gap-junction-plasticity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from fns import *\n",
    "from fns.functionsTF import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 800 200\n",
      "1000 800 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 120000/120000 [06:55<00:00, 289.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "415.54\n",
      "\n",
      "1000 800 200\n",
      "1000 800 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 120000/120000 [06:57<00:00, 287.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "418.37\n",
      "\n",
      "1000 800 200\n",
      "1000 800 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 120000/120000 [06:56<00:00, 288.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "416.65\n",
      "\n"
     ]
    }
   ],
   "source": [
    "params = []\n",
    "res = []\n",
    "config = load_config()\n",
    "glist = [1,5,7]\n",
    "    \n",
    "for g in glist:\n",
    "    T = 120000\n",
    "\n",
    "    gpu = TfConnEvolveNet(config=config, T=T)\n",
    "\n",
    "    # number of excitatory neurons\n",
    "    gpu.NE1=800\n",
    "    # number of inhibitory neurons\n",
    "    gpu.NI1=200\n",
    "\n",
    "    # mean input drive\n",
    "    gpu.nu = 120\n",
    "    gpu.device = '/gpu:0'\n",
    "\n",
    "    # mean initial gap junction coupling\n",
    "    gpu.g1 = g\n",
    "\n",
    "    # do not save the spikes\n",
    "    gpu.spikeMonitor = False\n",
    "    # do not save the individual voltages, currents, etc.\n",
    "    gpu.monitor_single = False\n",
    "\n",
    "    # iteration \n",
    "    gpu.stabTime = 1\n",
    "\n",
    "    # rule: g0 = 0 for no bound rule, g0 = 10 for softbound rule\n",
    "    gpu.g0 = 10\n",
    "\n",
    "    gpu.runTFSimul()\n",
    "    res.append(gpu)\n",
    "    del gpu  \n",
