qlsFQqUVdRZlHwEdKOf9vpDeuExN33iL762NizWQZJGTRScN/xMt89XeKxh4/QqNe58bIF/ujHbqSxfJIjyyc76efn5wfeDnJpaWngifhc0v/c85/C7Q8e58++cA8/+6Lr2DWTX5O+3EhhNkUmBY0Qnjhxhsv37ezJZy4bvR+Hj53myEylZ93FLL/SPznTh6Fz6swSS8UVQjdCh9CNlhu5fIFsrkAzvpjQvqhQqdWpN6JgO/npTWfSBJnerzvDaDQbtJprWxYzQYYgE7QTdvJqNBo0B6QPgkznfGiJPTf6zodYdG7JBlmy2aCTslTKsXxklXq9TqPRPd+2vzFyuRy5XC5aZp1iUUuczzt7NSefy1Mo5KPSJNJXq73fF+19FAoFpqam1uRfLpepVCqJtFH+01PTTE9Prcm//X3Xe+Z0ZmZmmJ2Z6cvfWFldobS62pPazJmdib6vLZEeYGVlmZWVld7jxZmbm2MhfiZIlD4qV3FpieLy8poz+fz83Dr/P5fXLJ+fn2M+Tu/x90o7/fKA9HP9+cdfKUvFddLPDyhPcUh55rrlSWof7/mmn5sb/v4sD0k/P6D8xeK5px9angHPfFkqFnvzj9/nufk55ufnaYX03J2zWCwOfv/n5gY+U6bYn/866R1YXif93Hnmf9b0cwPSLyfTWyL9LHMD0i8vF1leXlmzfHZudnD+xWLn89iTfna2U/7kb+LlYpHlAennZmdZWNjcRV3zCbpdqpllgPuBA8DjwDeBn3D3ziXmz3/+875oV46ohLJdWWKiPZ38Mu5fftnOHLc+cJp7T5X5+oOPcvyRR5krZPnqb76eSxemt7bwwJve/zm+cO/j/NJLbuC3/9Xz16wv11p85d4l7lsqU2qG/MzzL+Gy+VxPmr/4p/v54PfKvOqanbzzh67YqqLLJhw5coQrrxzNebAVOvVmGLVUtoOMxHTntZVs0fQ1AckGntMqIiLbwMJ0huzqgxw4cOCcrh5PVEuMuzfN7JeB/wukgQ8mA5i2XGDdq2EDf3RG/yav8PSu617NWbMumV9iZs265A/cdcsRLRi4zoakWbOuu5/Bx9F7vGvXJa5eDSlrd79DynqWcvTmaZ11x48f45JLLlk3zbplHbhuQFkTmXWPceNlPVd/950TLJYb3HNiicXHHgfgv7zxBSMJYADe9Zrn8IV7H+eDX/kev/DiG7hsR285ckHUs7T9wMtB42Lm45aY03rgpQD1Zkip2mK12qJUbVGqRdOV+vrPJdooAzJpI502Mikjk3xNG+kByzIpI5Xqfu6HxUGDLt75kJn1Yinv22hN2iH59Lb+OidPnWLXrl0DWoXjfAcub+fjQ5Z3W6TXLu/+433H4Yn0+ID1nX15b55DWrSHlnnYMSX2Ozx/70u/8fdso4ad+R3vaW077ww36QJnt/H9jmrHGzziZKrQQ1I2ZNTERTiOMX9rRvd/ZgNpgvTmSjdRQQyAu38G+Mx6aV584871VssYWTkdsmMmGHUxLqiDx0vce6zEd0+t8vjDR/Aw5HXPuYofe+73jaxMz7x8N6999lV88s7D/MFn7+IP3vyDPevTKSOIfwTC4CBmLht9GSxpYP+ThrtTqcfBSi0OVuLpRnPwz0IDskGKIBlo9AUi6Q0sS9nmLyJMmuZqk/278mdPKCM3ylZOOTeqq8lyxx3nvs3EBTHbRftKYPuqV/LqVWdZ4mrammXt7RN5tK8+DcyzM91e371C1823dz9rrmj1b9+3LHlVr/94esva3Xeplufkoys9V9c62w4q+6D3bshVwXba5BW9QduTSDeorGvyTF7d7NuPA0eLdR5arvLQo0eplErsnSvw3je8YOQ/yH79Vc/m03cd4cNfe4C3vfQZXL1nrmd98jbLqwNus7x/1xywwumKBvaPu0H9qdfTCr2nNaWUaF0Z1q0rnYLpfJqZXJrpfPQ3k08zlU2TSj05go8L5VzrS0ZHdTU5VFfb37YMYv76m8eAjQcGMPwHazQ95Md5Mr+hP+x7f+BO0BCkLVQ9e5IJUqw3uefYGU4dPQrAn/zkC9k5M/qrrNdcssAbv/9pfOTrD/B7n/oW//OtP9ITWEW3WY5aWwa1xFy5dyfwOGeqTdx95EGZDDdoUC5EXcDaQcpqHKSUztIFLJexToAynU8znYumc0Hqgv8fCEOn1gqpNkJqzZBqM6TacBqtEI/Xh9AZXN3zCvGdBLvLOuuhb3k7XTwd59m/fTLP7mt8w5DEd0bYn+cGOi+tfedW1l27/rbnluB8au18q/z89n1+O79wx10aluwC5D0wxXmsPd99n2X7i7rvC3HclaFrzufYL+5n8OJ+/i/SptH2m8xgz0yWPZvYblsGMY8u1UZdhA0zokpPjtNoL6Ozrnc8S/LuKz1piU7y/duTyDO5Pe6d8R/eTtXpTO69g0TaUxa3QrR34FF+7j7gf2+7LSfRg7i9fbzEO1NRwdotOsl3qP8HQXKuGxR6nLY9F+Xr3t2+/2eFe/tt8MT02iPo6Xfes92A1irgwVMVnjhyGHfnLS+8lgM37Gdc/Norb+Lv/vkhPnHHYa677Nu84+abOut6Hng5IIgpBGnymRTVZki5ETKdTW9ZuWXj2l3A+seqlKotGq3BP64NmMqlui0qidaVIL3xO/GH7tSacQDS8DgISQYk3elaI17WSR9SH1I+ERHZvloh7NlERLItgxjPxq/tee92IQo7y9o/es/SfatveRgv7G+J6Vx98+4+2rdhTOYfJtMkrhImuzqF/V2UvLvvdrp23smrgYPSJfPo3UY2qn8QcM98oltfe/rUsePUKlWu3D3Lu3/seVtUyo25fOcM7/upF/KLf/kV3vPpO8kHGf7dgRuBvgdeDuhOBrAjn+Hoap3TlaaCmBHbXBcwYyafYjqXZjqfiaYHdAFzd8qNkOMrDUr11tBApCcoaYbUhoyTORf5TIpcxsgHKXKZFPlMiiCdImV0xsmYQSq+YJNqT1v3OkordOrxXc4aYUgjhGYYds7N7Qs+JOZ7bvzRvrRj3en2zUKIWyE9sa3Tk3RtCz/dczKJ8zTJ5XjP94gP2r7vvA4k8un/Lony9ESaSXauh3BOF4TPkvm57Pucr0RfoH0P2++625/n/4vzack4e6+U4QksccFyqyQP9eLuezxy39TNK85j32Fmc8e9LYOYTx083p1pfynE052vi74r/r0/TPu6AyS+MEh8MSTzW7vN2v32b7Om1WHNNt0vtMTc8DwSX4aDt4nnew8m+q/a+Ub27nc3PRt2WHfHCb3lHnpcnWUDjjOR1nqSdL/tk8ffc+idYKL7AyCZvndsjq9d1gk4k8Gpb+BEO1jKjPf/9IuYyY3fTQte/9yrqTZa/Mr/+kd+65Zvkg/S/OyLriOXTa07sB9gZyEKYs5UGlzedwtmufDcnUbTO4Pqk60r63YBC4yZXIbpfCoOVtJM51I9XcBaoVOsNlksNThzosJSpcmZSoOlcpOlSnNTrSLuTi5j5NIpshkjFwck2XT0GsSv0f+z+Ee7dbuJNcIW1WbUglNptFhpNDhRjYKjWivRgtNyas1Wd1kzagGqttSaI9vDxfgRKRukt37LXbdnimddde7bbcsg5qG7hz+ZXGQz2t36UkZ8C1fruSpsZp0rw5m08baXPoPnXb131MUe6k0/cA2VRotf/9jXeOfHv85UNsNLrruy252s3ho47mVHITplnNFtls/K3ak2WixX66xUGixX6ixX6qxUu9PL1QYr8fSZcp1iuU69GdIKo3EgrdAJw94WWbzbottufUh2FbVEV8cwjJ6/0gqdZhjSCqHlnsg3EfSTGCfY3/rYd5Gh2wIdTbTLKCIicq5Sl+6Aq879+XPbMojZN1foPCsgZdbpJpFKdkOw+Edo/GOU+EdoKvmDNE4L9OaXXJfs1jAgjQ3dp2Epen4M95SPeFkiz24+3bEvqZQlulUk95nMt53n+vtMxf0hhu0z+V7077N9fDZgn6lUe1zP2vzKpRKzszPd4xmQZvD73Ht8g4658z4Pen+T7/OQuk0e13b01h96OtV6k3ff8k3e/jf/j3fcXOLaPZeSMaMZdydKdhlbWlpiRz5qWTq9zW+z7O6U680o+KjGAUelwUpnengwslJtp2v0PC36yarn82PdzlqdMYDJ1zVdxXrPd51ALZFvct66u5goYRiSSm187NFW0E1oBtuKupqkh5CPs83cgEbv/GhctXNzz9DblkHMd3/3TaMugmxQdB/3S0ZdjCetXzpwI5VGk/d8+k5+/9Y7mc3fw3OfegXXXXkZpVqrJ4gpFovsLEQDzs6M8W2W3Z1SrRkHF3Fg0W4JiYOLlWHT7VaTap3WBRhIEKRTTOcCprIZ8kFAPpOhkM0wlQ0oZKPlhSB6nS9k2TGTYzqXZiqXppCNXtMpp1QPKddDVupxl7J6yEqtyWo9JAzpCwigfeOPTMqYy2eYL8R/+TQ7CgFzhQyzuQzL9Ranyg1OVZqcqkSvJ0p1TpabnCo3KdbiYNWSN+foHUtSCNLsnQ7YO51l70zAJTM59k4H7ChkKGTSFIJoXEshHt+Sz6TIprfvxYHN0PMsJofqanKoribLHZt4UMy2DGJEZOPecfNNPGP/Ln7/1ru488hJvnTPIb52/2HqpWv5hRdfx+U7p0nHVx4XLnJ3sjB0VmuDWzj6p6PgpHc6agVpdLo5nY9CkGa2kGWukGU2HzCbzzJXCOL5LHP5gHw2QzadIUilSafSpCyNkSKbjoKVIN178wMDpvru/jWdS5FKGSv1FkvleFxKpcnJcpMzxWbf+KRU9GeQz+fI52Eqm2JHIWChkGHHVIYdhQyV4kkuu+wSVustFssNFkvR3wOn6iyWSiyW6pwqNzjb8JFskImCk+mAvTPR677pxOtMwGw2rYBERES2nIIYEeFlN17OS2/Yz/s+ez9//Y2DHDlxhg98+R4+8OV7yGXSXL1nlktnA2YXdlA8VeV7j8PDJ+apN0NqzSbVRiuabrSiAdft1850SK3Zolxr9HbL6gtMVqsXpoVnKpthrhAFHVHw0Z2ezQfxfO90Mu1sPiCbiQKQZss7z1PpuRNYrTW0y00mZT0PgCxkUzhQabUoVlqcqTR56ESVM+XGWQfRpwzm8xkW4gBlRyEgn03RdKfSDDldbbJYqnP3mRKLj0XByvHVGo1w6azv085CphOUrAlSZrLsyGdI68GVIiIyhhTEiAgQjSl43tWXMDU7yx2PLvLIsWM8crLIsWKZg0eXOHgU4AQAnzjyCJ/48p0XpRwz+YC5OLiYLWS70/lkoBHE6+KgoxB0p/MBmXN4tglEXdDqTe/cAWxxqdqZrjaGj2vJB91nq+QyRhOohSGlWoulSpPDJ6ucqTQpVpvrjjHIZYyFQtQFazafBiMKUlpRF7IT5QYPnYxaUBZLdVbXuTNZ533Mdrt57ZsJOi0q+6az7JsO2D0dkD3H90lERGRcKIgRkY72s2L2797B6599BS9/+k5Wqw0eXCzy9Xse5Eg5xcfuegxv1NmZS5EP0uQyabKZdDQdxNOZ9nSUJpuJ0k1lM53ApCcYiZfN5INO17WLIXSnUgs7LSvJp9YPfRCkEY9TSZFKGyFQD0MqzZBitcnh01XOlJtU1gl2AObyaebzaYJMCjdoeHQr4dVGi1OVJvctVjleqm/opgnZtHWDkrgFZW8cnOydzlI7fYynP/WqTbxDIiIik0FBjIzU/Pz8qIsgCbns2gdezuQDnnXFbq6cy5AqzPDVYsB0kOKzP/XMURZ1qHarSrnWolwLKddaneesrNsFLG1kA8NSRohTd6faCFmtt3j4dJWlSnPdhwamjXggvhGmolsbV1rOar3FUrXJXWcaLJbqZx2HkjbYNZUISvq6ee2dzrKQX38cypItbOCdknGh8+DkUF1NDtXV9qcgRkZqYUE/tsZJPkgRxD+OS30PvFxYWCB0J21Qip/UnsuMpjtSGDqVeki5HgUqlVqrO11vMezOxqE7lgKSgUozpFRvUaw2hz5x3t2ptaJt260x7e5epXqLYq3J6UqTSvPs3bwW8pmegfHJFpS90wG7poLOQ0c3S5+ryaL6mhyqq8mhutr+FMSISEcuSBHE3blW+4IYiJ7bsaMQcLLcYKnaZN9M9qKVpdGMbitcabeo1FtR60o9pDpkTIi70widEMcNmkC9FT3JfbUe3Zq4XzOMnwbfCmm6g0ELqLVCys1oTEqxtn4rTFshk2LfTCIomeltQdk7HYws8BMREdlOFMSISMd8Id3tTjYgiAHYUchwstzgdOX8ghiPu2tVat0WlXKtRSWeHjRGJXSnFQcqbtGT6xvu1JpOuRl1G2tv5u7UQ6faCuMgJQpWWkAzDKNWlEa4odYTgB35DLunA/ZMBeyJX3f3Tc/odsMiIiJbQkGMiHTMFDJcsTvHd06XaITOcqXBXCHoSbMjH502Tvc98NLdabacRstpNJ16K6TRdBrxa73lNJohtUbISjUKOBphFJQ049eWQyt0mu6E7oTEgUocgNRa3fTNOG0UxEQtKdVWSCN0amHUirKR1pMgZT3ByJ7pODiZiu7gtXc6y65ChkB38hIRERkbCmJEpMe1T5nmK4eLFOstPvTN4xy4egezhTRhCI1WSDpuuPjnw8tUl1tUGi2qTafejLpjtTxKVwuTLSBOPZ5vhkSBSCJ4iV6Jg5PewKadfjOPr5zNprtBSTtIiYOTKGDJMp9T64mIiMikURAjI7W0tKTBd2Mmm0nxo0/fyd/ffZLlWotbDp4EoqfNAxxfrQNwy4On+OyRfwaKgwAACYRJREFUMwSpVKf1pPO6mYjjLIKUMRWkmArSFIIUU0GKQpBmOkixu69r157pLLunAvJP0vEn+lxNFtXX5FBdTQ7V1fanIEZGqlgs6iQzhp62b4o3BHv45N2nWI7HxrTjkt35gEdW6zTdWW1EHb4GyaaNQiZFPhMFHlNBiplsmulsOwhJM5VJdabbgclUJrE+Eayc7x27nkz0uZosqq/JobqaHKqr7U9BjIgMdMXOAr/8ov24O+5w+MgjXH7F5UD0LJPleshiqU6pHsaBRjfwyGdSpNRFS0RERC4SBTEisi4zwwzSKXoGty/kUyzkdQoRERGRrffk7DAuIiIiIiITS0GMiIiIiIhMFAUxMlLz8/OjLoJskOpqcqiuJovqa3KoriaH6mr7UxAjI6U7h0wO1dXkUF1NFtXX5FBdTQ7V1fanIEZERERERCaKghgREREREZkoCmJERERERGSiKIgREREREZGJoiBGRmppaWnURZANUl1NDtXVZFF9TQ7V1eRQXW1/YxXEmNl/NbPvmdl3zOwWM1tIrPsNMztkZveZ2Y+Ospxy4RSLxVEXQTZIdTU5VFeTRfU1OVRXk0N1tf2NVRAD3Abc6O7PBO4HfgPAzK4H3gTcANwMvN/M0iMrpYiIiIiIjMxYBTHu/jl3b8azXwf2x9OvAz7q7jV3fxg4BDx/FGUUEREREZHRGqsgps/PArfG008BHk2seyxeJiIiIiIiTzKZrd6hmf0DcMmAVb/p7p+I0/wm0AT+pr3ZgPR+cUooIiIiIiLjbMuDGHd/6XrrzewtwKuBA+7eDlQeAy5PJNsPPDEsjzvuuON8iylb6NSpU6MugmyQ6mpyqK4mi+prcqiuJofqanuzbpwwemZ2M/CHwA+7+4nE8huADxONg7kM+Dxwjbu3RlJQEREREREZmS1viTmL/w7kgNvMDODr7v6L7n6PmX0cuJeom9nbFMCIiIiIiDw5jVVLjIiIiIiIyNmM893JzpmZ3Rw/DPOQmb1z1OWRLjP7oJktmtndiWU7zew2M3sgft0xyjJKxMwuN7MvmtlBM7vHzH41Xq76GkNmljezb5jZt+P6+u14+feZ2e1xfX3MzLKjLqtEzCxtZnea2afjedXVmDKzw2b2XTO7y8z+OV6mc+EYMrMFM/vb+KHpB83sBaqr8WNm18afp/bfspm9fTN1tW2CmPjhl38KvAK4Hnhz/JBMGQ9/SfSg0qR3Ap9392uIxjkp8BwPTeA/uPt1wA8Ab4s/S6qv8VQDXuLuzwJuAm42sx8A3gv8UVxfZ4CfG2EZpdevAgcT86qr8fZid7/J3f9lPK9z4Xj6E+Cz7v504FlEnzHV1Zhx9/viz9NNwHOBMnALm6irbRPEEA36P+TuD7l7Hfgo0UMyZQy4+1eA032LXwf8VTz9V8Drt7RQMpC7H3X3O+LpFaIvgqeg+hpLHlmNZ4P4z4GXAH8bL1d9jQkz2w+8CvjzeN5QXU0anQvHjJnNAS8CPgDg7nV3X0J1Ne4OAA+6+xE2UVfbKYjRAzEnzz53PwrRD2dg74jLI33M7Crg2cDtqL7GVtw96S5gEbgNeBBYcvdmnETnw/Hxx8B/AsJ4fheqq3HmwOfM7Ftm9vPxMp0Lx8/VwAngL+Kumn9uZtOorsbdm4CPxNPnXFfbKYjRAzFFLiAzmwH+D/B2d18edXlkOHdvxU3z+4lapa8blGxrSyX9zOzVwKK7fyu5eEBS1dX4+EF3fw5RV/W3mdmLRl0gGSgDPAf4M3d/NlBCXcfGWjz277XA/95sHtspiDmnB2LKWDhuZpcCxK+LIy6PxMwsIApg/sbd/y5erPoac3H3iS8RjWVaMLP2bfR1PhwPPwi81swOE3V5fglRy4zqaky5+xPx6yJRv/3no3PhOHoMeMzdb4/n/5YoqFFdja9XAHe4+/F4/pzrajsFMd8Eronv8pIlaqL65IjLJOv7JPCWePotwCdGWBaJxX30PwAcdPc/TKxSfY0hM9tjZgvxdAF4KdE4pi8C/zpOpvoaA+7+G+6+392vIvqO+oK7/ySqq7FkZtNmNtueBl4O3I3OhWPH3Y8Bj5rZtfGiA0TPFlRdja830+1KBpuoq231nBgzeyXRVa008EF3/50RF0liZvYR4EeA3cBx4N3A3wMfB64AHgH+jbv3D/6XLWZmLwS+CnyXbr/9dxGNi1F9jRkzeybRIMg00YWpj7v7fzazq4mu9u8E7gT+rbvXRldSSTKzHwF+zd1frboaT3G93BLPZoAPu/vvmNkudC4cO2Z2E9ENM7LAQ8Bbic+JqK7GiplNEY1jv9rdi/Gyc/5cbasgRkREREREtr/t1J1MRERERESeBBTEiIiIiIjIRFEQIyIiIiIiE0VBjIiIiIiITBQFMSIiIiIiMlEUxIiIiIiIyERRECMiIiIiIhNFQYyIiIiIiEwUBTEiIjI2zOz3zOztFzjPb5jZDRcyTxERGS1z91GXQUREBDPbA9wFPM3dKxcw3zcAb3T3H79QeYqIyGipJUZERMbFzwCfuZABTOyTwIvN7NILnK+IiIyIghgREdkyZvYFM8sOWf0K4MuJtIfN7D+a2XfMrGRmHzCzfWZ2q5mtmNk/mNmORPpfN7PH43X3mdkBAHevAt8CXn4xj01ERLaOghgREdkSZvYUom7M9SFJngHc17fsx4GXAf8CeA1wK/AuYDfRd9ivxHlfC/wy8Dx3nwV+FDicyOcg8KwLciAiIjJyCmJEROSiM7OXAX8EHDOznx6SbAFY6Vv2Pnc/7u6PA18Fbnf3O929BtwCPDtO1wJywPVmFrj7YXd/MJHPSpy/iIhsAwpiRETkonP324gCjT909w8NSXYGmO1bdjwxXRkwPxPnfwh4O/BbwKKZfdTMLkuknQWWNn0AIiIyVhTEiIjIRWdmRtRqcsc6yb5D1G1sU9z9w+7+QuBKwIH3JlZfB3x7s3mLiMh4URAjIiJb4XrgoLu3zOx1Q9J8BvjhzWRuZtea2UvMLAdUiVppWvG6HPBc4LbN5C0iIuNHQYyIiGyF00DRzH4C+KchaT4EvNLMCpvIPwe8BzgJHAP2Et0AAOC1wJfc/YlN5CsiImNID7sUEZGxYWa/Cyy6+x9fwDxvB37O3e++UHmKiMhoKYgREREREZGJou5kIiIiIiIyURTEiIiIiIjIRFEQIyIiIiIiE0VBjIiIiIiITBQFMSIiIiIiMlEUxIiIiIiIyERRECMiIiIiIhNFQYyIiIiIiEyU/w8se8lkAl02cQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize = [12,5])\n",
"colors = ['#a6bddb','#74a9cf','#2b8cbe','#045a8d']\n",
"for color, G_K in zip(colors,[40,80, 120, 160]):\n",
" V_membrane,_,_,_,_,Time = run_simulation(G_K = G_K)\n",
" ax.plot(Time, V_membrane, color = color, label = f\"{G_K}\")\n",
" \n",
" \n",
"ax.set_xlabel('$t$ (ms)')\n",
"ax.set_ylabel('$V$ (mV)')\n",
"ax.set_ylim(-20, 120)\n",
"title = plt.title('Membrane Potential $V$')\n",
