hvc_neurons_sims.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single Neuron Conductance model of HVCX Neurons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from brian2 import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import hvc \n",
    "import simulator\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefs.codegen.target = \"numpy\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`simulate_hvc` takes the type of the HVC neuron along with magnitude of the injected stimulus and other values of model parameters (if any)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### an RA-projecting neuron exhibits lack of excitability in response to depolarizing current pulse (applied current $I_{app}$ = 150 $pA$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    (string):85: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):92: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):102: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):132: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = simulator.simulate_hvc(\"ra\", 150) \n",
    "Time = G.t/ms; Voltage = G.V[0]/ms\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(Time,Voltage)\n",
    "plt.xlim([0, 1000])\n",
    "plt.title(\"$I_{app} = 150pA$\")\n",
    "plt.xlabel('$t\\ (ms)$')\n",
    "plt.ylabel('$V\\ (mV)$')\n",
    "plt.savefig(os.path.join(\"plots/hvc_ra/\", \"fig_sim_1.png\"), dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### an X-projecting neuron exhibits some spike frequency adaptation in response to a depolarizing current pulse (applied current $I_{app}$ = 150 $pA$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    (string):85: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):92: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):102: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):132: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = simulator.simulate_hvc(\"x\", 150) \n",
    "Time = G.t/ms; Voltage = G.V[0]/ms\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(Time,Voltage)\n",
    "plt.xlim([0, 1000])\n",
    "plt.title(\"$I_{app} = 150pA$\")\n",
    "plt.xlabel('$t\\ (ms)$')\n",
    "plt.ylabel('$V\\ (mV)$')\n",
    "plt.savefig(os.path.join(\"plots/hvc_x/\", \"fig_sim_2.png\"), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### blocking $I_{SK}$ in the $HVC_{RA}$ neuron model ($g_{SK}$ = 0) caused a dramatic increase in excitability and increased the resting membrane potential."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    (string):85: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):92: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):102: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):132: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = simulator.simulate_hvc(\"ra\", 150, \"./configs/xhvc1.json\") \n",
    "Time = G.t/ms; Voltage = G.V[0]/ms\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(Time,Voltage)\n",
    "plt.xlim([0, 1000])\n",
    "plt.title(\"$I_{app} = 150pA$\")\n",
    "plt.xlabel('$t\\ (ms)$')\n",
    "plt.ylabel('$V\\ (mV)$')\n",
    "plt.savefig(os.path.join(\"plots/hvc_ra/\", \"ra_150_g_SK_0.png\"), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### blocking $I_{A}$ in the $HVC_{RA}$ neuron model ($g_{A}$ = 0) is shown to eliminate the delay to spiking in *Daou et at.(2013)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    (string):85: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):92: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):102: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):132: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):98: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):98: RuntimeWarning: invalid value encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):99: RuntimeWarning: overflow encountered in cosh\n",
      " [py.warnings]\n",
      "WARNING    (string):100: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):100: RuntimeWarning: overflow encountered in cosh\n",
      " [py.warnings]\n",
      "WARNING    (string):101: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):102: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):103: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):107: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):109: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):109: RuntimeWarning: overflow encountered in cosh\n",
      " [py.warnings]\n",
      "WARNING    (string):110: RuntimeWarning: overflow encountered in cosh\n",
      " [py.warnings]\n",
      "WARNING    (string):112: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):114: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):129: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n",