    "    gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Evolution of the mean gap junction coupling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f1da46647f0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NRcRLoUYImZ2qQi3XkIN+z3XIspzQzudaERoYUKgqQoiWqCrUVgrL4An5EIgmHu20lK6C\nv79foaoIIVqiqlBjGAaC0YpAJDHU9AX5iHi8ClVFCNESVYUaABh1BkyEE8fVeJsNkbExhSoihGiJ\n6kKtyJKP04NnE9qMJSXwu/ohRSIKVUUI0QrVhdrawjXwhhKPdOrMJvA2G6I+urMUIWRmqgs1M2+E\nP5x8WpQuJ4euq0YImZXqQs3Cm3HFdy2pXZ+fB9+5CwpURAjREtWF2vriu3Bu+GJSu3XDegSvJYcd\nIYRMpbpQKzDlISxFIMlSQjtvsyHipWkdhJCZqS7UWJaFntPDF0o8KGAoLIDv7DmFqiKEaIXqQg0A\nllsX4/xI4i5ozprVCI+5FaqIEKIVqgy1xdZF8IUSr3ars1gg+v1J54USQshUqgw1qz4HgxMjCW2s\nXg/WoIf/0mWFqiKEaIEqQ225sBSfDidP3xA234OJS5cyXxAhRDNUGWrLrIsRjISS2g1FRQgNDilQ\nESFEK1QZalZDDgYmhpPaLSuW0+3yCCEzUmWoFZjzIENOugmLyW6nuWqEkBmpMtQ4lsOa/BUYDSRO\n4TAUFMDv6ocYSt41JYQQQKWhBgB5RgEXRhOPdOrz82AsLoK/z6VQVYQQtVNtqK0rWo1+b/K5noZF\nixBx0yRcQsj0VBtqRZYCDN00Vw0ATEsWw/vJpwpURAjRAtWG2irBjgsjlxC86X4F1rvXIXj1qkJV\nEULULu1Q83q92L9//0LUkiDHYEFJTlHSwQLjkiUY702+NBEhhABzCLWGhga4MzSmVWDOw/mRSwlt\nlhXLERoehiyKGamBEKItaYWay+XKWKABkwcLro0PJrQxHAedxYwwHSwghEwjrVDr7OzEQw89tFC1\nJMkzCkkntgOAadkyeE+fyVgdhBDtSDnUOjs7sXPnzoWsJcnawlJ8cO100lVwc9asRthN9wElhCRL\nOdS8Xi+sVutC1pJkubAUFt6Eq96BhHZDYSHGz9NNWAghyVIKtba2NlRVVS10LdNaLixLmoQrbL4H\nnlOnIUvSLX6LEHKnmjXUlNhCm6okpwjnbrq0t3nFcnAmE8Yv9CpUFSFErWYNtePHj6OioiITtUxr\nc8l6XLgp1BiGgXX93fCc+lihqgghajVjqDkcDpSXl2eqlmnZbUtwxXs9qT13XRkmLvcpUBEhRM10\nMz14+vRp9PX14dixY/G2M2fOwOVy4dChQ9i4ceOCj7UJRiskWYIn6IXNeGM3WNh8Dy6+8ipkSQLD\nqvZsL0JIhs0YatXV1UltTU1N+Pjjj/H8888vWFFTMQyDtYWrcaLvJL5S9ki83bhoEfSFhfB8fBrC\nPZsyUgshRP3S3sTJ5BkFMV9e8zA6+7qT2oXNm+A7dz7j9RBC1CvlUHO5XNi/fz+OHDmCN998E/v3\n74fD4VjI2uI2LVoHl+cqPMHES3nbyssxdjI57Aghdy5G1sjdgf+64yX8/vqd2LJkY7wt6veju+a/\nYdNLB2FaukTB6gghaqGZEfby4rX4eOBsQpvObEbRlx7G8IlOhaoihKiNZkJtU8ndeK//A9y8YWnb\nWA73R6cUqooQojaaCbW7i9aAYzmcHU48iyCndBX8ly5DCocVqowQoiaaCTWWYbF1yUa86+pJaDcU\nFYG3WTFx8ZIyhRFCVEUzoQYAv7f2Ufz60m/hDY0ntOeuLcMoHQUlhEBjoVZozsd9yzaj7fwvE9qL\nH/kS3D0fKFMUIURVNBVqAPDVddvR3vtOwoUjc9etReDqNUS8PgUrI4SogeZCbZl1MQwcD+fojZPZ\nWZ6HbdNGDP3yVwpWRghRA82FGgDcu7gc71/5MKFt8c4duPazY5AiEYWqIoSogSZD7XfXPoqO3t8g\nHL0xjcN2zyYYiosx0PGWgpURQpSmyVArySnC0txFOD14Lt7GMAxKqnZg8BdvK1gZIURpmgw1APhS\naQV+crY9oS3/c/chODCAwNWrClVFCFGaZkPt4ZUPYHBiBJ8O3birFKvTofiR38GV//yJcoURQhSl\n2VDjWA7f2PR1/GPXPyMqifH2kqodGOnsRHBgcIbfJoRkK82GGgA8aN8Km9GKE5e74m2mxSUo2fFl\nuN5oVrAyQohSNB1qDMNgd/nvocXx04TJuEsf+xpGT/Zg4tJlBasjhChB06EGAOuL7kKBOQ9vOW9c\nU02Xk4PFO3fgytH/VLAyQogSNB9qDMPgyY1fxX+cOY6oGI23L/n6V+H99FOMvt81w28TQrKN5kMN\nANYVrsES6yIcn3Kiu85sxpo/fQa9ja9ADAaVK44QklFZEWoMw+Abmx7DT892ICzeOE1K2HwPcteW\noe/f6KABIXeKrAg1AFiZZ8fq/BU4di7xNKnS//rHGGjvQOBa8l3eCSHZJ2tCDQD+YNPX8LNzbyWc\nE6rPz8Oyxx/Dhe//IOn+BoSQ7JNVoWa3LcGG4jL8sCdxd3PpY1+DGAhgpPNdhSojhGRKVoUaANRt\n+wZOXj2Ffu+1eBvDcVi55yk4m36IiMejYHWEkIWWdaFm4o34vbWV+NdTP0poFzbfg6KHv4ALP/g/\ntBtKSBbLulADgK+UPYILIxdx8spHCe0r/ugbCA0P49pPf6ZQZYSQhZaVoabneHzrgafxf3uaMeIf\ni7ezOh3K/vIv4GpuQdTvV7BCQshCycpQA4CNi9Zh511fwoFf/h384UC83WxfhoIHH8C5hr+FLIoz\nPAMhRIuyNtSAyTtPlRevxT90vZ4wjrb6mVpI4TD6//0/FKyOELIQsjrUAGDPvU9g1O9Gq+PGOBrD\ncVj9Z8/getubuP7z9hl+mxCiNVkfajzHY+/nn0GH8zf49aX34u2mxYtR/t3/gcuv/wt8Z8/N8AyE\nEC3J+lADAMFkw19/8Vt44+Mf4y3niXi7aekSlNb+Cc79r5cR8dGNkAnJBndEqAHAcmEp/uqLf45/\nOfWfcI7euHhk0Re/AGHLZpz/25fpnqGEZIE7JtQAYJltMf5ky5N46Tf/CJfnxh2nVv3xHoBhceHv\n/5GOiBKicXdUqAFAxfKt+Mamx3Dg7b/D+ZGLAACW57F277cRGhzE+e//AGIopHCVhJC5uuNCDQC+\nsPJ+PHP/H6H+nX9An/sKAIAzGLD+hb+CFIniw+f+EuNOp8JVEkLmgpHv4BMhf33pPfzzR0fxrQee\nxsZF6+LtQ79+B86mH2LlN/8QiyofVbBCQki67uhQA4BT1z/B37////Dgsi34w82/Dx3LAQD8/Vfg\neOFF2P+gGiVfrlS4SkJIqu74UAOA8fAE/vdvX0UwGsa3PrcHhZZ8AMDE5T58+j8PIXdtGUpr/hg6\ni0XhSgkhs6FQ+4woifjxp+346blf4PfX78SONQ+DYzmIgQAuvvo6xk6eRGldLfLv3waGYZQulxBy\nCxRqN+n3XkPTyX8Fx3D49kO1sOjNAAD3hx/BefgVmFeswJo/ewa6nByFKyWETIdCbRqiJOLVD47A\nMXgO366oxTLbYgCAFA7j4g9fw+j7XVj+B9UofuRLYDhO4WoJIVNRqN2CLMt4y3kC//TRUVTd9TAe\nv3sn9Do9AMB39hwuvfY6ouPjsD9ZjYIHPweGvSNnxxCiOhRqsxieGMU/fXQUl939+NP7n0JZYSmA\nydAb6+6B69+awfA8Vj9TC8vKFQpXSwihUEtRZ183Xv3gCDYWr8V/uefrKDRPHiGVJQnXjrWhv/Xf\nYVm1Cst2PQbr+vV0MIEQhVCopSEQCeInZ9tx/Nzb+J1VFfjauu0QTDYAk+Ntg7/8Na78+3+At1mx\n9LGvTx4ppTE3QjKKQm0ORvxj+Mmn7fjV5ffw8MoH8LV