"ax.legend(title = \"G_K in mS/cm**2\" )"
]
},
{
"cell_type": "code",
"execution_count": 233,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x25750048e48>"
]
},
"execution_count": 233,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize = [12,5])\n",
"colors = ['#a6bddb','#74a9cf','#2b8cbe','#045a8d']\n",
"for color, E_Na in zip(colors,[20,50, 80, 110]):\n",
" V_membrane,_,_,_,_,Time = run_simulation(E_Na = E_Na)\n",
" ax.plot(Time, V_membrane, color = color, label = f\"{E_Na}\")\n",
" \n",
" \n",
"ax.set_xlabel('$t$ (ms)')\n",
"ax.set_ylabel('$V$ (mV)')\n",
"ax.set_ylim(-20, 120)\n",
"title = plt.title('Membrane Potential $V$')\n",
"ax.legend(title = \"E_Na in mV\" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each channel activation"
]
},
{
"cell_type": "code",
"execution_count": 225,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Ion Channel Activation')"
]
},
"execution_count": 225,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.xlabel('$t$ (ms)')\n",
"plt.xlim(0, 30)\n",
"plt.ylim(-0.1, 1.1)\n",
"xticks = [x * dt for x in range(T)]\n",
"plt.plot(Time, b)\n",
"plt.plot(Time, c)\n",
"plt.plot(Time, d)\n",
"plt.legend(['h (Na inactivation)', 'm (Na activation)',\n",
" 'n (K activation)'], bbox_to_anchor=(1, 1), framealpha=1)\n",
"plt.title('Ion Channel Activation')\n",
"#plt.savefig('1actv.png', dpi=144)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Electrical Bursting"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"vt = mt = nt = ht = 0.\n",
"it = [10] * T\n",
"vts, hts, mts, nts = ([] for i in range(4))\n",
"for t in range(int(100 // dt)):\n",
" vt, ht, mt, nt = update(v=vt, h=ht, m=mt, n=nt, i=0, dt=dt)\n",
"for t in range(T):\n",
" vt, ht, mt, nt = update(v=vt, h=ht, m=mt, n=nt, i=it[t], dt=dt)\n",
" vts.append(vt)\n",
" hts.append(ht)\n",
" mts.append(mt)\n",
" nts.append(nt)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x108f98f98>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Ji0LTifUPAW+lvnPvrsBbMvN9bRYmSRp+sx6JRMRewGRmrgXIzHOoRyKSpBE1l8NZa4GI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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108f8e4e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.xlabel('$t$ (ms)')\n",
"plt.ylabel('$I$ $\\mathrm{(\\mu/cm^2)}$')\n",
"plt.xlim(-5, 50)\n",
"plt.ylim(-2, 12)\n",
"xticks = [x * dt for x in range(-500, 0)] + [x * dt for x in range(T)]\n",
"plt.plot(xticks, [0]*500 + it)\n",
"plt.title('Membrane Current $I$')\n",
"#plt.savefig('2current.png', dpi=144)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1091dd080>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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OeC7n8aXACmPMIuAu4LKq7LWMUV6y/EqEBuRkFXsDA1BKLbJZjlFk0iX+jkpU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\nIhD/H5S5KsRPHTGHAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10911a780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.xlabel('$t$ (ms)')\n",
"plt.ylabel('$V$ (mV)')\n",
"plt.xlim(0, 50)\n",
"plt.ylim(-20, 120)\n",
"xticks = [x * dt for x in range(T)]\n",
"plt.plot(xticks, vts)\n",
"plt.title('Membrane Potential $V$')\n",
"#plt.savefig('2potential.png', dpi=144)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Refractory Period"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"vt = mt = nt = ht = 0.\n",
"it = ([10] * 200 + [0] * 800) * (T // 1000)\n",
"vts, hts, mts, nts = ([] for i in range(4))\n",
"for t in range(int(100 / dt)):\n",
" vt, ht, mt, nt = update(v=vt, h=ht, m=mt, n=nt, i=0, dt=dt)\n",
"for t in range(T):\n",
" vt, ht, mt, nt = update(v=vt, h=ht, m=mt, n=nt, i=it[t], dt=dt)\n",
" vts.append(vt)\n",
" hts.append(ht)\n",
" mts.append(mt)\n",
" nts.append(nt)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Membrane Current $I$')"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.xlabel('$t$ (ms)')\n",
"plt.ylabel('$I$ $\\mathrm{(\\mu/cm^2)}$')\n",
"plt.xlim(-5, 30)\n",
"plt.ylim(-2, 12)\n",
"xticks = [x * dt for x in range(-500, 0)] + [x * dt for x in range(T)]\n",
"plt.plot(xticks, [0]*500 + it)\n",
"plt.title('Membrane Current $I$')\n",
"#plt.savefig('3current.png', dpi=144)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x108f1fe10>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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SSiiFiKMNt60TRobwo/uiXa7IPONHRzANqqFUMiWUQsTRhptKBWMK6ShJZG5x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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108f06780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.xlabel('$t$ (ms)')\n",
"plt.ylabel('$V$ (mV)')\n",
"plt.xlim(0, 30)\n",
"plt.ylim(-20, 120)\n",
"xticks = [x * dt for x in range(T)]\n",
"plt.plot(xticks, vts)\n",
"plt.title('Membrane Potential $V$')\n",
"#plt.savefig('3potential.png', dpi=144)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Noise Resistance"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"vt = mt = nt = ht = 0.\n",
"it = [np.random.normal() for i in range(T)]\n",
"vts, hts, mts, nts = ([] for i in range(4))\n",
"for t in range(int(100 / dt)):\n",
" vt, ht, mt, nt = update(v=vt, h=ht, m=mt, n=nt, i=0, dt=dt)\n",
"for t in range(T):\n",
" vt, ht, mt, nt = update(v=vt, h=ht, m=mt, n=nt, i=it[t], dt=dt)\n",
" vts.append(vt)\n",