      "WARNING    (string):130: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = simulator.simulate_hvc(\"ra\", 200, \"./configs/ra_I_A_current.json\", duration = 2) \n",
    "Time = G.t/ms; Voltage = G.V[0]/ms\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(Time,Voltage)\n",
    "plt.xlim([0, 1000])\n",
    "plt.title(\"$I_{app} = 200pA$\")\n",
    "plt.xlabel('$t\\ (ms)$')\n",
    "plt.ylabel('$V\\ (mV)$')\n",
    "plt.savefig(os.path.join(\"plots/hvc_ra/\", \"ra_200_g_A_0.png\"), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### no sag is shown to be present ( in *Daou et al.(2013)* ) in $HVC_{RA}$ neurons in response to hyperpolarizing current pulse.($I_{app}$ = $-150pA$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    (string):85: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):92: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):102: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):132: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: overflow encountered in power\n",
      " [py.warnings]\n",
      "WARNING    (string):91: RuntimeWarning: overflow encountered in power\n",
      " [py.warnings]\n",
      "WARNING    (string):92: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in power\n",
      " [py.warnings]\n",
      "WARNING    (string):101: RuntimeWarning: overflow encountered in power\n",
      " [py.warnings]\n",
      "WARNING    (string):102: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in power\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in multiply\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n",
      "WARNING    (string):107: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):109: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):109: RuntimeWarning: overflow encountered in cosh\n",
      " [py.warnings]\n",
      "WARNING    (string):110: RuntimeWarning: overflow encountered in cosh\n",
      " [py.warnings]\n",
      "WARNING    (string):111: RuntimeWarning: overflow encountered in multiply\n",
      " [py.warnings]\n",
      "WARNING    (string):112: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):114: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):121: RuntimeWarning: invalid value encountered in true_divide\n",
      " [py.warnings]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = simulator.simulate_hvc(\"ra\", -150) \n",
    "Time = G.t/ms; Voltage = G.V[0]/ms\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(Time,Voltage)\n",
    "plt.xlim([0, 1000])\n",
    "plt.title(\"$I_{app} = -150pA$\")\n",
    "plt.xlabel('$t\\ (ms)$')\n",
    "plt.ylabel('$V\\ (mV)$')\n",
    "plt.savefig(os.path.join(\"plots/hvc_ra/\", \"ra_-150.png\"), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a weak sag followed by postinhibitory rebound firing is shown to be generated (in *Daou et al.(2013)*) in response to a hyperpolarizing current pulse in $HVC_{X}$ neurons ($I_{app}$ = $-150pA$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    (string):85: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):92: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):102: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):132: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = simulator.simulate_hvc(\"x\", -150) \n",
    "Time = G.t/ms; Voltage = G.V[0]/ms\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(Time,Voltage)\n",
    "plt.xlim([0, 1000])\n",
    "plt.title(\"$I_{app} = -150pA$\")\n",
    "plt.xlabel('$t\\ (ms)$')\n",
    "plt.ylabel('$V\\ (mV)$')\n",
    "plt.savefig(os.path.join(\"plots/hvc_x/\", \"x_-150.png\"), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### an HVC interneuron is shown (in the paper) to fire tonically at high frequencies in response to a depolarizing current pulse ($I_{app}$ = $150 pA$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    (string):85: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):92: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):102: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):132: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):118: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):118: RuntimeWarning: invalid value encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):119: RuntimeWarning: overflow encountered in cosh\n",
      " [py.warnings]\n",
      "WARNING    (string):120: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):120: RuntimeWarning: overflow encountered in cosh\n",
      " [py.warnings]\n",
      "WARNING    (string):121: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):122: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):123: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):87: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):89: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):89: RuntimeWarning: overflow encountered in cosh\n",