1X0beZ+EmiyJG3nsfV47+CBGvByU7voyS\nnTugM5sVrpqQOwOF2m0YDbjx409+jl9dfg8V9q2ouut3YLctiT/uO3sOV396DGPdPSj6wkNYtOPL\nyCldpWDFhGQ/CrV5MBbwoL33HXT0voM8ow0Vy7fh8yvuQ4E5DwAQGhnB4C/exrVjbTCWLELxlx5G\n4UMPQZdDZygQMt8o1OaRKIk4O9yLX176LU5eOYXV+Svw0PJt2LZkE3IMFkjRKNw9H2Dw7V/B/dFH\nyLv3XhQ89CDy7t0MzmRSunxCskJKodbW1gaXyxX/qa6uRlVVVSbq06xwNIzf9n+A9698iNMDZ1FW\nsAr3Li7HliXlWJRThIjPh+HfdGL0t+/Bd/YcrOvXIW/rFuTffz8MRYVKl0+IZs0aam1tbbDb7diw\nYQMAwOv14vHHH0dNTQ2qq6szUqTW+cMBnBr4BD3XTuODaw6YdUasK1qD1fnLsb6oDIvYXHg++ghj\n3T0Y7eqGcVEx8j93PwoeuB8mu52mhxCShllDrampCTU1NQltzc3N2L9/P86ePbugxWUjSZbQ576K\ncyO9uDB6GY7BcwiLEZQXl+GekvXYWFgG7uJVjL73Pkbfex8Mz6Pgc/ej4MEHkFN2FwUcIbOYMdS8\nXi/27NmD1157DVarNd7ucrlQWVmJjo4O2O32jBSazQbHh3Fq4BOcGvgUpwfOotCch3VFa7ChqAyr\n/QZMnPwIw785ASkUQsEDD6D40S/BsmolnZpFyDR0Mz1otVrj42ix3U8y/4pzClGZ8wVUrv4ComIU\nzrE+nBk6j5/3/grnRy7hrqWrsPUvHsN6qQDoOo2zDX8LKRSEcO+9sK5fB+v6u2EsKaGtOEIwx6Of\nzc3NaGhoQFdX10LURKYIRoI4NfApuq9+jFPXP4EMGXcXrUG5VIiSq35wl67C98mnkEUJwj2bkFN2\nF3JWl8JSugqcwaB0+YRk3JxC7fHHH8fOnTuTxtrIwpJlGdfGB/Hp0AU4Bs/hwsgljAY9KDbno4wp\nwOohGQXDITD9Awj0uWBcXIKcNWuQs2Y18rZtgbG4WOm3QMiCSzvUmpub0dbWhldffXWhaiJp8IcD\nGJgYxsWxPpwbuYhPhs5jaGIUS00FuCtihd0NCNfHgdMXoDMaYVm1CsaSRTAUFoK3WcELAszLlkJf\nUEBjdCQrpBVqLpcLzz33HI4ePbqQNZHbFBYjuOq9jj7PVfR5rsLluYp+9xVIw26UBUwoiRqRH+KQ\nGwL04yFIAyOQwmFYVq2EsaQE5mVLYSgqhL6gALzVCj4vDzozTQ4m2pBWqD377LP43ve+l3AklGhH\nMBrCVe919Huvo997Df3e67jmHcCgfwQrWAFrQhYUTjAQPBEYfCGw3glg3I+o2wMwDPR5AvT5+TAu\nWgR9fh70BQUwFBXCUFQIXhDA22x0sIIoLuVQ279/P2pqamgKRxYKR8PxoBucGMF13yCG/CPwBsfh\nDfngD/thY0woEvUoDPEoDukghFiY/VFwnglw7gnANw45HIHOkgOd2QTOaATDcTAuWTIZhgUFMBYX\ngbOYwRlN0FnMYDgd+DwBrG7Gg/CEpCWlUGtubkZFRUVCoHV2dsJut1PI3QEkSYIvPA5vaByeoA/X\nx4cw7B+FO+BBIBqCJ+iFLzyB4LgPkfFxGKOAlTEghzOiaBzIi+hg8Udh8IXABMNgQhGwgTAYSQL8\nQeiKCsCZzdBbcsCbzOBMJuhyLMhZsxqmpUuhy7FAZ7GAM5spAMmsUjpNyuPxoLy8PN7m8XjQ1taG\nAwcOLHiBRFtkWUYwGoIvPIHx0DjcQR+G/aPwhnzwBH1gwCAqRRGRogiJYfjGPdANuRENBhGeGAcX\nESHACEHUoWgoBLM3BD4sgg1GJgOR14HPzwdvNoMzGsEaDOBzc6HLzYEuN3cyEC0W6PPzAIaBzmKB\ncXEJdDk5tGt8h