" hts.append(ht)\n",
" mts.append(mt)\n",
" nts.append(nt)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x109542518>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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vqpamX4pvF4vd9tP3kCW46B/d73KJ0kD4tDtI/R3Tj94PcXSgq/P08AeoRQaX\niIGX6SUYABt1Q5jkuD1tsvrzklMxtnpZg8ssKeXBAL6EUy0GOKP4z4zepH5p7zxSQd7gUu1nnf/v\nq8LrauellOC4E5NT6Bc9usBji62dqdhjnpdRF7I++K25Q0N8ySXqO68/fDPV7vSM74seIR3ZoB8R\nXOw/nZJqn5F5iRicGr2B5zuS9hkpBkr4SdXiQwCB7MHlEQDjUQgsAPArtNLgEsvznPvmaSeqxXuH\nV+HHrhsXdadcgv3688UL2zZEV43ln/FRRkNlTVgaM1o/iZCOGBXzQ8K/dYVviLS3t2cSnnaT/KDg\nWhQ7KWYVZW5zUUo96F0gpTzaQH7qTuQcSNVW6yUXU8KqHyrVzbaWLF3SwpOLZjT2Y9hJnypa6dOW\nNqBm7UhAADKOcwkGFndZhoeetwJJB8C1tBXg+gogPJBkLAXVE11ipHxNCevRF8bUE19NWcbgUo6s\nI/RPA7CTUup09/1hAGYppd6L39IsKeVBAG6GEyTvUUoNb8n917TIB5a1MiUGs7ZaUyMaxckYe4iZ\nh8ytqLLWnbQD0Dz02J3McgsjOUpISmkBuB3AgQC6w3m8cvZncnrob0IeOkVUS36sgyoxWqFlDS4r\nKaWCXXI2KTczKe0EYIJS6lul1DIAowAkHCEWLzhlPlHNSTBxKlE1ZW3QXyilfAbAm3Bq9/cFkL3T\nfjbrAfBW5k6BE3Bi6QQDkpKsQ0RE0TIFF6XUnVLK/wI4Ek7p5yqlVInn7bY8KWUPOA83AwAopWC9\nHdHH3sOu8uA6IqJaI6Uc4nk7Rik1Jm79xMFFSrkFgPlKqWkAoJR6E07JpVqmAvDM546u7rJm7sGP\n8SwarM+8tHTKaWYnJSJaASilhqRZP03JZRoAKaXsCmdk/hNKqWp21fkAwKZSym4ApgM4BkD8gw2I\niKhFiCzTWbulmF5wgtNLSqmqNFK4XZFvQaEr8rUlNtHTpkU/czsv1XMViIhaufWf+RBIOXouU3DJ\nc7sD7w9nqv2fADyqlKrlPpIMLkREKbV4cPGSUq4CQAJYF8DnSqmYhz5UTbLgcvEpwM/pphanBLbe\noTBxYmvXsTHVA51atS4bACGP6qX6kSW4GJuASik1Vyk1Uil1JYDPTaVbDdZVd1U7C61S7pw/VzsL\n5Vt19WTrcRxKQduG0uuUY71ulU2fMskcXKSUm3heHyal3C//XimV7iHkNUY0tCs/jSNPMpATSmSL\nrVtsV9ZpxrUrAAAcvklEQVQl1xY/nySE2PvgFshN5YkDjwAAWFffnTkN67QLTGWn2Ha7wTrrssql\nX2esC4ZWOwvNyim5XCql/MJ93PE6SDCAsdXIPwM7hthh9xbICLU4IRLNOG39vpXcXHTq7Px/jbUz\nJyHW6WooMyFp53KAVfzI8RWW50bL+5jmasgcXJRSpymlugMYDie41NE0rWUq9YhYMmOjzcOXd9kg\nfHkW66yXcgPRaqf0F3sdVLzQfUiWiDvm9boB667vT2v33ybb6VpdkmYvnBCFloDV1syezprr+JPt\ndUL2tKpEHHgkhPeBax0bq5cZGGhzUUp9opQaCmfsS6vRXv4h+sNae5ywwWe3iF32MZZWqFU6J17V\nkoXqJ3HQ7wsf5AJ3qmWcD7HVthk2Mv+sHHFqBauONtg42XqNhRK5OO1C50WCGylx4JGweh3vvN5x\nT+f/J53j/v9sAIAV1d62UodkeYviPfddN8ycjNjEP+et2HTLzGlVi3XUyb73sTcELaCcNpc3pZT/\nkFL2kVJ2B7CNwXxVnRV3R5vkkactGYDWWCv6s1/9JlVS4rAKP/MtzRfes67YdCvPcoPBNG1w8d4p\nZ9lf/qIdXL7l/2VPtNQ+vWlvEDK/bP5u19vwnv/bJ+mYIADd5P4m3PZKIQSsu56Atcf+zvutd0ib\n7XjeklL+p5bwN2ddfHXI0sBJ3XgLWNfdF5/OmYUHBYr9kg9fEIfVz1hva/Ct2bctY797wZl2fzMA\n9wM4V0p5lZRy+zLSrB1xd2xlBA5r0I2whgV6o4VU/4g+7rMk2mSdWxTIjRwNsf5G8SsVdV5wf2Rl\n9PCxrhwR/WHWwLDqap40glf3FgzkbXIoGV1iOoRYO+8NrLZG8QcCZXVMsIYm/Ju3bx/9eVjbhV06\nuIi2DZ71CudCBEuYlZLy+fZi81+HLPSfU9G2AaJziZ6BnVYtvG6fvAQmtt2l9ErbhK8jfntY4v2U\nKzdyNEQZpcFy2lxspdRbSqnLlVI7wnmmyjgAfTLnppbEBZCYz/LVAljZU9/ZuErh9XrdINb21zOL\nvYvruq09D4DoeTzEDnuUzmtYIMy1Cf2s8S/3+vcdKEo3bz7isdL7zQsGsMaYDg9xJZdum4bnZeRo\n399QbBG4OJRVSky5bS5BsO9W4ukToQG2zCqM1dYEOqwc/pm3Hj7sb5X/21oC1oDr/J81Jbhwd98W\n4jc7Qhxzess1rnuPI+73GFXd6P1NAoCV4e9vp//eJbrhA/znzGsTI4+sahElg4uUciMp5TGl1lNK\n/aiUegjAUPdJlfXNc8dmXX6z/7OIL7PYcU+I0y50vkArdywsP/RoYPPuEIcfF3lxskIu5tahRxd6\n68QJy0/+t+Kp0xY9DoG1nr8x3Nrnd8HECi+Tll6CF5SYACJ229e/IJcr5DG4nfe998ffsZOvDt86\n4IjovLVfyVnn9kej1/Hy3o2GyeWK87ndbpl6U4lDpOeNBRERXAH3OxSnbUNMFVYhv9YBvfwf/d9O\nsC69rjkPhU3yJdi28fsFINp3gFipA6zfHpos+Aa33/OA9Nvkq6G09n3/cyNH+9azdulR1FgPANis\neyDBwrFb519RvH77lWD1HxRYGPjd5UueYfsDIHZL2MkBcaW++ulMUjK4KKUmAXhPSjlcStlfStld\nSuk7QinlylLKfaWUwwAcD+CeCuW3xbTZentgu12dN+t18weYiGK4dfrFvt4a1i3/hDjpbFj7Horc\nxdfAOuyYyEY2EXkhT3B3FFZ14f5YxCG9gXbOBVbsvDeElYPYNabR3ru7pCWC4F1WTHCxeh4P0fO4\n8HU9r62B1wfSDFz4lrvPN2/bAGy2FSLlL9huHsVOe0WvG8xPmEAgtQbfityZAyKPA0DxwEv3c+sI\nT48kEV2KFMedAavn8bAuCmsrgHPjIgTQFP/Md2vQjcD6/lKVWHMdiHwvq5C7ZdFlA1jD/aXd+Lvn\n9HfzVp/0jxO28qV9rUues1ySMTre9r2wdriVG4H1Ax0jAr8P64gTi/cX1saVRETJRXReLXS57+Ym\nQfWqdc3I4rR32jt8nz2yjdlKVC2mlJqklLoEwBtwJqx8Qkr5gpTyZSnl8wDuANANwPVKqVuUUjXW\nnSo9a7U1YZ1yXvN74e1xk/DoRIeVmxs0Y9fbONBTJeGdnHXXE86LsDtW97ci2jYU1fFbfzg/UfrJ\nDzT44/a8D7uL8yUbEWyD7VDeH7KwoPPBxbIgVm7EKvc9UyKP7v8OP86/3P3xF4JOeH6a71pzbXyr\n5OukrfOvhPXnW/zb5EtLG24WyEvEMQeXu0FTuFU4werAwoA5d7uVS3Q9bdsAsXqwu65nn2F3y902\nhQi2EeXXiwvqScVVae11oP+92/OsSBljcAqJl7ipECFd0H3fyfDNrJRdmq0z/uS+KFyaxannwxrs\nfLd8HVuC+Qt7HWWV4iBl9XVqXdDGX1q1jj+zdHohUpVhlVKfo86ndkklf7fsPW+9ToAe/bDZ3azr\nGWT26+1K38ltthUwYVyh6BxaHeLJdP5HkORL56vL9ixv06ZQWhAi8MMKpGuVuGB5t7VifhTe98uX\nOauf8Sdgq22gP//A3d4tkUR1aV2pA6yBN6AQXQK76Lw6ciNHQ4/9CPr9N8LTAICtd3Da0yyrOBE4\nJQDPO+e/7dyeU9vvCv3pu+Hpdl4DmPNj6EeiU2fnFHjaUqyBN8C+2ulxFuxhZl1+M+wHbss+f5uv\nLcg9hqJgVFhPrNMVesK4bPtKq2MjxMZbFN3uaGhY+e9Jmra3olOYJLgU3lrD7wVmTvWvs/EW6HSH\nwsL8Jr1OgNh6e1gjHoPd76jiNDfvDnz9BcSxp0P/0yntiB32AP56na+EbO2yD/RP4d+R5nVOPMvz\nJkGZQcA5j2G1MIa6MJvvsN+aNF/43B/angdA7HMIEHX3YIDoUHrgk3XBMP/o29CSi+cLEtLgL470\n97sQh/SGOPo0+CJKfgDdrvtCbLebd+1AhoLVYlb46xD+doeYEtDqa8E6ZzDEDntAdOgI8Wu3U2K7\nkN5PXlpDbLRZIW23irAoH/n0gofm1r8Ly3KqPTP88ETwLtFb/efekTZXlQ0dAevuwhPDO93+CLCl\np5d/TGcBsUpniLg2o7CmOW8XWivZANHm4wm7mMc0clvD7oJ12Q2BhdHfD+2Z7DJ300PAmus6byJ/\nfwmCi3sTIIpOdILg4t2moSFwYRZO92tvMHZvCpqrvAN/L7HmOsB2u4V32klY1dw8rshzoyF23Rco\n1SNNWLD++kThfYJZR9JicIkTOKFWn/4QHTrCunCoUwWy/W4RG2bc3YFHQOwTqN8M+QGLNm0gvD2y\n3IGJ4vg/elby1CEf0tt54RnZLvbvBWvonQAA69p7IHoeByvYV9/dtfWH84C1PeN+gj/EogDift7Q\nENpA7r2gWb+TRZ+HEUJAbF3o5S46doJ11V2wBvwlfrt8t+B8lts2NDf6WhcOC9vC/3bdkNkAUgQY\n63YVMobFc24CVVlina6+AGatsZY/oJXcd9wFNuS75L0Yti10oRZrhTdKA4Doc1ZxO0yeW8IM3W7t\nLhDBKsL2/mBvjXjc8yb88pS7xHlskzjlPFg9jy/6vKgDDlDo2h+UnyGgZLWY5V8n16Y5kIoT+xVV\n4QGIL0lttyvEDnsgd+aAohsCq99AiB6HBPYfnz3f9rv0QK7fQOfN6oUxcNagG33peb9XuZvMTxXD\n4BIrX5US6ANv5SDW3wjW/r1CtsnOOuqU6DrVCGKvg2D1vRDW7Y/C8n4hvb1fdv+t04PN23OsTRsI\nd6CoWH1NiHwx3Pd70OGvi6rBAl8jN/hYA29werwF8xzVZTafbjAgRBBrdQmvtvGu01xfLHx5A1A8\n9cuvt4PYfjf/hah9eEknfqee4OGWrMTJ5xY+T9rtNeSCF1pyShrsYi521sDrfZ0dRLdNi3peNX/W\nrr3TDhOW3rIUD6ft2MnpYeZNO653WvBrt9u+EO5gSnFiv+b2DREyI4EV0Y6Zy8+AXmr8lUChdDn8\nXve35By/tddBEKtGNLRHyJ15aaG0HNzVtrsAnt6mzk6Kq5fF6Ren2qfotinQNd8NOmE7XRlSBRcp\nZSuZjS8h9yJQ7WkU4lgn9oPYcLPm+n10dhpfxf49M6bouWBstHlxYzRQ/EOMq86Kmh8s7G/a0W24\nzgeklH9361J/KUb06V/oEt6cVnSauXOHwDqmr/9C1K5d5EU2DW+bjNise7I5tVrweyc22txpw+u2\naYqLVkhwSTH9vfXnWyAiBgsCKKrOFW3aFvcizKe110GxXbnzeWseQBnMZ5LqwE6rwrr46kIHh1K1\ncEWfp2kT8v/GRKdVC1WKboCwdtwT4tCjIU7sF5OHQI+2y9y/X/54K/gVS9spvZ+UchGA15VSMyuR\noZpS6m6mnDmtDjwC+oUnSq+YUu66iOqKpDzHFD5NBiD2/R3wy0Igl4Me81xMsAnUU3utv3FR11mx\n8sqBn1+2b77oeTzEbvsWutjCc4Pgu4gkSN/YVDOev2toFY2hX3nYSPHmpN08dFoVmPdz6OYilysM\nBC6lobi9SxzSG+LgkMbrAGutLvEj4LtsAOuAI2B/M943jXxRL8IUckNuK6TzO+mfry5JtRjgH92f\ncmaAVL20Q0q3+SpFseMe0FMmOa+7bAARM5Gr2HYX6FeeLl4ed7wN7dKVQCOkDS5z4IxjuUtKOR3A\na+6/MQCOUEr9rewc1RAhRPxd60pu9c6mWwLfjE+VtnXUKWj6YQbw8TulMpEqXZNEm/AqCnFIb4gO\nHWE/5FYpRFSLATrymmm59eYRe06Vz6K0YwccJm+/EPseWl41VBrB6qB11we6p59UUxzRp/hiUqGB\nAeLIPkVjIERYl93QjUt8vHn35irJSsy7Jiwr8L1N0qAfUPLvWsYf3tBNjXVMX9gN7YAF86J2VLzN\n5TcZmfk97RFcpZTqCWANACcB+A7AaQAmAYhvWW2FxHobwCqnISxJycc36+tGxfOSmZbl91DU8Fr6\nAi4a2pV+KNtqaxb1uY+1xlqx83qlJfY5JHx59+1KbBh2zDHVcSNHF/0tclfeAWv3/SK2iN6faNcu\nfIR5BYj2K8XeNcdvXOrSU14AF336F8aMJJFvJ4zqqBAaXAw+eiNiDq/QZ7KkrDGxjuzTXFp2qhY9\nPfbC2vVWXyvQtT6btONc/uP+3wbwofvvOillWwDhlaGtnOjYKXv1WJLNOnga9lZfs2heMvMiMhWT\nV2u338LOjzsBQqvFfN0eE+ZBNHZC7s7HS6zr2W2nzsjdUWKaF+9vqdR4gMbwbr2i8+qwbnsE+DJi\nyFeFS5ti572h33u98D7wLJWwrrrW+VcA623oTyfj4DgjYjo1WBdfA6y3AfD9pOzJp55Sxs1PVFVd\naFfdEj/gosbymPUjutSLuHn6MhIbhbSjVoCRspdSahkAsyMLCQAgDjqyrEfMppaoNOV/K7bfzTcP\nmRAC2GZnpyRRQ50hxDF9m/NpDbqpeeR7prTarwSxzc7hn5meXj5o4y2aX1ojHoM4pvRUfmKrbYuq\n+KyM03qYEf29EJt3d7pot+R3R2TovLN69MwA1vUPQOwcPp1KqBQldLHdrs5YFhMq+DfOPp97gFLq\nPVNprThKX8hFm7aFKVRa4se25roJHhhWOt8597nmunmAZ4K85ycSKFXtlJHlma5clJq5OOs+RjyW\nriovC099eOicdEUzHZT4vBoSdcduwXxaVnz7alj10fobRXfXDnsoXrCE6d39MX2hp3/vSSAmK+uu\nD/GH86JXqBHGgssKLXO1WO1NwSbatYM4NencY57tDugFPX0K8PHbgQ/y/09+oQgbp1AvoicgNbiP\nzbtDxz5nI/5vbf3pGmDJEqN5Sq8GAlwaZQZk687HY2eMFl039D87pXFVWO4NWkXVQ8llhZZyAFUm\n/7dT5KylLS4kKFq9ToCePQt2MLggeXWD+J0Egs9qoSJig02Qi3tCYLAtKViNWe5z600w0KOsnkT1\nvIxcP1+1XHGV+yNzhL4B1h/Oh3XDA6m3K9lbyiPXfxCsHRM8OKwlRN2dhz5XRkDsnuw5FmKtdWGl\neOZFxdXpxU3kcrDOGWw+4Q4dS6+TxNrroU334qeip3lUsHnxtQhRo+nrXq5yIYAlFwNEu/alJ1AM\n2+74M0s/BKrG+OqYgxffsHnQhPBPfVInrMtugDB1MQUSPXTLqO7bwLrkWtjDBxgLkmL/ntATvyw7\nndywO9GhsRHz58/3p29iCv8KKXrIWithXXQ1sGRxRdJmcKkisXLH4jmEalbttQ9VUtEEi+XacDNY\ncVVZhgkrZ3z27vjBqZVQ/aJj86SvrZSJ8SxRGFzInBrsoFArhBCRA+UqaoutK981uhXLP12S0mNw\noTIU1YtVJRcULXfRVdXOQnYt1GVabL87xLZmH59BDC5UjmDXSsYWKkfMoMRKsv54SVX229oxuFBm\n4vBjIXbyzKCbtVpsna7AnNlmMkV1KXQwYtcNzY1EpxbH4ELJhPUEW6mD/3ktGXrMAe4TIQ3Mwkqt\ni1ipQ12MRKdwDC5kjOi8OqxbR6XfLmNQIqLaxUGUZJT3UcpEtOJiyYUSETvtBSxcUO1sEFGdYHCh\nRESXDSCO/2O1s0FEdYLVYkREZByDCxERGcfgQkRExjG4EBGRcQwuRERkHIMLEREZx+BCRETGMbgQ\nEZFxDC5ERGQcgwsRERlXl9O/SCkHA+gLYJa7aKBS6vkqZomIiDzqMri4blRK3VjtTBARUbF6rhZr\nmQdsExFRavVccukvpTwRwIcALlRKza12hoiIyFGzwUVK+RKAtT2LBAAN4DIAIwBcqZTSUsphAG4E\ncGpIGj0A9Mi/V0qhsbEx0f4bGhoSr1tPeFz1hcdVX1rrcQGAlHKI5+0YpdSYuPWFDnk2ej2RUnYD\n8LRS6jcJVtfTpk1LlG5jYyPmz59fVt5qEY+rvvC46ktrPa4uXboAKZsi6rLNRUq5juftkQDGVisv\nRERUrGarxUq4Tkq5DQAbwGQAZ1Q3O0RE5FWXwUUp1afaeSAiomh1WS1GRES1jcGFiIiMY3AhIiLj\nGFyIiMg4BhciIjKOwYWIiIxjcCEiIuMYXIiIyDgGFyIiMo7BhYiIjGNwISIi4xhciIjIOAYXIiIy\njsGFiIiMY3AhIiLjGFyIiMg4BhciIjKOwYWIiIxjcCEiIuMYXIiIyDgGFyIiMo7BhYiIjGNwISIi\n4xhciIjIOAYXIiIyjsGFiIiMY3AhIiLjGFyIiMg4BhciIjKOwYWIiIxjcCEiIuMYXIiIyDgGFyIi\nMo7BhYiIjGNwISIi4xhciIjIOAYXIiIyjsGFiIiMY3AhIiLjGFyIiMg4BhciIjKOwYWIiIxjcCEi\nIuMYXIiIyDgGFyIiMo7BhYiIjGtT7QxEkVIeBWAIgC0B7KiU+tjz2aUA/gBgOYBzlVIvViWTREQU\nqpZLLv8FcASA170LpZRbApBwgs7BAEZIKUXLZ4+IiKLUbHBRSn2llJoAIBg4egIYpZRarpSaDGAC\ngJ1aOn9ERBStZoNLjPUAfO95P9VdRkRENaKqbS5SypcArO1ZJABoAJcppZ6uTq6IiKhcVQ0uSqn9\nM2w2FcD6nvdd3WVFpJQ9APTw7A9dunRJvKPGxsYM2at9PK76wuOqL631uKSUQzxvxyilxsRuoLWu\n6X+9e/d+rXfv3tt73m/Vu3fvT3r37t3Qu3fvjXr37v1N7969RQX2O6Tax16hvyePq47+8bjq6x+P\nq/CvZttcpJS9pJTfA9gFwL+llM8BgFJqHAAFYByAZwH0U0rp6uWUiIiCanaci1LqSQBPRnx2DYBr\nWjZHRESUVM2WXGrAmGpnoELGVDsDFTKm2hmokDHVzkCFjKl2BipkTLUzUCFj0m4gtGaNEhERmcWS\nCxERGcfgQkRExtVsg341SSkPAnAznOB7j1JqeJWzZISUcjKAuQBsAMuUUnU5bY6U8h4AhwKYqZT6\njbusM4BHAHQDMBmAVErNrVomM4g4rsEA+gKY5a42UCn1fJWymJqUsiuAv8MZLG0DGKmUurXez1fI\ncd2tlLqtFZyvdgDeANDg/ntKKTUwy/liySVASmkBuB3AgQC6AzhWSvmr6ubKGBtAD6XUtvUaWFz3\nwTk/XgMAvKyU2gLAqwAubfFclS/suADgRqXUdu6/urlQuZYDuEAp1R3ArgDOcn9P9X6+gsfV33Od\nqNvzpZRaAmAfpdS2AH4DYF8p5e7IcL4YXIrtBGCCUupbpdQyAKPgTJbZGgi0gnOulHoTwJzA4p4A\nHnBfPwCgV4tmyoCI4wKKJ2+tG0qpGUqpT93XCwCMhzOrRl2fr4jjys9xWLfnCwCUUr+4L9vBuV7M\nQYbzVfcXmgoITow5Ba1nYkwN4CUp5QdSyr7VzoxhaymlZgLODx/AWlXOj0n9pZSfSin/JqVcpdqZ\nyUpKuSGAbQC8C2Dt1nK+PMf1nruors+XlNKSUn4CYAacaV7GIcP5YnBZseyulNoOwCFwqif2qHaG\nKqi19LEfAWBjpdQ2cH7sN1Y5P5lIKTsCeAzOw/0WoPj81OX5Cjmuuj9fSinbrRbrCmBPd47G1OeL\nwaXYVAAbeN5HToxZb5RS093//wDgCbSu5+DMlFKuDQBSynVQaFCta0qpHzzTG40EsGM185OFlLIN\nnAvwg0qpp9zFdX++wo6rNZyvPKXUPDhTbO2ADOeLwaXYBwA2lVJ2k1I2ADgGwOgq56lsUsoO7l0W\npJQrAzgAwNjq5qosAv667dEATnZfnwTgqeAGdcJ3XO4POe9I1Oc5uxfAOKXULZ5lreF8FR1XvZ8v\nKeUa+ao8KeVKAPYH8AkynC+O0A/hdkW+BYWuyNdWOUtlk1JuBKe0ouF0QX+oXo9LSvkwnEcprA5g\nJoDBcOahexTO4xi+hdNV8udq5TGLiOPaB059vg2nC+gZ+brveuD2NHoDzmPLtftvIID34UxAW5fn\nK+a4jkN9n6+t4TTY5zv/PKiUul5KuRpSni8GFyIiMo7VYkREZByDCxERGcfgQkRExjG4EBGRcQwu\nRERkHIMLEREZx+BCRETGMbgQEZFxDC5ELUhKebWU8hzDab4npdzSZJpE5WJwIWohUso1AJwI4K+G\nk/4LgKGG0yQqC4MLUcs5GcCz7tP+THoawD5Syrp9Jgq1Pm2qnQGi1kRK+QqAA5VSy0M+PhjAPZ51\nJwG4A0AfABvCmRhwIID7AewO5+FTvfPPKpdSXgLgbACd4DwGop9S6jWl1BIp5UdwHpH8YGWOjCgd\nllyIDJFSrgcAEYEFALYG8FVg2ZEA9gWwBYDDADwH53nlawLIATjHTXtzAGcB2F4p1QlOIJnsSWc8\ngP8zcRxEJjC4EBkgpdwfzlMHZ0gpT4hYbVUA8wPLblNK/eg+yO0/AN5VSn2ulFoK5xEJ27rrNQFo\nAPBrKWUbpdR3SqlJnnTmu+kT1QQGFyIDlFIvwQkANyql/hGx2hwAjYFl3md9LAp539FNfyKA8wAM\ngfNUwIellOt61m0EUDfPQ6HWj8GFyJxtlFIfxXz+OYDNsyaulBqllNoTQDd3kfdhb1sC+Cxr2kSm\nsUGfyAAp5VZw2j0gpTxGKTUqZLVn4Txp8p8Z0t8cwHoA3gKwFE6pxnI/awdgezgdA4hqAksuRGb8\nBGCulPIYAGMi1vk7gIPdYAA4j8b1inssbDs4JZUfAEyD0+B/qfvZ4QBeU0rNyJBvoorgY46JWpCU\nchiAWUqpWw2m+Q6AU5VS40ylSVQuBhciIjKO1WJERGQcgwsRERnH4EJERMYxuBARkXEMLkREZByD\nCxERGcfgQkRExjG4EBGRcf8PIzUgtr5Uho0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1090f0cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.xlabel('$t$ (ms)')\n",
"plt.ylabel('$I$ $\\mathrm{(\\mu/cm^2)}$')\n",
"plt.xlim(-1, 30)\n",
"plt.ylim(-10, 10)\n",
"xticks = [x * dt for x in range(-500, 0)] + [x * dt for x in range(T)]\n",
"plt.plot(xticks, [0]*500 + it)\n",
"plt.title('Membrane Current $I$')\n",
"#plt.savefig('7current.png', dpi=144)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x109789390>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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7f2rmUVIrohyHgE947+cBZwEfyXzeymX55bbvqqz1SUkvQ+/9fuB87/0pwEnA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\nzZ50uQEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10911c780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.xlabel('$t$ (ms)')\n",
"plt.ylabel('$V$ (mV)')\n",
"plt.xlim(0, 30)\n",
"plt.ylim(-10, 10)\n",
"xticks = [x * dt for x in range(T)]\n",
"plt.plot(xticks, vts)\n",
"plt.title('Membrane Potential $V$')\n",
"#plt.savefig('7potential.png', dpi=144)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysis in m-v phase plane"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"vt = mt = nt = ht = 0.\n",
"it = [10] * 500 + [0] * (T - 500)\n",
"vts, hts, mts, nts = ([] for i in range(4))\n",
"for t in range(int(100 / dt)):\n",
" vt, ht, mt, nt = update(v=vt, h=ht, m=mt, n=nt, i=0, dt=dt)\n",
"for t in range(T):\n",
" vt, ht, mt, nt = update(v=vt, h=ht, m=mt, n=nt, i=it[t], dt=dt)\n",
" vts.append(vt)\n",
" hts.append(ht)\n",
" mts.append(mt)\n",
" nts.append(nt)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"def m_nullcline(v):\n",
" alpm = 0.1 * (25 - v) / (np.exp((25 - v) / 10) - 1)\n",
" betm = 4. * np.exp(-v / 18)\n",
" m = alpm / (alpm + betm)\n",
" return m\n",
"\n",
"mnc_vs = [x * 0.1 for x in range(-200, 249)] + [x * 0.1 for x in range(251, 1400)]\n",
"mnc_ms = [m_nullcline(v) for v in mnc_vs] "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"def v_nullcline(m, n, h, i):\n",
" gna = G_na * h * (m**3)\n",
" gk = G_k * (n**4)\n",
" gl = G_l\n",
" v = (gna * V_na + gk * V_k + gl * V_l + i) / (gna + gk + gl)\n",
" return v\n",
"\n",
"p1 = 2500\n",
"p2 = 250\n",
"p3 = 300\n",
"p4 = 400\n",
"vnc_ms = [x * 0.001 for x in range(0, 1400)]\n",
"vnc_vs1 = [v_nullcline(m, nts[p1], hts[p1], it[p1]) for m in vnc_ms] \n",
"vnc_vs2 = [v_nullcline(m, nts[p2], hts[p2], it[p2]) for m in vnc_ms] \n",
"vnc_vs3 = [v_nullcline(m, nts[p3], hts[p3], it[p3]) for m in vnc_ms] \n",
"vnc_vs4 = [v_nullcline(m, nts[p4], hts[p4], it[p4]) for m in vnc_ms] "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x109a3db38>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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5ti7vssvsMnn4ISfHQEKCJ9ATHdQ58uWRjvN4hK69rY4xHD2K9fnnyXr/fb+q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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10953d4a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(mnc_vs, mnc_ms, color='r')\n",
"plt.plot(vnc_vs1, vnc_ms, color='b')\n",
"plt.plot(vnc_vs2, vnc_ms, color='b', linestyle='--')\n",
"plt.plot(vnc_vs3, vnc_ms, color='b', linestyle='-.')\n",
"plt.plot(vnc_vs4, vnc_ms, color='b', linestyle=':')\n",
"plt.xlabel('$v$ (mV)')\n",
"plt.ylabel('$m$')\n",
"plt.xlim(-20, 140)\n",
"plt.ylim(0, 1.2)\n",
"plt.legend(['m nullcline', 'v nullcline'], \n",
" bbox_to_anchor=(1, 0.22), framealpha=1)\n",
"# fixed point\n",
"plt.scatter([-0.732, ], [0.0486, ], 18, color='k', marker='o')\n",
"plt.annotate('P1', xy=(-0.732, 0.0486), xycoords='data',\n",
" xytext=(+9, -8), textcoords='offset points', fontsize=11)\n",
"plt.scatter([114, ], [1, ], 18, color='k', marker='o')\n",
"plt.annotate('P2', xy=(114, 1), xycoords='data',\n",
" xytext=(+10, +5), textcoords='offset points', fontsize=11)\n",
"plt.scatter([-10, ], [0.02, ], 18, color='k', marker='o')\n",
"plt.annotate('P1a', xy=(-10, 0.02), xycoords='data',\n",
" xytext=(-30, +7), textcoords='offset points', fontsize=11)\n",
"plt.scatter([20.5, ], [0.38, ], 18, color='k', marker='o')\n",
"plt.annotate('P1b', xy=(20.5, 0.38), xycoords='data',\n",