      " [py.warnings]\n",
      "WARNING    (string):90: RuntimeWarning: overflow encountered in cosh\n",
      " [py.warnings]\n",
      "WARNING    (string):92: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):94: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n",
      "WARNING    (string):101: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = simulator.simulate_hvc(\"int\", 150) \n",
    "Time = G.t/ms; Voltage = G.V[0]/ms\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(Time,Voltage)\n",
    "plt.xlim([0, 1000])\n",
    "plt.title(\"$I_{app} = 150pA$\")\n",
    "plt.xlabel('$t\\ (ms)$')\n",
    "plt.ylabel('$V\\ (mV)$')\n",
    "plt.savefig(os.path.join(\"plots/hvc_int/\", \"int_150.png\"), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a prominent sag followed by postinhibitory rebound firing was shown to be generated in $HVC_{INT}$ neuron in response to a hyperpolarizing current pulse ($I_{app}$ = $-150 pA$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    (string):85: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):92: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):102: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):132: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgwAAAFRCAYAAAAYfvW7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAABE0klEQVR4nO3dd3hcZ53//fd31CXL6rZlWcWOneI4sRNMCCG0JBuSUAIsy7KUzbOwm4u2lGcbZffHw49ed1kIJUsoARZCCUlIAqSZhABJcIqduCXu3ZZcZclqM/fzxzlndDSapjKakfR5XZcva845c849x2Pd3/O9mznnEBEREUknku8CiIiISOFTwCAiIiIZKWAQERGRjBQwiIiISEYKGERERCQjBQwiIiKSkQIGERERyUgBg4iIiGSkgEFkmjKzOjNzZtae77KMl5m918zWmlm/mX0vYd/vzKzPzE75f7Yk7K83s1+aWY+Z7TKzN4/j+sVmtsnMnpvgRxGZ8YrzXQARGbdVwDHn3K58F2QC9gOfBF4BVCTZ/17n3LdTvPcGYACYj3cv7jKzdc65DWO4/juBeUCtmVU553rG8F6RWUUZBpHpaxXwVJ7LMCHOuVudc7cBR8byPjOrAv4S+A/n3Cnn3MPAHcDbQse8xcz+ZGa3mNkBM9tjZleH9tcAHwPeC0SBFRP/RCIzlwIGkenrAnIQMJjZnWZ2PMWfOyf7ehl8xsy6zOwPZvay0PYzgSHn3LOhbeuAc0Ovz8MLqm4BWoCvAN8M7f8PYLtz7sfAs8D5k156kRlETRIi09cq4AuTfVLn3Ksm+5zj9G/ARrxmhzcBvzKzVc65bcAc4GTC8SeA6tDr84D/dM7dCmBmNwNfMLNyYCHwj3hNIQAbUMAgkpYyDCLTkJmVAedQQE0SfidFl+LPw2M9n3PuUedct3Ou3zn3feAPwDX+7lPA3IS3zAW6Q6/PA34eej0POOWc6wM+D9znnPudv28DsHKsZRSZTZRhEJmeVuC1u28CMLNPAC8CGoB3O+f+YGbrgZ1AK/Bj59zn/WOTbg+Y2a+BF6e47u+dc1cn2+Gce9nEPlJGDjD/52eBYjNb5pwLRjisxKv4MbNavM/XGXr/G4Bfm9mleP0fTprZQX9fBRDLbfFFpjcFDCLT0wXAM865If/1Z51zPWb2fOBNZrYBqAcuw0vd/xn4vF+RjtoePnGqgCAXzKwY7/dQEVDkNxcM4TU5vAB40H/918BLgPf7Zewxs1uB/2tmf4/XPHMtcIl/6vPwAqo3m9mX8Joe3g28HPguXl+Gj4eK0g48YmZtzrndOfvAItOYAgaR6WkVfnOEmc0DvmRmi4A64Ga8CvMnzrkuMzPgtP++VNvz5d/xRioE3opXkd+AN9zybLyKfzPw2oROju8GvgMcxhtl8a7QkMrzgB8BLwSOAVuA1+IFWvOBf3HOnQpOZGaH8JozzgcUMIgkYc65fJdBRCbAzL4K3OWc+42ZfRf4AV5F2+Gc+1d/QqMlzrlPmtm7k23PY/Fzwsy+ATzrnPvPfJdFZKZQhkFk+vsD8EUz+2u8YYXrgL8CBs3sfuAg8Hb/2PNSbJ9pzgNuz3chRGYSZRhEZiAzuxd4hXMuls32mcbMjgOrnHM781wUkRlDGQaRmak0RVCQavuM4pyrzXcZRGYaZRhEREQkI03cJCIiIhnNmiaJxsZG19HRke9iiIiITJnHH3+8yznXNBnnmjUBQ0dHB2vXrs13MURERKaMme2arHOpSUJEREQyKviAwcxazWyNmW00sw1m9n5/e72Z3Wtmz/l/1+W7rCIiIjNVwQcMePPI/5NzbjlwMfAeM1sOfAi43zm3DLjffy0iIiI5UPABg3PugHPuCf/nbrzV+VrwFpr5vn/Y9/HmiRcREZEcKPiAIczMOvAWj3kUmO+cO+DvOoi3oIyIiIjkwLQJGMxsDvAL4APOuZPhfc6bfWrUDFRmdr2ZrTWztZ2dnVNUUhERkZlnWgQMZlaCFyz8yDl3q7/5kJk1+/ub8Za4HcE5d6NzbrVzbnVT06QMQxUREZmVCj5gMDMDbgI2Oee+HNp1B3Cd//N1aGU6ERGRnJkOEze9CHgb8LSZPeVv+wjwWeCnZvYOYBfwxvwUT0REZOYr+IDBOfcwYCl2Xz6VZRERyaUndx/jjHlzmFteku+iiIxS8E0SIiKzwcBQjNd9/Y+843t/zndRRJJSwCAiUgBizhvotX7viTyXRCQ5BQwiIgVk1PhwkQKhgEFEREQyUsAgIlJIlGKQAqWAQUSkAFiqsWAiBUIBg4iIiGSkgEFEpIA4tUlIgVLAICJSACzl/HQihUEBg4hIAXFKMEiBUsAgIlIA1BQhhU4Bg4iIiGSkgEFEREQyUsAgIlJA1DAhhUoBg4iIiGSkgEFEpABodIQUOgUMIiIikpECBhGRAuKUapACpYBBRKSAKFyQQqWAQURERDJSwCAiUkDUIiGFSgGDiEgBMa1BJQVKAYOIiIhkpIBBRKSAqElCCpUCBhEREclIAYOIiIhkpIBBRKQAqClCCp0CBhEREclIAYOIiIhkpIBBREREMlLAICIiIhkpYBARKQBOy05JgVPAICIiIhkpYBAREZGMFDCIiIhIRtM6YDCzq8xsi5ltNbMP5bs8IiIiM9W0DRjMrAi4AbgaWA78jZktz2+pRETGRzM9SqGbtgEDcBGw1Tm33Tk3APwEuDbPZRIREZmRpnPA0ALsCb3e628TERGRSTadA4aMzOx6M1trZms7OzvzXRwREZFpazoHDPuA1tDrRf62OOfcjc651c651U1NTVNaOBERkZlkOgcMfwaWmdliMysF3gTckecyiYiMi/o8SqErzncBxss5N2Rm7wV+CxQB33HObchzsURERGakaRswADjn7gbuznc5REREZrrp3CQhIiIiU0QBg4iIiGSkgEFEpAA4TfUoBU4Bg4iIiGSkgEFEREQyUsAgIiIiGSlgEBERkYwUMIiIFAB1eZRCp4BBREREMlLAICIiIhkpYBAREZGMFDCIiIhIRgoYREREJCMFDCIiBUAzQ0uhU8AgIiIiGSlgEBERkYwUMIiIiEhGChhEREQkIwUMIiKFQJ0epcApYBAREZGMFDCIiIhIRgoYREREJCMFDCIiIpKRAgYRkQLg1OtRCpwCBhEREclIAYOIiIhkpIBBREREMlLAICIiIhkpYBARKQBa3loKnQIGERERyUgBg4iIiGSkgEFEREQyUsAgIiIiGSlgEBEpAOrzKIWuoAMGM/uCmW02s/Vm9kszqw3t+7CZbTWzLWb2ijwWU0REZMYr6IABuBdY4Zw7H3gW+DCAmS0H3gScC1wFfN3MivJWShERkRmuoAMG59w9zrkh/+UjwCL/52uBnzjn+p1zO4CtwEX5KKOIiMhsUNABQ4K3A7/2f24B9oT27fW3iYiISA4U57sAZnYfsCDJro865273j/koMAT8aIznvh64HqCtrW2CJRURyR2nqR6lwOU9YHDOXZFuv5n9P8CrgMvd8P+ofUBr6LBF/rbEc98I3AiwevVq/W8UEREZp4JukjCzq4B/BV7jnOsN7boDeJOZlZnZYmAZ8Fg+yigiIjIb5D3DkMHXgDLgXjMDeMQ5907n3AYz+ymwEa+p4j3OuWgeyykiIjKjFXTA4Jxbmmbfp4BPTWFxREREZq2CbpIQEZkt1MlKCp0CBhEREclIAYOIiIhkpIBBREREMlLAICIiIhkpYBAREZGMFDCIiBQAzQwthU4Bg4iIiGSkgEFEREQyUsAgIiIiGSlgEBERkYwUMIiIFACnyaGlwClgEBERkYwUMIiIiEhGChhEREQkIwUMIiIikpECBhGRQqA+j1LgFDCIiIhIRgoYREREJCMFDCIiIpKRAgYRERHJaMwBg5lVmVlRLgojIjJbqc+jFLqMAYOZRczszWZ2l5kdBjYDB8xso5l9wcyW5r6YIiIikk/ZZBjWAGcAHwYWOOdanXPzgEuBR4DPmdlbc1hGERERybPiLI55r3Pu6cSNzrmjwC+AX5hZyaSXTERERApGNhmG283su2bWmuoA59zgJJZJRERECkw2AcPZwBPAQ2b2FTNrynGZRERmHadej1LgMgYMzrkB59xXgXOAPcBjZvYJM5ub89KJiIhIQch6WKVzrs8590VgBXAaeNzM/jlnJRMREZGCkXXAYGYdZnYV8PdAG9ANfDpXBRMREZHCkXGUhJmtB1qA3XhzMGwC7ge+Bjyb09KJiIhIQchmWOVrgR3OqUuOiEiuOM31KAUum06P24NgQfMtiIjkhh7JpNBlk2EAwMy+DbzSzIaA/cB6YL0/gkJERCYgpohBCtxYFp96MbDIOdcKvB74JVCVk1IlMLN/MjNnZo3+azOz/zazrWa23swunIpyiIjkiuIFKXRZZxiAR4EG4LBzbh+wD7g7J6UK8WeYvBKv02XgamCZ/+cFwDf8v0VEpiUFDFLoxpJh+BbwoJn9s5m92MxqclWoBP8J/CsjV3+9FrjZeR4Bas2seYrKIyIy6dQkIYVuLAHDD4Gb8bIS7wb+aGbbclIqn5ldC+xzzq1L2NWCN+tkYK+/TURkWlLAIIVuLE0Se51znwlvMLOyiRbAzO4DFiTZ9VHgI3jNEeM99/XA9QBtbW3jPY2ISM4NxRQwSGEbS8DwlJm93zn3lWCDc65/ogVwzl2RbLuZnQcsBtaZGcAi4Akzuwiv/0R49cxF/rbEc98I3AiwevVq/W8UkYI1GI3luwgiaY2lSWI+8E4z229md5rZp8zsr3JVMOfc0865ec65DudcB16zw4XOuYPAHcDf+qMlLgZOOOcO5KosIiK5Nhj1nmmKI5bnkogkl83U0OZ3Lnyj/7oMOBc4D7gI+FlwTG6LOsLdwDXAVqAX+LspvLaIyKQLMgwlRWN5jhOZOtk0Sawxs18AtzvndvvNEE+Y2TPAi83s+8Aa4Hs5LCd+liH42QHvyeX1RESm0nDAoAyDFKZsAoargLcDPzazxcBxoBwoAu4B/ss592TOSigiMgsETRLKMEihyhgwOOf6gK8DX/fXkmgETjvnjue4bCIis0ZP/xAAFaVFeS6JSHJjGSWBc24QUOdCEZFJ1t03CEB1udb4k8Kk3JeISAE4edrLMFSXj+k5TmTKZAwYzOycqSiIiMhsFmQYqtQkIQUqmwzDXWb2XTPTVIkiIjlytHcAAH+iOpGCk03AcDbwBN7CU18xs6Ycl0lEZNbZf7wv30UQSStjwOCcG3DOfRU4B2/Bp8fM7BNmNjfnpRMRmSX2HTsNwNTOgSeSvaw7PTrn+pxzXwRWAKeBx83sn3NWMhGRWSIac+w+2pvvYoiklXXAYGYdZnYV8PdAG9ANfDpXBRMRmS12dPVwejCa72KIpJXNWhLrgRZgN7AZ2ATcD3wNeDanpRMRmQU2HjgJeNNCq0FCClU2A35fC+yY4sWlRERmjbU7j1JRUkRHY1W+iyKSUjadHrcrWBARyZ3fP9fFC89ooLTI0G9bKVSa6VFEJI/2HO1lR1cPly5tBM3BIAVMAYOISB7d9bS3PM8V58zPc0lE0lPAICKSR7c9uY8L2mppa6gEUKdHKVgKGERE8mTTgZNsPtjNa1e1AKAGCSlkChhERPLk5j/toqw4wqtXLsx3UUQyUsAgIpIHx3sH+OWTe3ntqhbqq0rj2zUoTQqVAgYRkTz48WN76BuM8XeXdsS3aZCEFDIFDCIiU6x/KMr3/7iTFy5p4OwFWsdPpgcFDCIiU+ynf97DwZN9vPeypSO2K8EghUwBg4jIFOofinLDmm08v6OOS85oyHdxRLKmgEFEZAoF2YUPXHEmlqTTgvo8SqFSwCAiMkV6B4b42pqtrG5Pnl1IFkCIFAoFDCIiU+R/HtrBoZP9/NvVZ6cMDpzmepQCpYBBRGQKHDrZxzcf3MY15y3g+R31SY9RfkEKmQIGEZEp8MXfbiEac/zbVWfnuygi46KAQUQkx57ac5yfP7GX6y5pp72hKu2x6vQohUoBg4hIDg1GY3z41qeZX13O+y5flvZY9XmUQlac7wKIiMxkNz28g00HTvLNtz6P6vKSjMcrwyCFShkGEZEc2XWkh/+671muXD6fq1YsyHi8qdujFDAFDCIiOTAUjfGBW56ipCjCx689N9/FEZkwNUmIiOTAVx/YypO7j/PVv7mA5pqKrN+neRikUCnDICIyydbuPMpXH3iO11/QwqtXLsz+jWqRkAJW8AGDmf2jmW02sw1m9vnQ9g+b2VYz22Jmr8hnGUVEAodO9vGuHz1Ba33luJoi1OlRClVBN0mY2cuBa4GVzrl+M5vnb18OvAk4F1gI3GdmZzrnovkrrYjMdv1DUd75w8fp6R/ih+94QVajIsKUYJBCVugZhncBn3XO9QM45w77268FfuKc63fO7QC2AhflqYwiIsRijg/f+jRP7j7Ol9+4krMWVOe7SCKTqtADhjOBF5vZo2b2oJk939/eAuwJHbfX3yYiMuWcc3zm15u49Yl9/L9/cSZXrWge/7kmsVwikynvTRJmdh+QbIDyR/HKVw9cDDwf+KmZLRnDua8Hrgdoa2ubeGFlVtvR1cPmAye5+rzxVwYyM33jwW38z+93cN0L2/nHy5aO+zxm6sMghSvvAYNz7opU+8zsXcCtzjkHPGZmMaAR2Ae0hg5d5G9LPPeNwI0Aq1ev1n9DmZDLv/Q7Yg52fvaV+S6KFAjnHF97YCtfuvdZXrNyIR979bkpl63O/qSTUzaRyZb3gCGD24CXA2vM7EygFOgC7gD+18y+jNfpcRnwWL4KKVPvrvUH2H20l3e97Iwpu2ZMv8glxDnH536zhW8+uI3XX9DC599wPpHIxIIFb6ZHfdHG68TpQdbvPc7Ww6fY0dXDjq4eDp/s51jvAMdPDzIUjQEQMaO6vJj6qlIa55TR0VDF0nlzWDpvDitba6mvKs3zJylMhR4wfAf4jpk9AwwA1/nZhg1m9lNgIzAEvEcjJGaX9/zvEwBTGjCIBPqHonz41qe59Yl9vOUFbXzi2hUTDhZk7A6f7OPBZzt5dMdRntx9jG2dPfF91WXFLG6qor2hklWttdRWllBaHME5iDnHidODHO0ZoLO7n/s2HeKWtcPd4pY0VfH89npefGYjL17WRE3F2Ea7zFQFHTA45waAt6bY9yngU1NbIhGZ7bpO9fPOHzzO2l3H+MAVy3j/5csm3gwRopkeU4vFHGt3HWPNlsP8bksnmw6cBKChqpQL2mp5/YWLWNVay1kLqmmoKh3Tv8uxngG2HOrmid3HeHznMX6z4SC3rN1DccRY3VHHFefM55XnN49p1s6ZpqADBhGZuL3Hetl9pJdLljbmuyjT3tqdR3nfj5/kSM8AX3vzBbzq/DHM4pgFLW89mnOO9XtP8Kt1+7lz/QEOnuyLV+L/dtXZvPTMJs5prp5w0FZXVcrFSxq4eEkDANGY46k9x7h/02Ee2HyYT961iU/dvYmLOuq5dlUL15y3gNrK2dV0oYBBZIa77EsPMjAUG3Nnzdue3MdANMYbV7dmPniGi8YcN6zZyn/d9yyL6ir5+Tsv4bxFNfku1oy25WA3v1q3n1+t38+uI72UFkV46VlNfGTlObz8rKYxT4o1VkUR43nt9TyvvZ5/vepstnee4lfrDnD7un185JdP87E7nuGlZzbxugsWcfk58ygvKcppeQqBAgaRGW5gKDau933glqcAZn3AsPXwKT5863r+vPMY165ayCdfuyKnldVsHla560iPFySsO8CWQ91EDF60tJH3vHwprzh3QV77EixpmsP7r1jG+y5fyob9J7n9qX3csW4/9206THVZMVeft4DXXtDCxYsbZmx/FgUMIjJmQ9EYP3xkF2+5uJ2SokKf/218BoZifPPBbXztga1UlBbx5Teu5HUXtExqf4VEs7FJ4uCJPu5cv59frdvPur0nAHh+Rx3/99pzuea8ZhrnlOW5hCOZGStaaljRUsOHrj6HR7Yf4ZdP7uOu9Qf46dq9NNeU85pVC3ndBS2cvWBuvos7qRQwiMiY/fix3fx/v9pI72CUd79s/BMVFaoHn+3kE3duZOvhU7zq/GY+9upzaaqemoprNiQYjpzq59fPHOSOdfv5886jOAcrWubykWvO5pXnL6Sldnp0LCyKGC9a2siLljbyiWtXcN+mQ9z25D5u+v0OvvXgds5pnsvrLljIa1a2sKCmPN/FnTAFDCJT7J9+uo7bntrHtk9fk++ijFt3/xAAJ08P5bkkk2vLwW4+dfcmHnq2k7b6Sm66bjWXnzN/yq5vM3j5qROnB/nthoPcuf4Af9jaRTTmWDpvDh+84kxedX4zS5rm5LuIE1JRWsSrVy7k1SsXcuRUP3euP8Avn9zHp+/ezGd+vZlLzmjgtatauGrFgpz3v8gVBQwiU+wXT+zNdxEmLKjY3AxpcN9ztJcb1mzlp2v3MKesmH9/5Tm87YXtlBXP/I5sudTTP8R9mw7xq3UHeOjZTgaiMVrrK7j+JUt49fkLJ2V0QyFqmFPGdZd0cN0lHezo6uG2J/dx21P7+Jefr+ffb3uGS85o4LKz5/Hys+exqK4y38XNmgIGERmz4Hf8dA8Xdh/xAoVfPLGXiBnXXdLB+y5bRl0eZ/qb7kFY32CU3205zK/WHeD+zYfoG4yxYG45f/vCdl61ciErF9XMyCAhlcWNVXzwL87kA1cs48k9x/nVuv08sPkwa27fALdv4Mz5c3j5WfO4+IwGVrfXFXT2QQGDyBg552bVL7xkgk8/XSu3bZ2n+ObvtnHrk/soihhvvbidd770jLy3M0/Xr9Wp/iEeeraT3244yH0bD9EzEKVxTilvXN3Kq85fyOr2uhk7ciBbZsaFbXVc2FbH/3nVcrZ39bBmszfHw00P7+BbD20nYrCipYaLOupZ3VHHipYaWmorCub3jQIGkTFybvr+Yp8sEQuaJPJckDFwzvHw1i6+8/AO1mzppKw4wtsubuddLzuD+XMLp0PadLmlnd393L/pEPdsPMTDW7sYGIpRV1nCq8732vEvXlJP8QwdQTNRZsYZTXM4o2kOf//iJfQODPHk7uM8uv0Ij+w4ys2P7OLbD+8AoLayhBULvVEZZy2Yw5LGOSxpqspLJkIBg8gYxZwjMoM6pz224ygrWuZSWZr9r4Pp1CTRNxjll0/u4zsP7+C5w6donFPGB65Yxlte0D5lIx9mih1dPdy78SD3bDjE47uP4Ry01lfwtovbuXL5fJ7XXqcgYRwqS4vjoy3A+85uPtjN0/tOsGHfCZ7ed4KbHt7OYHT4f1xTdRlLGqtoq6+kuaac5toKFtSU01xTzoK55dRUlEx6ZkIBg8gYTYdKMplkTSmHT/bxxm/9iatXLOAbb33eOM6Zfv+mAyf52pqtfOWvV015RXLwRB8/eGQn//vobo71DrK8eS5f+quVvGplszozZimYlvkeP0h47vApAM5dOJcPXH4mV547n7MXzMyOi/lUXlLEqtZaVrXWxrf1D0XZfaSXbZ09bO86xY7OHrZ39fDQc50c7u4f9X+xpMgmfdVNBQwiYzSd0vBhyZpSege8RV43+ov4jPmcGcKn9/34SZ47fIr3XbaMsxZUj+saY/XUnuN89w87uGv9AaLOceXy+bz9RYu5aHH9tKjY8v39GhiK8eiOI9yz4RD3bjzEwZN9FEWMizrqefML2viL5fOnVc/+maKsuIhl86tZNn/0/6PBaIzD3f0cPHGaAyf6OHiijyM9Axw9NcBjk1gGBQwiYzRdVxOMJmlKCfoixMZYS2XbhyF+XI7v2VA0xm82HOQ7D+/gid3HmVNW7A1re2EHbQ3Tp3LLV0Bzqn+IB7d0cs/Ggzyw+TDdfUOUl0R46ZlN/Mvys7js7Hl5HTki6ZUURWiprUg64dXnJ/E6ChhExijfT4DjNTAUGzWNc7wvwhg/0/D70r8xOC42vuUsMjrRO8hP/ryb7/9xJ/tP9NHeUMnHXr2cNzxvUUEPT0tnqr5end393LfpEPdsOMgfth5hIOp1Wrzq3AVcee4CLl3aSEWpmm5k2KwJGA6d7OPL92zBzIiYETGIRAwzhl+bYeZNSTO839tXZEYkYhSZURQJ/+y9L9gWMfOPJcmxFj92xPvi24bLlup9EUv9FOKcwznvF45zzv/be7oLfq+HXzu8J7PBqGMoFmMo6hiMxhiK+X/72wejztvnHzMUjTEQjdE/GKNvKEr/YIz+oShDMccbV7eycAqndY3F3JQP15qsgGGqym7mlbmnf4iqsuJR+2AcAYP/d6a3WY4yDNs7T/G9P+7k54/vpXcgysVL6vn4tSu47Ox5FE3j4Xu5LvmOrh7u2XCQezYe4olwp8UXqtOiZDZrAobD3f18dc3Waft0GBYEN4mBQSGIxhz/dOVZU3a9vqHomHr3T4aBodikPHl19w9Nyep7c8qK6e4b4lT/EPMS9sUr9DRfoGSdJbNNnUfGGZCkKsefth3hpod38MCWw5REIrxm1UL+7kUdnLtQS00nE4s5nt43utPiipa5fPAKr9PiWfPVaVGyM2sChvNaalj7mVfGn8JjzhHz/x5+7W1zCX/HnCMa8/4EP3t/M2pbsu1R54jFEt4X2hb/ObhewrWGjx1+X8w5zLwpes3POhiM3Ba89n8ZJNtXHIlQUmQUF0UojhglRRGKi2zE9pKIv7/IKIl4f5cVRygrKaKsOEK5//eLP7eGAyf6pvTftad/6gKGipIiTg9G6RkYoqZy/BV9xCDm4OTpwSkJGOaWl9DdN8TJvtHrPmSTKegdiI47MzHeDEZYNOb4zTMHuWHNVjYeOElDVSn/eNky3npxG/OqC2f+hEkzwegqVafFFyyu5y0vaOMKdVqUcZo1AUPAzG+GmEHj6AvF/JpyDp2c2oDhWO/AlI2lryz1AoZgZMF41VSUcKx3kGO9A7TW5/4X97y5Zew7fpqDJ/qgdeS+bCr0Qyf7Ri0MFPzvydRZMr7mxDiaJAajMW5/aj9f/91Wtnf2sKSxis/95Xlcu6qF8pKZ2bY+3gf9ZJ0WK0qKeOmZTVx57nwuO3setZXqtCgTM+sCBsmdBXPL2N7ZMyXXaqouo7O7nwMn+jgzyTCjXKgsK+JID/QOTGyFxvlzyznWO8jBE32cv2iSCpdGc005TwIHT5wetS/dKImg78OBE6MDBuJ9E9KLd3ocQ7wwFI3xs8f3csOarew9dpqzF1TztTdfwNUrmqd1/4RsZXur9h0/zQObDnHfpsP8aZvXabG+qpSrVyzgyuULuHRZ44wNrCQ/FDDIpFkwt5w/bjsyJddqrimn0x93PFXqq8rYc/Q0Xaf6J3Se5ppyNh/snrLmmzr/yTLZ9dLN2Ng8t5z9J/qSv8//O3OTRParWjrnNT184bdb2N7Vw8rWWj7+mnO57Ox5s6aNPd2njMUc6/Ye54HNh7lv02E2+XNndDRU8rYXtvOKcxfwvPa6WRFUSX4oYJBJM7+mnO6+oaS98SfbPL8ZYir7TDTPLWfdJFyzvsor+/5JDnZiMTeiz0ogeJm84k89n8L8Gj9gOJ46M5HpeTiSZYbhsR1H+dTdm1i35zhL583hW297Hlcunz9rAoVUuk7188dtR/j9s52s2dJJ16l+IgarO+r5yDVnc9nZ8zmjqWrW3yeZGgoYZNI0+yv9HTjRx9J5c5IeE405egaGmDvBMfLBfAIHpzBgmDfXD1KOT+yaQSU60fMkWvKRu/mL5fP5n79dnXT/gSQBynA9M7pGL/Xv8YEk/VKC90UzRAKZVrU83N3HZ+7ezC+f3EdzTTmff8P5/OWFi2btU3JxUYT1e0/wyTs38odtR+JZhLnlxbzkzCauOGc+LzurSf0RJC8UMMikaa7x5l84cOJ0yoDh47/awM1/2sXmT1yVsn21fyjKwFAsq4l39k9hwBA8VU80MxBUnckq8Im6d+OhlPvSNS2kq/eTZRgq/WGlmTqAWoq+DtGY4wd/2smX7nmW/qEY7335Ut7z8qWzfqKgdr8T7M1/2sXqjjr+5RVncenSRla01MzaIEoKhwIGmTQLg4AhzZPzbU/uA7zV2FIFDK+74Y9sPHCSnZ99ZcZrTmUfhuFrTk6Qsn+SMwyZHDrZl3KyqHR9DJIFGsFw0BOnB9NeM1lfh51dPfzzz9axdtcxXryskY+/5tzRnSpnqfddsYyrz1vA8uaaWR88SeFRwCCTZn5N5rb54CkpXSo7m4WQggporP0Jjpzqp2HOxIZhTla/iUMn+4jGXM6fHIN7NRh1dPX0J527IG2GIcnnDVZ7PNozkPbakVCnR+ccP3x0N5++axPFRcaX37iS113Qovb3kLnlJTyvvT7fxRBJSnOAyqQpKy6icU5Z2gxDPGCYpKkpgxkMs7HpwEme98n7uOXPuyd0zQMnTo96Iv/jti46PnQXe4/1Zn2eoZjjyARHXIxV4r9N8CnSDRU9cXow5f5M2ZaSYu/f+0jPAO/64RP8x23PsLqjjns++BJef+EiBQsi04gCBplUC2vL02YY4uP+s1iMKJbl4P1smwh2dnlzRDyw+XBWx6fSNxjjeO/IVPzP1u4FvN7+YzGVfTAgdXZkMDr6Xoe3pHrfkQwZhkY/m/PuHz3BvZsO8dFrzuHmt18U7+8iItOHAgaZVM015WlT9kGGIZvllLOd7yDbgKHcbxM+PTjxpRMTg6IgEBoaywxFJO9QmEtj7fNhExzRsWCu1/xRWhThx/9wMf/wkiXKKohMUwoYZFI111Rw4PjolH0gqFjT9WGo9udwyPT0XV7iD/vLshKs9TvqHcvwVJyNxCAlWOAv26xIMMogFxmG/qHRIxcaqkopLYqMuf9F89xgqOzIexye6jnZ9QIL/KG2d73vUi5arLZ5kelMAYNMqoW15fQMRJMudAQQCSrWNBmG5lq/ksrw9B08vWabYQg66k3GcMbEin4sfTOc82ZfLC+J5CTDcOjE6MyMmVd5pwsYkgU780Nza4zleommar0PEckdBQwyqcJzMSRTWeJlD3r60z2VeufI9PRdVlxEQ1XpmJ/Su06NL8PgnKO6vJjiiI2q6If7ZmTfJNFcU5GTmSoTm0uCEnkBQ8K+UHG7ekZX/GXFERqqStOWc98UN6uISH4oYJBJtTCeHUhewQQp6oMnU1cy9f7S0ena24OUeLJKMJeKIsb8ueVJmiQyN7Ukaq5J30F0vJLfD2NhhgzDvmPJy5LpHu9XwCAyKyhgkEk1nB1IXokE00dnM2lRpsyBGSysrZjyCitZRV/st7WMpdOj199j8jIMFf5EWKkr/or45E3JpPo3aa6pSNvso4BBZHYo6IDBzFaZ2SNm9pSZrTWzi/ztZmb/bWZbzWy9mV2Y77KKZ351GRFL3a9gfopOdMlk077fUlvBvmOpO1mGhTvqdfeln6Ewneba0RVoY7U3t39nd/bzKiysLedwdx9D0YmP2oDhTqCpmghaassZjDo6U4w+2Xc8YQ4JN1zOdM0O6QK7SZpuQ0QKQEEHDMDngY8751YB/8d/DXA1sMz/cz3wjbyUTkYpLoowr7o85dNqSZGXus/myTqbLMSiugp6BqIZpyhONJG+A83+Ko7hICXInIylPb+5poKYg0NjCDLSCUqzN0WGoaWuIu3+ZPfbMFpqK+juG+JkOMhy4fdl/syWduFmEZkOCj1gcMBc/+caYL//87XAzc7zCFBrZs35KKCM1lybuV9BNm33h7v7GBhK//TdUpu+EkxlIh31WmorGBiKjeg8WVrkD5PM4rxBpiPb0SBjleqzLarzFjZKNRtlqnsYvC9ZU0ddZYmaJERmiUIPGD4AfMHM9gBfBD7sb28B9oSO2+tvkwKwME3v/+ChPF2FHTy8xlzmIZPBU/NYA4CJ9B1YFH9SH13xZruglFlosa5JHimxP2EejODHILgK36twM02qij9dZiLoQ5JNk5CITG95DxjM7D4zeybJn2uBdwEfdM61Ah8Ebhrjua/3+z6s7ezszEXxJYnmmvKMlcj+46k734WlehpOrATHnmHIfs2H+DX9v4ef1Edf8+DJ7Cv/BfEOoKEK3Dke2HxoTKMtEvUNxkZN2WwGVWXF1FWWJC13xNJlJvxAI8m/xcJar0no5Ons1vMQkekr7wGDc+4K59yKJH9uB64DbvUP/Rlwkf/zPqA1dJpF/rbEc9/onFvtnFvd1NSUy48hIc21FfQPxdKuZBiNubRTPwcLOO7NkDmoryqloqQo5ciAMDei3X18T/VG8qxG+Ek926ftmooSqsuLR5znd1s6efv31nLDmq3jKl88i5CmH0OyfQtrKzhxejDpQl4NVd4kU0kzDBn6bijvIDJz5D1gyGA/8FL/58uA5/yf7wD+1h8tcTFwwjl3IB8FlNGSpb6T2ZOmkm+uqcAsc+bAzLxKcIwZgz1Hx55hCMyJP6knP0fiwlTpLKqrHPEZj/V6Qdb2zlNjLpdzmTs2LqqtTPrvEvyb7U8SBJl5HR+TvW9hkvclpT6PItNeoQcM/wB8yczWAZ/GGxEBcDewHdgK/A/w7vwUT5JprfcqkT1HR1ci4SfOdM0NJUXGgrnlWS0XnaoyS6WmInlafixa6ipSnmMs526tqxgRvATTVw+Mc6hlvPkgRQDllbt3VBYkVZAXrBPVkhDYBO8OAoapnDxLRPKjoAMG59zDzrnnOedWOude4Jx73N/unHPvcc6d4Zw7zzm3Nt9llWGt9el74wfSVaxmxqIU6fNEqdLsqSyqq+BQd1/aRZMynqO2MmX592QKckJ1dZBhCCrwYFGq7hRrcWQyt9xv5hhRtuELttRW0DcYai4K+oLUpW/KWFSXPChrqi6jpMjYN4kTUIlIYSrogEGmp7nlJdRUlKStOGsrSzJmBVpqUz/FJx53rHeQ3oHsKtnWukqcG38/BvAq0PCTeviBPZvmjuDJfVFdBacHo/EKvL7KmwBqLAFQomQZl6BFYFGKJot51WUURyz1SInaCo72DIy6xxEzmmsqssoEicj0poBBcmJRmpR9Nvu9Yyo5eDLzTIiLMjwdJwqaTCZSyS2qS3hSD8mYYRhRlkr/PV7Zg0BiLOcIBMFLunubahhqJGI0p5nRMd09bq2vSNsfRURmBgUMkhOtdZVpn7S9lH76SnFRXQXRmEs6VDHcAh8fWpllP4Z4JZ2kj0U6LqEpAUY/qZeXRMZ03sQ5HYJrDEbHP74gXZ+OdJMwLaxJvS5HqswEQFt9JXtT/FtrfgaRmUMBg+REa33FiLb5QPAy6J+QrEIJtmTq8W/+43im9vdE86rLKSmycWUYEq+ZWLbWusoxZQeCijhpB9FxVrYtdUmmcvbVVJRQXVac9LOn68gZD5CSBBSL6io50jNAT5IhmQHTKAmRaU8Bg+TEorpK+odiKRc6aq1Pv99IP0FSWBAAZOoTEdS/RRFjYe3E0ugtiZkBf3tbvdeJMZtJqQCqy0uoTTFEc6zrY8TLVps6iwB+J1H/XoVL2eo3AQWdQcPxStOcMkqLIknL2RZvVlE/BpGZTAGD5ES6oZWQPsUdWOivtZApE1AUCTrejT7X536zmbd++9ER24zhTouJdnb18Nlfb85Y4QcdO0dlGOorGUgTCMHoyYxaQ0MWw/vG2mQSSOxvkJioSNbHwTDaG7zOoOFAI8gMRCLmrVqZ5JxBE8/uIwoYRGYyBQySE60ZFjrK1NwA3pwE8+eWZdXUkCoA+MbvtvHw1q6k5UtWIf/Lz9fxzQe3sfHASQD+7efrueXPu1NeMzGrMdzEkL7yDK/euKiuIunT+Vif2Ienrq4Y9f5wk0CqJcGDTMGuFGVP1mRhFs4wqOOjyEymgEFyIlVAEMwemG7lxHBFlu3QyvaGyrRPuImV46K6CrpO9dM3OHIuhpqKEmC4wr9l7R7+7RdPJz1nsiCldRzp+VT9OcYzG6WZNzSzqrSIXSnux6K6Srr7h0Y1ecQr/hTXTTX3RF1lCXPKiic0e6aIFD4FDJITlaXFNM4pTVmJVJUWUV9VmjoYiM9TkF0nwrb6Ko70DCRdCwGg69TAiPUeUk0u1VZfBaR+yg5LnHTJ25a+KSaZeH+O7v4R59o9zgrYzGhrqEr5/vYGP5OQEFA0VZdRXhJJGXi1NVTSdap/1FwMwSRbwb/1XesP8MFbnhp5zHg+iIgUFAUMkjOJ6ySM3j9cydy38RA3/2nnqGPaGyrZf/w0A0Mj52JIbJcfrgR7Rmyv8mdO3H10eLtZOG0/snzN/mJKyZ7OXULvg9a6CnoHohzpGYhX9GXFRcyrLhvT03aqskwkxd9eXznqXsT3NQwHReH7aGa01VemDJZSBRrgZSeCAOU9//sEv3xy34RW3BSRwqOAQXKmtT59dqA99BT89zev5f/cviHpMTGXfCGr8FNrW4qOd21+5biza+T2VCMwIv4ymYkVfjB5VPia7Y1+xXtkZDCS6XMnStbfo6KkKOXcBimF6uf2hkr2pBitEe+r0DWy3MG+4LMnvrM9yL4kCRiCz+yco9i/hxkXpBKRaUUBg+TMojpvIqBUT5odDV4GYjA0k+PpgeiIiqrDf6rdeaSHvsEof0zSgRFCT78JlWwwqVPi9qY5ZZQWR1JmAnYdHfl0nmwa6Y4UwYi3oJRXWf766QM8sPnQiP2j+1MMBzvJhmf+/rlObn9q1OrtSQWdKdsavNEaB0/2jcrGVJR6nUmTZRLa6r0gLihjuHNmWyiLk5htaauvpG/QGx2SLhMhItOXAgbJmbb6SgajbsRKhm7EU3AV0ZgbMQoiqKiDaipeSXX18OV7n+XN336Up/YcH3Wt6vIS6qtK2XWklxO9g7zrh49z5FQ/JUXemXYd6Rlx7UjEaK+vZGdXD919g3R86C5uf2pfvKLcc3Rk34TEAAK8YKQoYqNS/631lRw44QVC7/rRE7z9e6PXRguPWggq8J2hCra1vpKBaIzD3f287abHeP9Pnhp1jnQSswGW0Iugvb4qaV+FtnqvmaXr1Ogpr2sqSqirLBkRaMT/nUKzZwZNHjtTNImIyPSkgEFyJnjSTHwCB6+9PJw9CDIBicc2zSmjsrSIXUd7OeJXYpv8IY+JvHb0Hn72+B5+/cxBblizLb4v/LQbVNbtDVXsOtLLsR5vtMCn79404nzhdSJ2JqlcS4sjLKwtH7Wvta6SmBudko/FXMrZG72yDFewwTwW4Y6Lg9EYe4720p1kBsfR50vepyPQ1lCZtEIPArRUHSbbEsqZWN49R3uZP7csfm3NDC0ycyhgkJxZ3Dj8pHmsZ4DDCWtCxDvfHelNWcGZWbxiz5Tqbm+oZNeRXhbUDE/4FFRYySrAxY1epVnkZyEOnRw52dKuo73UVXrDLMPt/WEdCRWoAR3+594Res/JvkGWfORuPvvrzUnPs7ihip2hCra9fvT92HO0lxd/fg3X/Pfv+cXje+n40F0pl+hurvFmv0zVgbGjoZLD3aNHPASjRFI11XT49zhR0Kzivc+7nztHBGkaJyEy3SlgkJyZX11OeUmEnV09vPCz93PRp+8fsb9xjjdfwM4jPVSXFwPJ09gd/tNw45zhJ9dk2uu9ERXV5SWjznW0Z4DuvpGVY0djFf1DMQ6k6Jy3+0hvvKJLHFEQv2ZD5agMQ0djkFkZvv62w6cA+NZD25Neq6Oxiq5Tw8NCW+srKYoYO4/0UFs58vPsOXqaG363FfCCks/cvYnnDnWP6FVQXBRhUV3quSmCzqBBIJW4/HXKoMy/x4MJK4iWl4zuF5Hq30lEpicFDJIzkYjR4T859w16FUy4UgtnD4LKeGdX76ju+W0NXs/9qH+QV0En6f3vj6gIRhskNm8kBiNBp8XtoYo9HBSEK81UlV9HQxUnTg9yvHe4maBpTpkfCPXGRwzsSJGhGD7PyIxCcVGE1roKdnb1xrMNO0KfJyj7+r0n+NZD23n9N/4IjOwb4Q2RTB1cJX5G8Cr+BXPLR3R8HPG++D0eHWR1NFT5QZKLnzumNgmRGUMBg+RUe0PliMqyK2GNhY7GkW3pwc/hFHZHQxWDURfvE7Arfszoa3n7vUpwIBob0Zs//j7/eTpoOghnAo72ev0WqsuL/Tb4cOWX7JqjO/iZGR2NVezo6olPEBW+B8mq0OGy9I7Ytr2rh5rKUv8cp+L75s8tH1H2xOxJcD92HekdNaIBhgOOpHMqNAwPrUx1j5P1S1nS5H3mIEbo90dpiMjMoIBBcqqjsWrErIc7Okc+8bbVV3nZA3/o5YETfaOmaw4qqeC9vQNRBqNJnn7rRzcF9A/F4h3yEivH5rnllBZH2B4qU3CNjoaqeHo9YvgzMY6u/BKDlCDQWdzoZVbKir3/YuEsRk//0KiZD9tDHUADixurRgQt4Ur6pD+tc7rMRVt9Jd19QxzrHRxV8ddUeotn7U6SgWivr2RHpkmfkgRtixu92TbDy2praKXIzKGAQXJqcUMVA9EYpX7FmVjBdTR4Qy/3nxiujBM7KHYkeYpPNjFSU3UZFSVFI96/+0gvVaXFNM4pG1V5BUMrw2UKfh5+Og9fP/XSzonNHYsbvUBowG/rDwdKySr5ytJib2hl1/Cw0sWNVfQORDnkP6WH3xcEIOFtien/oHJP1Y8hWf8LgMVNVXR29yedZrtxTml81EqieBNP6LPuzNAUIyLThwIGyakg1R5M7ZyYoo6n9LtGN0sEFviZgGQVe5jXJ2JkE0dQsXY0JG/Pb2+oGvE0Hfzc0VDlrZvQH41/hmT9GMpLimiuKR8VjHT4bf1Baj9T2YOyJGYYYLgC3h+az2Jb56lR5+odiI6ciTJJ1iLxeskyBUsa53jvSzEcti3FtNNLmoZHhzRVl1FaFBluYkpaAhGZThQwSE4FlV5YuHIKRhScHozGe+gPRt2ICiYS8Sqp/tB6Eqn60i1urIp3sAxra6gc3j4ijV45Yp2K4Oegsh2IxlhYW05xxJI2gwTHJs5mGQQZwXtOh5pZUi2xsNgPMuLn8IOpIX9j+DMH5ewfGv1ZA231lZilPqbdn1grUVDxD0STvy/oU5Kotb4y3nxTZEZrfUXKzyoi048CBsmpeX4zQSrzq8vj7fzBTILJBKMIMgkquzAzY0mSwAWGK/ZEZ8ybE/+5OBKJd15MXrbhcwSxSDhQCppjMgmXxQwW1lZQWuS9tyzLc4SVlxTFg7BkkgVz4AVAkTQpgVT3rKy4KD4fg9nI+yIi058CBsmpoJkgbNQUzaH9qSqjcOWWLngI0umjtjcl3x6u1IJ5HsB72h+Zpk9d+SUrc53fqTDTe0eWZeTnKgrdm1SVeyap7geMDIrCwhV/0nMmCcoC4XvRroBBZEZRwCA5l6myCyq1dE+lZ4Qq/KDyTzZ5YLgyC65rCe8PC1dw4fcmPp0H+5Kl2JOdOxhaCcMdIzNJFngE22oqvLUyxipd5Z5uX7p/s5Gfd+Q/wpLQPU93fhGZfhQwSM6lyhoEzpjnt9VHXbzCOZ0wtDKcIQiO6U/SVyHZcacHo6OyHIFmfz6D8PHgBSPhp/PgvOH1JQJLUzypL/avWVZSFG9qWehPW30sNNFTYGTTxvDwTMAfrZE525AYRAXlTjYfwtzy5M0/3vvSBQyZA42uUwMjzqGZoUWmPwUMknPZZhj2HO2Np8kTZxIckTloGjmtcVhNRUm8aSE+GdKRHspD/SgSO1QGlVliJRl+na5ZoTVFP4HhESLReHo+KPuJ06MDhorS0X09gnt38ERf/Hzh+xlkQRb4gU9iZ8QzgoAjRefD4P2Jq1kGn/dQkkCjtjJ1piMo20A0xtIUWR0RmZ4UMEjOpXoCDwQVc89ANGXTQUMoHR8EGKl6/wfnC/4OKsu5/noVo473K7mW2pFZiKAsB0/0pewDAd40zoER/R789+w+ejp+jUzBU2VC0BBkHXYf7Y2XpynU1yLYH2RpEoOodOVOtz/YnmlK60Thz9dUXZbmSBGZbhQwSM6dkabjHYystFI1HYSnis4UgAQp88QOf8F1Eh+2g+3RhMfw8LwCjXPG3n8gKMf2zlPxDEO6ESPee4IyemUJAoHw+cLzMQT3K8iqJFbwwVLTqQSBzLHekU0tQcWfaijpglBTTtjC2uFsi1aoFJlZFDBIztWEhkomqzCD0QTAiKaDVKpTZAoCQaWbmOIP+gB0do9czyKoNHeHJiMqLYrEU+rbu05lrPxa/IoyPKdDOBMSVOzJFm0KC4KhYN2McDYh+FzhiZjiQZBfrxcljIfMVO7hoGb0ZFnpBMFUeBroZNcXkZlDAYNMmbnlxdx03WreceniUfvmlBXH29PbGyqTVlh/c1Eb4FVKS5qqOHN+8kzDytZazLyhjbWVJfEVI19/4SIAzl5QPeL4K8+dD8CZ86v5q+d5x0QiFk+pX7WiGYC/WD6fqiT9DADe/fIzgJHBTxCwXHJGA6s76ryflzayqrU23vkx0WsvaAGGmxqCCr+1voI2P+i4ZkUzLzurCYCXLGsE4KVnNvG5vzyPm65bPeqcL1hcT3VZ8iDrsnPmU1NRwqtXLhyxPRIxKkuLuOSMhqTv+8AVZ3rlSjL8ctm8OZQUeeX+xLXnUlNREp9PQkSmL0u2hO1MtHr1ard27dp8F2PW6h0YwrCkHfsA+oeiRMwoKYowFI1hZqOeVqMxx6n+IWoqSojFHGapn6CPnOqnYU4ZfYNRnBuuvPuHopQVjy7D8d4BaitLicUcA9FYPNNxsm+QipIiSooi8UWgUl1zMBqjJKFi7OkforQ4QklRhJ7+oRF9FFKdp7tvkOrQCIbTA1EiEW9+hN6BIcqLi4g6x8BQjKqyYrr7BplTVpzyfDF/LGhkjE//0ZgjkuYex2Iu6TkHozGcy37CKhHJHTN73Dk3+kliHNLndkUmSWVp+q9auBIvTvE0WhSx+BN8psqvwU/lJzZxJAsWYLjnfyRilEeGjwkPPcyU3k8MFgCqQk/2VSme8hNVJwx3DAdZwX2MYPHrJR6faKyBQiBT80Kq8ya7DyIy/el/toiIiGSU94DBzP7KzDaYWczMVifs+7CZbTWzLWb2itD2q/xtW83sQ1NfahERkdkl7wED8AzweuCh8EYzWw68CTgXuAr4upkVmVkRcANwNbAc+Bv/WBEREcmRvPdhcM5tgqTtw9cCP3HO9QM7zGwrcJG/b6tzbrv/vp/4x26cmhKLiIjMPoWQYUilBdgTer3X35Zqu4iIiOTIlGQYzOw+YEGSXR91zt2ew+teD1wP0NbWlqvLiIiIzHhTEjA4564Yx9v2Aa2h14v8baTZnnjdG4EbwZuHYRxlEBEREQq7SeIO4E1mVmZmi4FlwGPAn4FlZrbYzErxOkbekcdyioiIzHh57/RoZq8Dvgo0AXeZ2VPOuVc45zaY2U/xOjMOAe9xzkX997wX+C1QBHzHObchT8UXERGZFTQ1tIiIyAw1mVNDz5qAwcy6gS35Lscs0Ah05bsQM5zuce7pHk8N3efcO8s5V535sMzy3iQxhbZMVpQlqZnZWt3n3NI9zj3d46mh+5x7ZjZpqfVC7vQoIiIiBUIBg4iIiGQ0mwKGG/NdgFlC9zn3dI9zT/d4aug+596k3eNZ0+lRRERExm82ZRhERERknGZFwGBmV5nZFjPbamYfynd5piszazWzNWa20cw2mNn7/e31ZnavmT3n/13nbzcz+2//vq83swvz+wmmD38p9yfN7E7/9WIze9S/l7f4s5ziz4R6i7/9UTPryGvBpxEzqzWzn5vZZjPbZGYv1Hd5cpnZB/3fFc+Y2Y/NrFzf5Ykxs++Y2WEzeya0bczfWzO7zj/+OTO7Lptrz/iAwcyKgBuAq4HlwN+Y2fL8lmraGgL+yTm3HLgYeI9/Lz8E3O+cWwbc778G754v8/9cD3xj6os8bb0f2BR6/TngP51zS4FjwDv87e8Ajvnb/9M/TrLzFeA3zrmzgZV491vf5UliZi3A+4DVzrkVeDPzvgl9lyfqe8BVCdvG9L01s3rgY8ALgIuAjwVBRjozPmDAuxlbnXPbnXMDwE+Aa/NcpmnJOXfAOfeE/3M33i/YFrz7+X3/sO8Dr/V/vha42XkeAWrNrHlqSz39mNki4JXAt/3XBlwG/Nw/JPEeB/f+58Dl/vGShpnVAC8BbgJwzg04546j7/JkKwYqzKwYqAQOoO/yhDjnHgKOJmwe6/f2FcC9zrmjzrljwL2MDkJGmQ0BQwuwJ/R6r79NJsBPF14APArMd84d8HcdBOb7P+vej89/Af8KxPzXDcBx59yQ/zp8H+P32N9/wj9e0lsMdALf9Zt+vm1mVei7PGmcc/uALwK78QKFE8Dj6LucC2P93o7r+zwbAgaZZGY2B/gF8AHn3MnwPucNu9HQm3Eys1cBh51zj+e7LDNcMXAh8A3n3AVAD8NpXEDf5YnyU9zX4gVnC4EqsniKlYnJ5fd2NgQM+4DW0OtF/jYZBzMrwQsWfuScu9XffChIz/p/H/a3696P3YuA15jZTrzms8vw2tpr/bQujLyP8Xvs768BjkxlgaepvcBe59yj/uuf4wUQ+i5PniuAHc65TufcIHAr3vdb3+XJN9bv7bi+z7MhYPgzsMzvmVuK1+nmjjyXaVry2xNvAjY5574c2nUHEPSyvQ64PbT9b/2euhcDJ0JpM0nCOfdh59wi51wH3nf1AefcW4A1wBv8wxLvcXDv3+Afr6fiDJxzB4E9ZnaWv+lyYCP6Lk+m3cDFZlbp/+4I7rG+y5NvrN/b3wJXmlmdnwm60t+WnnNuxv8BrgGeBbYBH813eabrH+BSvFTXeuAp/881eO2M9wPPAfcB9f7xhjdCZRvwNF5v6bx/junyB3gZcKf/8xLgMWAr8DOgzN9e7r/e6u9fku9yT5c/wCpgrf99vg2o03d50u/xx4HNwDPAD4AyfZcnfE9/jNcnZBAvU/aO8Xxvgbf793or8HfZXFszPYqIiEhGs6FJQkRERCZIAYOIiIhkpIBBREREMlLAICIiIhkpYBAREZGMFDCIiIhIRgoYREREJCMFDCKSNTNbZGZ/nWJfhZk96C8pP5FrlJrZQ6Hpg0WkAChgEJGxuBxvzYVk3g7c6pyLTuQCzluG/n4gaWAiIvmhgEFEsmJmlwJfBt5gZk+Z2ZKEQ97C8Bz2mNnPzOxrZvawme0ys0vN7Adm9qyZ3eQfc52ZPW5m683s4dC5bvPPJyIFQlNDi0jWzOw3wD87555J2F4K7HbOLQht2wzc6Jz7spl9BG9RnJcBnXhz4J8F/AlY5ZwbMLNa59xx/71FwEHnXNMUfCwRyYIyDCIyFmfhLSaUqBE4Hrwws3KgFvgvf5MDbnLOHXDODQFRYAioAL5kZquDYAHAb9YYMLPqyf8IIjIeChhEJCtm1oi3PO5Qkt2n8VYbDJwLPOGci/mvVwKP+udZBOx3zvUAK4A/ADea2bsTzlkG9E3iRxCRCVDAICLZ6gD2J9vhnDsGFPmZBYDzgHWhQ87HW0YavOBhvZktc871OOd+AtxJKOAwswagyzk3OLkfQUTGSwGDiGRrM9BoZs+Y2SVJ9t8DXOr/fB7wFMSbJyr8oAKGg4ePmtkWM3sCWAx8PXSulwN3Tf5HEJHxUqdHEZkUZnYh8EHn3Nsm4Vy3Ah9yzj078ZKJyGRQhkFEJoVz7glgzWRM3ATcpmBBpLAowyAiIiIZKcMgIiIiGSlgEBERkYwUMIiIiEhGChhEREQkIwUMIiIikpECBhEREclIAYOIiIhk9P8DwGWXPhhnj0AAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = simulator.simulate_hvc(\"int\", -150) \n",
    "Time = G.t/ms; Voltage = G.V[0]/ms\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(Time,Voltage)\n",
    "plt.xlim([0, 1000])\n",
    "plt.title(\"$I_{app} = -150pA$\")\n",
    "plt.xlabel('$t\\ (ms)$')\n",
    "plt.ylabel('$V\\ (mV)$')\n",
    "plt.savefig(os.path.join(\"plots/hvc_int/\", \"int_-150.png\"), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### an HVC interneuron is shown ( *in Daou et at.(2013)* )to fire tonically at high frequencies in response to a depolarizing current pulse ($I_{app}$ = $75 pA$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    (string):85: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):92: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):102: RuntimeWarning: divide by zero encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):132: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n",
      "WARNING    (string):115: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):116: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):85: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):86: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):96: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):105: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):106: RuntimeWarning: overflow encountered in square\n",
      " [py.warnings]\n",
      "WARNING    (string):118: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):118: RuntimeWarning: invalid value encountered in true_divide\n",
      " [py.warnings]\n",
      "WARNING    (string):119: RuntimeWarning: overflow encountered in cosh\n",
      " [py.warnings]\n",
      "WARNING    (string):120: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):120: RuntimeWarning: overflow encountered in cosh\n",
      " [py.warnings]\n",
      "WARNING    (string):121: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):122: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):123: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):87: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):89: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):89: RuntimeWarning: overflow encountered in cosh\n",
      " [py.warnings]\n",
      "WARNING    (string):90: RuntimeWarning: overflow encountered in cosh\n",
      " [py.warnings]\n",
      "WARNING    (string):92: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):94: RuntimeWarning: overflow encountered in exp\n",
      " [py.warnings]\n",
      "WARNING    (string):95: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n",
      "WARNING    (string):101: RuntimeWarning: invalid value encountered in add\n",
      " [py.warnings]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = simulator.simulate_hvc(\"int\", 75) \n",
    "Time = G.t/ms; Voltage = G.V[0]/ms\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot(Time,Voltage)\n",
    "plt.xlim([0, 1000])\n",
    "plt.title(\"$I_{app} = 75pA$\")\n",
    "plt.xlabel('$t\\ (ms)$')\n",
    "plt.ylabel('$V\\ (mV)$')\n",
    "plt.savefig(os.path.join(\"plots/hvc_int/\", \"int_75.png\"), dpi=300)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.2"
  },
  "vscode": {
   "interpreter": {
    "hash": "a9cff5a362bc38ef45d817ae74b1af54d6a076e3d773891282bce078b815ba34"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}