" xytext=(+9, -7), textcoords='offset points', fontsize=11)\n",
"plt.title('m-v Phase Plane')\n",
"#plt.savefig('4nullcline.png', dpi=144)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x109a3a0f0>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x108a8eda0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(vts[:2500], mts[:2500])\n",
"plt.xlabel('$v$ (mV)')\n",
"plt.ylabel('$m$')\n",
"plt.xlim(-20, 140)\n",
"plt.ylim(0, 1.2)\n",
"plt.legend(['simulation'], \n",
" bbox_to_anchor=(1, 0.15), framealpha=1)\n",
"plt.title('m-v Phase Plane')\n",
"#plt.savefig('4m-v.png', dpi=144)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysis in n - v phase plane"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"vt = mt = nt = ht = 0.\n",
"it = [10] * 500 + [0] * (T - 500)\n",
"vts, hts, mts, nts = ([] for i in range(4))\n",
"for t in range(int(100 / dt)):\n",
" vt, ht, mt, nt = update(v=vt, h=ht, m=mt, n=nt, i=0, dt=dt)\n",
"for t in range(T):\n",
" vt, ht, mt, nt = update(v=vt, h=ht, m=mt, n=nt, i=it[t], dt=dt)\n",
" vts.append(vt)\n",
" hts.append(ht)\n",
" mts.append(mt)\n",
" nts.append(nt)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"def n_nullcline(v):\n",
" alpn = 0.01 * (10 - v) / (np.exp((10 - v) / 10) - 1)\n",
" betn = 0.125 * np.exp(-v / 80)\n",
" n = alpn / (alpn + betn)\n",
" return n\n",
"\n",
"nnc_vs = [x * 0.1 for x in range(-200, 99)] + [x * 0.1 for x in range(101, 1400)]\n",
"nnc_ns = [n_nullcline(v) for v in nnc_vs] "
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"def v_nullcline(n, m):\n",
" i = 0.\n",
" h = 0.8 - n\n",
" gna = G_na * h * (m**3)\n",
" gk = G_k * (n**4)\n",
" gl = G_l\n",
" v = (gna * V_na + gk * V_k + gl * V_l + i) / (gna + gk + gl)\n",
" return v\n",
"\n",
"vnc_ns = [x * 0.001 for x in range(0, 1000)]\n",
"vnc_vs1 = [v_nullcline(n, mts[2500]) for n in vnc_ns] \n",
"vnc_vs2 = [v_nullcline(n, mts[150]) for n in vnc_ns] \n",
"vnc_vs3 = [v_nullcline(n, mts[200]) for n in vnc_ns] \n",
"vnc_vs4 = [v_nullcline(n, mts[250]) for n in vnc_ns] "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x109a2bda0>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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5chwOB3a7na1bt8o2EH8KxCqshujWrYSJE6smlrQ0A7m5vv18UWvWED9rFhnv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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10942d0b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(nnc_vs, nnc_ns, color='r')\n",
"plt.plot(vnc_vs1, vnc_ns, color='b')\n",
"plt.plot(vnc_vs2, vnc_ns, color='b', linestyle='--')\n",
"plt.plot(vnc_vs3, vnc_ns, color='b', linestyle='-.')\n",
"plt.plot(vnc_vs4, vnc_ns, color='b', linestyle=':')\n",
"plt.xlabel('$v$ (mV)')\n",
"plt.ylabel('$n$')\n",
"plt.xlim(-20, 140)\n",
"plt.ylim(0, 1)\n",
"plt.legend(['n nullcline', 'v nullcline'], \n",
" bbox_to_anchor=(1, 0.9), framealpha=1)\n",
"plt.title('n-v Phase Plane')\n",
"#plt.savefig('5nullcline.png', dpi=144)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x109bbb160>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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VbC8dEWzoIpHTJ2MrTF7PYH+QtathmFaki3QWv30brFqOX/EB/sP34MMlUFCI\nGT0BM2ka5ryLMf2LAMgtLMRs3RpxYmmiAtkHM2I0/uMPMCoQkZTw27bCmo/xqz+Cjz/Ef/whbNoA\nww7AjBhN7KgT4YLL9hSGZDYVyL6MHA0rP4RjT406iUhW8fU7oboCv3YNVHyMX7MSVq+AnduhfGSw\nefz4Q4md/gUYOgyTo0VS2UgFsg9mxGji4ZYeIrI37z3UbgiKomoNrF2Dr66AqjWwdUtwPp0hZZjy\nkcSOOxXKRsLAwVrp3YWktUCstacDtxDsAX+Pc+7nrUw3DXgJONc591gaI+5t2CioXIXf3YDJ7RFZ\nDJGoeO9he12wQcn6KqiqgKqwMKoroWfPoCSGlMOQcmKHHA5DyqG4RCu6u4G0FYi1NkZwLpGTCM4r\n8i9r7RPOuaUtTHcD8Pd0ZWuN6dkTBpXCqo9g1Nio44h0Cr+7gcaqCvzHy4OSqKnCr6+GmipYXw0+\nHhzap2QIZnApTJxM7KSzg+LIL4g6vkQonXMg04EPnHMrAay184BZwNJm010BPAJMS2O2VpkxE/Hv\nv4NRgUiW8rvqg0VNG9fjazdAbQ1sWBeUxfoq2LyRbQMGEi8ehCkZEixmmjoaM3AIlAyGPoVa7CQt\nSmeBlAGrE4bXEJTKHtbaUuBzzrkTrLUZcTAbM/Zg4i8+B6d/IeooIp/iGxqCQqitwdfWwMam60Fh\nUFsDO3fCgGIYUIwZMBAGDIQRo4lNPRoGDoGiEvoOGMBWbR4r7ZRpK9FvAf4jYTj6rz0HHQz33Ypv\nbNSWIpJWfndDMOewpxBqoHZ9wvUa2LEN+hXBgIGYorAchgwjNuEwKBoYFEdBP0xMB96W1EtngVQA\nwxOGy8NxiaYC86y1BhgInGGtbXDO/SVxImvtDGBG07BzjsLCTjppTGEhWwYOJn9DFbkHjuvQQ+Xl\n5XVezhRSztRqKaffvRtfW0N843riG9YT37CO+Ib1+D3X1+HrtmL6FxErLiGneBCxohJiww7AHHZE\ncL14EKbfgJSUQza/l5koW3ICWGvnJgzOd87Nb+t9jfc+5YFaYq3NAZYRrERfC7wKnO+cW9LK9PcC\nT7ZxKyxfWdl55+6Iz7sbCvoSO/vcDj1OYWFhViwmUM6O8Y2NsHkj1G7Ab6yh5/at1FdV4JvmGmpr\ngs1cC/sFh+hoWqxUFM5F9C+GohLo1z9tWzJl6nvZnHKmVmlpKXRgSU/a5kCcc43W2suBZ/hkM94l\n1tpLAO/z6safAAAP/0lEQVScu6vZXdLTbG1gDp1O/PHfQwcLRLKfjzfC5k2frHdoKoWN4TqI2g2w\nZRMU9A0XIQ0kPnhosP5h1DjMgOJgfL8iLRKVrJe2OZBO1qlzIH73buJXX0Dsx7d36BAL2fKtpLvm\n9PF48OFfuyFY17Cx5pN1EBvXB9c310KfgmCOoWm9QzjXYIpKwnIYsNd+Q9nwfmZDRlDOVMuaOZBs\nZnJzMROn4N9+DaPDmmQlH49D3eZwc9aavRYn7bm+eSP07hOWQ3G4UroEhh1ArGkFdf9iTA/tVCoC\nKpC2O3Q6/pUXdFysDOV3bA82W90YzjnsuR7OOdRugF69oH+zdQ1lh39SDgOKMT3yon4pIllDBdJG\nZvIR+IfuwG+pxfQdEHWcbsXv3h0csXVjuCgpvNRtqaVxXVWwSWvjbigeFCxWKg4XJY07NKEcBgZH\nFhCRlFGBtJHp2Qtz6HT8q//EnDwr6jhdit+5A2qqoaYav2H9nh3g/Mb1sGE9bN0M/fqH6xxKgi2U\nykbQc9oxxHsXQHEJ5Bdob2mRNFOBtIM58gTij94PKpB28Q0NsGFdWBDrPimL8Ce7dkLx4OAQGsUl\nUDQIho8i1lQW/VveYqlHYSE7s2BFpUhXpQJpj3GHwLat+BUfYA44KOo0GcXv3A7r1uKrK4OjtK6r\nDA/IVx2svO5fHBTEwKAomHwEsabrfftr7kEkC6lA2sHEcjAnnIV//i+Yr18ddZy08w27YN1aqK7E\nr6vc6yc7tkHJUBhcihlUCgdNJHbUSUFBDBiofR5EuiAVSDuZY04hfu1F+E0bu+xpN/2uXfg1K/AV\nq6ByNb5yFVSuCjZ1LS6BwWWYQUODA/JNPw4Glwabt+p4SyLdigqknUyfAswRM/DPPoGZfWHUcTrM\nb9oIqz/Cr1wenKe6YhWbN64LjtJaOgxTOhwz/ThM6XAYPFQn1hKRPVQgSTBnfJH43Cvwp8zE9C+O\nOk6b+U0b4aNl+FXL8as+glXLYfduGD4KM3wUZspRmJlfonD0WOp27Iw6rohkOBVIEsyAYszRJ+H/\n+ifMv10adZwW+cZGWPMxfvkSWL4s+LljO4waixk5mtixp8LwUVBU8qkV2MFchgpERPZNBZIkc8Zs\n4tddhj/2VMzwUVHHCQ7yt3oFfsli/JK3YPnSYI/r0eNh/KTgSMKDS7WeQkRSRgWSJFPYF3POBcR/\nfzuxa/47bYfdbuK9h+oK/JK38EsXw7J3oLAfZvwkYsefDhf/H0yf7DgfgYhkJxVIB5ijT8a/Mh//\nt0cxZ9lOfz6/aUMwd7FkMX7pW2DAjDsUM/lIzHkXB4cKFxFJExVIB5hYjNjXvkP8/16NHz0eM/aQ\nlD6+314H778bLpZaHBxKfNykYC7jLAuDhmoHPBGJjAqkg0zRQGJfvYr4Xb8gdtVPMOUjk3ocv2M7\nVFXgVy8PtpT66P3gmFCjxmLGTyb21W8HW0uleVGZiEhrVCApYCZMxpz7deK3zCV22ff3OsyJ31JL\n/IHbiX3mBHbl5RHfshm21cGWWti8Cb9pA1RVwPa6YCV3+UgYNZbYiWdD2UjtwS0iGUsFkiKx6cfh\ne+QRv+0nmNM+jznx7ODEQxWrYPGrxN95g4bpxwIG8vtAvwFQOpxYv6JgT+6iEm0hJSJZRQWSQuaw\nI4mVDifu7sH//TEYMxHq62HAQGLfv4k+5cOz4jSXIiJtoQJJMTO4lJwrfoivqcYvX4rJzYUJh2F6\n50cdTUQkpVQgncQ0HbpcRKSL0kJ3ERFJigpERESSogIREZGkqEBERCQpKhAREUmKCkRERJKiAhER\nkaSoQEREJCkqEBERSYoKREREkqICERGRpKhAREQkKSoQERFJigpERESSogIREZGkpPV8INba04Fb\nCIrrHufcz5vd/iXgP8LBrcA3nHNvpzOjiIi0TdrmQKy1MeBXwGnAROB8a+24ZpN9BBznnDsU+Clw\nd7ryiYhI+6RzDmQ68IFzbiWAtXYeMAtY2jSBc+6VhOlfAcrSmE9ERNohnetAyoDVCcNr2HdBfB34\nW6cmEhGRpGXkOdGttScAFwLHtHL7DGBG07BzjsLCwrRk64i8vDzlTCHlTJ1syAjK2RmstXMTBuc7\n5+a39b7pLJAKYHjCcHk4bi/W2knAXcDpzrnalh4ofIHzE0Zdt3Xr1pQF7SyFhYUoZ+ooZ+pkQ0ZQ\nzlQrLCzEOTc32funs0D+BYy21o4A1gLnAecnTmCtHQ48ClzgnFuexmwiItJOaVsH4pxrBC4HngHe\nBeY555ZYay+x1l4cTvZDoAj4tbX2TWvtq+nKJyIi7WO891FnSAVfWVkZdYb9yqbZWuVMnWzImQ0Z\nQTlTrbS0FMAke3/tiS4iIklRgYiISFJUICIikhQViIiIJEUFIiIiSVGBiIhIUlQgIiKSFBWIiIgk\nRQUiIiJJUYGIiEhSVCAiIpIUFYiIiCRFBSIiIklRgYiISFJUICIikhQViIiIJEUFIiIiSVGBiIhI\nUlQgIiKSFBWIiIgkRQUiIiJJUYGIiEhSVCAiIpIUFYiIiCRFBSIiIklRgYiISFJUICIikhQViIiI\nJEUFIiIiSVGBiIhIUlQgIiKSFBWIiIgkRQUiIiJJUYGIiEhSVCAiIpKU3HQ+mbX2dOAWguK6xzn3\n8xamuRU4A9gGzHHOLUpnRhERaZu0zYFYa2PAr4DTgInA+dbacc2mOQM40Dl3EHAJcEe68omISPuk\ncxHWdOAD59xK51wDMA+Y1WyaWcADAM65hUA/a+3gNGYUEZE2SmeBlAGrE4bXhOP2NU1FC9OIiEgG\n0Ep0ERFJSjpXolcAwxOGy8NxzacZtp9psNbOAGY0DTvnKC0tTVXOTlVYWBh1hDZRztTKhpzZkBGU\nM9WstXMTBuc75+a3+c7e+7RcZs+enTN79uwPZ8+ePWL27Nl5s2fPXjR79uzxzaY5c/bs2X8Nrx85\ne/bsV9r42HPT9To6+B4op3Jm5CUbMipn5uVM2yIs51wjcDnwDPAuMM85t8Rae4m19uJwmqeAFdba\nD4E7gW+mK5+IiLRPWvcDcc49DYxtNu7OZsOXpzOTiIgkp6usRJ8fdYA2mh91gDaaH3WANpofdYA2\nmh91gDaYH3WANpofdYA2mh91gDaa35E7G+99inKIiEh30lXmQEREJM1UICIikpS0rkRPNWvtfwOf\nBeqB5cCFzrkt4W3XAF8FdgNXOueeiTDnfg8iGQVrbTnBoWMGA3HgbufcrdbaAcDDwAjgY8A65zZH\nFjQUHk/tNWCNc25mJua01vYDfgscTPCefhV4n8zLeQ3wZaAReBu4EOhDxDmttfcAZwPVzrlJ4bhW\nf89R/Z+3kjPjPo9ayplw29XAL4CBzrmNyeTM9jmQZ4CJzrnJwAfANQDW2gmABcYTHNn319ZaE0XA\nthxEMkK7ge845yYCnwEuC7P9J/Ccc24s8A/C9zUDXAm8lzCciTn/B3jKOTceOBRYSobltNaOAC4C\nDgs/VHKB88mMnPcS/K8kajFXxP/nLeXMxM+jlnI2fXk8BViZMG58e3NmdYE4555zzsXDwVcI9lwH\nmEmwn8lu59zHBL/M6RFEhLYdRDISzrmqpsPlO+fqgCUE7+Es4P5wsvuBz0WT8BPhH/yZBN/um2RU\nTmttX+BY59y9AOHf32YyLCewBdgF9LHW5gK9CY74EHlO59yLQG2z0a3liuz/vKWcmfh51Mr7CfBL\n4LvNxs2inTmzukCa+SrwVHg9kw7K2JaDSEbOWjsSmEzwhz/YOVcNQckAgyKM1qTpDz5xs8FMy3kA\nUGOtvdda+4a19i5rbT4ZltM5VwvcBKwi+N/Y7Jx7jgzLmWBQK7ky6f+8uUz9PMJaOxNY7Zx7u9lN\n7c6Z8etArLXPEiyjb2IIPkS+75x7Mpzm+0CDc+6PEUTMetbaAuARgmWeddba5tt2R7qtt7X2LIJl\nuIvC46C1Jupt0nOBKcBlzrnXrLW/JFj8kmnv5yjgKoJ1CpuBP1lr/62FXFG/n63J1FxAZn8eWWt7\nA9cSLL7qsIwvEOfcPl+otXYOwaKNExNGt+mgjGnSloNIRiZchPEI8Hvn3BPh6Gpr7WDnXLW1dgiw\nLrqEABwNzLTWnkmwuKXQWvt7oCrDcq4h+Gb3Wjj8KEGBZNr7ORVYkLDi9HHgKDIvZ5PWcmXS/zmQ\nFZ9HBwIjgcXh+o1y4A1r7XSS+KzK+ALZl3Drpu8Cxznn6hNu+gvwYPgNsAwYDbwaQUSAfwGjwxWX\na4HzCFZYZorfAe855/4nYdxfgDnAz4F/B55o4X5p45y7luBbE9ba44GrnXMXhFu9zCFzclZba1db\na8c4594HTiI47tu7ZFBOYBnwQ2ttL4Ithk4i+DutIzNymvDSpLW/x6j/z/fKmcGfR3tyOufeAYY0\n3WCtXQFMcc7VWmubct7c1pxZvSe6tfYDIA/YEI56xTn3zfC2a4CvAQ1kxma8/8Mnm/HeEFWWRNba\no4F/EmzG6cPLtQR/NI7gW9NKgs0mN0WVM1FCgcy01haRYTmttYcSrOjvAXxEsHlsDpmX87sEH8qN\nwJvA14FCIs5prX2I4FQNxUA1cB3wZ+BPLeWK6v+8lZzXkmGfRy3lbNrII7z9I2Bqs81425wzqwtE\nRESi05W2whIRkTRSgYiISFJUICIikhQViIiIJEUFIiIiSVGBiIhIUlQgIiKSFBWIiIgkRQUi0kHW\n2v9rrf1WJz7+wvBcDSIZJauPhSUSNWvtQOACguMGtfe+fwMWOufmNhs/C7gDKAvPL/EL4L+AL3Y4\nsEgKaQ5EpGPmEJyBsH5/E7bgfoLTyjb3ZYKjIzednOhJ4ARrbaacn0ME0ByISEedAdyTOCI8wunt\nwFcIDp3tCA60dx/BoekXArMJDhL4G2vtMeGZ47DW9ic4h/W0psdzztVba18nODXp7zv35Yi0neZA\nRDrmEILDozf3eYJzQowFPgv8jeDcICUER+f9lnNuJ8FRZr+ScL9zgSXhYbcTLSE4x7pIxlCBiLTA\nWlturf2ctdaFw1dZa69uYdL+wNYWxt/mnKtxzq0F/pfg0N5vOed2AY8Dh4XT3Q/MttbmhcMX8Mn5\nvxNtDZ9LJGOoQERadhDBeTKaTqc8D4i3MF0twXk0mqtOuL6jheECAOfcAmA98LnwVLPTgIdaeLxC\nICPOySLSRAUi0gLn3AvAV/lkbqAIeK6FSd8CxnTw6X5PcKa9LwN/d86tb2Ga8cDiDj6PSEqpQERa\nNxV4Jbw+2Tn3dgvTPEVwxreOeAA4meCsgJ9afGWt7QkcDjzbwecRSSkViEjr/kiwaOkc4KVWpnkA\nOCP8kG/S/DSf+zztp3NuZfj4+QTnz25uJvCCc66qTalF0kSntBXpIGvtT4F1zrlbO+nxXwa+5px7\nrzMeXyRZKhAREUmKFmGJiEhSVCAiIpIUFYiIiCRFBSIiIklRgYiISFJUICIikhQViIiIJEUFIiIi\nSfn/Ac8VVda8wYo4AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109501a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(vts[:2500], nts[:2500])\n",
"plt.xlabel('$v$ (mV)')\n",
"plt.ylabel('$n$')\n",
"plt.xlim(-20, 140)\n",
"plt.ylim(0, 1)\n",
"plt.legend(['simulation'], \n",
" bbox_to_anchor=(1, 1), framealpha=1)\n",
"plt.title('n-v Phase Plane')\n",
"#plt.savefig('6n-v.png', dpi=144)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}