5j1jIL77rtv2sfsdjs6OjoWrDByZwpGghgLejEWcGM04IE76MFowIMR/xgGx4cQ\nnBgH6xlHNBCAPgpYwGMRLMiHAaYwYIjK4IMidONBsGDA+IPAiBsIhidfQMeBMRnBREUAAMOwAMuA\nYdjJ0GMZAAwYlgEYFmAml2E+a2cNesiSBFkUwRmM4POEyYMp1lzwNht4wQZ9/uSl3hmWhc5qhRQK\ngdHpIOs4MBwHvckE1micDGWeV2ZFZzG69BDRJFmWEREjmIgEcNU3gKGJEUyE/fBHApiIBBCIBBER\nIwiLEYTFMMaD4wAATpShj8gYi04gKkURioQgSSJEWYQkimAZFjqwiG3TiVERPMtBz/LQszqYZA4c\npwPH8zBLmDHiAAAPKElEQVSJHCxBCTp/GFwgBH0gCv1EGIaJMCR5Mvj4YBRhDmBEEZwIcJIMnQjw\nURl8RILEMogadJB0LESOgcwArDj5JykyMiQAMgPILAMwTNK/UaMODBu7ZLwMmWEQNuoQNvFgGEDH\n8WAZBhAlyJIEiCJkUQIryWB5HizLgTMYIPEcgjwgMQA4DmAm/5vjdNCxOjAWE3idHgzPg5EASZQQ\nEqNgwyKiHAuOM4DX8wiHQmAlCXoZ4FmAFSXIERGsKIERJYiQwUAGI8pgIiLEgB/gdah85tswGufn\n6tA0QEE0iWEY6HV66HX6+KXUU+UPRjDkDiAYikKWgYlgBP5gFJIkgecZMCwAyAiFJQSCIvyhEMZD\nIeTmcDCbWUTFCALhEAKREHyhEPwhEeP+KILhKEwGHcxGHWwmC6xGMxjOADECsDIPWWYRlmVEEUQo\nGoGRZyBFQggH3EAkDCYiIRyRILMsbBY9DCwPnuGg51gwkBEKRxAJRxCORMBCgixFEfH4AFaCnucg\nyoAYjoIPBmAKBSGKMqLBCKKSBFanA8txiMgMRJaFyMiQIxFIkgj4QmCjIfChKGRJBiOJgAQwsgwJ\nEkKSBH00jLAsQyeJ8UDlAUgcC6Mkg5UlADKMDIsoyyDEMBhnGEgMA5FjIbIMRIYBKwMyGIgcEGVZ\nRFgeMqfH58MijMZ5+mzQlhpZSLGPlz8YhXciDO9ECN6JMIIhERPBCHz+MCYCk6ESC5dwRIQsAzzP\nwsBzYBggHJGQa+YRiogIRyRIkoxQRJz8CYuQJBkcx0CWZYiijEAoikAoinBUAssw0HEMGAaIRGWw\nLINF+SYY9DpwDAOzUQezcXLLJhSZfC4A0PMcckw8DHoOJoMOI54gvBOTu7FGPQeDnoOe5yDkGJBv\nNcJs1GEiEIHXH4Z3IoxxfwSSJMNi4sGxzOSuLBhIsgyOZSbfJwAGmKxRx8Js0CEqyRjzBiFKN96L\nDBlGvQ4GnoOeZyF+VmOOiYcoyfBOhMHrWJiNPGRZhiTJ0PMcjAYOBl6HSFREVJRhMerA61joeQ4c\ny4DnOeh1LPQ6DjodCz0/+RjPTe5681zsOTgw8fWo7rFJCjUyLVme/EMZcgcw4g5g2B2Y/EMNRjDu\njyAYjsIzHkYoIoIBEIlKCIVFBMNRhD4LJVGSEY6IYFkGBp6D1aKP/xj1OlhMPHLNPCwmHmYjD4tR\nB7OJh0E3ufsTe05JlsHrWIz7wzDwOuh59rM/QO6zP/LJP1BRkjE5LHYjqHQcA1kGoqIEAOA4FjzH\ngmXV/YdJ5o5C7Q4ViYoIRSS4fUEMuwO4MjSBgVE/hsb8uDo8gWvDE2BZBkWCCYWCCUWCCdYcPSxG\nHjkmHka9DkKuAXp+8tSqWMAY9BwMet3k1sdnYSbJMnQcnYJFMoNCLYvIsoyJYBTD7gAGx/wYGvVj\nyB3AmC+EEU8Aw+4gQhERgWAEwbA4ueuUa0ChzYQlRRaUFFhQKJiwpNCCJYUW5Jj1Sr8lQtJGoaYx\nsd3C6yMTuOBy4/KAD4OjfgyOTe4iAohvWRXnm1EkmJBvNSDPakSRMDmOZDbqkGPiVT82QshcUKip\nkCzLGHIH0HfdB894CGO+EC5d9eJC/xgGxwLQ8xwW5Zmxxi5gRUkuSgosKMozoSjPDItRR2FF7mgU\nagoRJRmDo36MeCbHs64MjWPMF8SVwXH0D47DqOewosQKwWqAkGPA8kW5KFueh0UFZhj1NBOHkFuh\nUFtAkiRjcMyPgVE/Bkf96Bvw4drwBK4Oj+P6iD8+nrWowIzli3KRl2vEsuIcLCvOofEsQuaIQm2e\nBENR9F7xwDXgw+VrXly85sWla14Y9RwKBRMWF1qwosSKxQUWLCmyYElRDgw8N/sTE0LSQqE2B1FR\nwsCoH64BHxzOEZy5OILL131YUZKL5YusWLnEipUlk//acug+AYRkEoXaLCRJxuXrXpy9PAbnFQ8u\n9LvRN+BDfq4RiwstWLcyH+WrC1C2PI+2vAhRAQq1m4iSjMvXvDjdO4wPzg3hXN8YLEYe60vzUbrE\nhjV2AaVLbDAaaLCeEDW640MtGIri1IVhnLk4gt4rHpzvG4OQa0T56gJsLivC3SvzUWCj6/MTohV3\nXKhJkgznVQ9+e/oaPrk4ivOuMaxZlodNdxWidKkNZfY8CLk0DkaIVt0RoRaOiDhzcQTvnb6Ozo+v\nwqDXoWLjYmwoLcCG0gKYjXShPkKyRdaGWiAUxfuO6/jNR1fw0fkhLC+x4r67F+Hzm5diaVGO0uUR\nQhZIVoWaPxhBz9lBvPPhFXx4bgh3r8zH5+9Zis+VlyCXJrMSckfQfKhdvu7FyTMD+PDcEM72jWLd\ninx8fvNSPFC+GFYLBRkhdxpNhtr1kQm88+EV/PqDKxj3h/G58sXYXFaEe+4qgommWhByR9NMqPmD\nEbx10oX29/ow5A7g4XuXomLTEmwoLaCrmBJC4lQfaleHx/HjXzvxy55+bC4rws4HVmLjmkIKMkLI\ntFQZarIs43TvCH78Ti/OXBzFjgdW4HcfWkWTYAkhs1JVqPmDEbzd3Y9fdPXBH4zgq19cjUe22umU\nJEJIylSVFsc7L+G1n53Bf3/qPjy4cTHtYhJC0qaqLbVYKXQ5akLIXKlqS43CjBByu+hmjISQrEKh\nRgjJKhRqhJCsQqFGCMkqFGqEkKxCoUYIySoUaoSQrEKhRgjJKhRqhJCsQqFGCMkqFGqEkKxCoUYI\nySoUaoSQrEKhRgjJKtyLL774otJFOBwOvP322/B6vejs7ITX64Xdble6rKzk9Xrx+uuvo7i4GAzD\nYHBwEIcPHwbDMAnrPJU+ma9lyA1tbW0YHBycdh1lsk803W+ywvr6+uQ9e/YktH3rW9+S+/r6FKoo\nu/X19cllZWXxn23btsnHjx9PWma2PpmvZcgNJ06ckLdt2yafOHEi6bFM9onW+03x3c833ngD1dXV\nCW1PPvkkDh06pFBF2e/VV19FV1cXOjo60NXVhaqqqoTHU+mT+VqGAC6XC/v374fL5YLNZpt2mUz2\nieb7TelUffTRR5O+ATwej1xWVqZQRdmtr69v2i2BqVLpk/lahiR69NFHp+2fTPaJ1vtN0S01r9c7\n7beT1WoFMPkNRjIrlT6Zr2VIajLZJ9nQb4reo8Dj8QC4scJu5nK5tDM4qSEulwttbW0AJvvAZrPF\nd0FT6ZOY212G+jY1meyTVJZRe78pGmper1fJl78jxb6Bp46jPfvss/G2VPpkvpYhqclkn2RDvyl+\noIBkltVqnXYQuKGhQaGKCJlfqgi1bPh20DK73R4fT4mhb371oS221CgaarF989iYQUxshap9312L\nmpqaktpiu6RTx0tm6pP5WoakJpN9kg39pmioWa1W2O32pG8Fj8cTf4zMH5fLhYaGhqQjWLEPsN1u\nT6lP5msZkppM9kk29Jviu58VFRU4ffp0QpvD4cCDDz6oUEXZy26348CBA0kfzM7OTmzYsCF+xCuV\nPpmvZUhqMtknmu83pSfKeTyepFMy9uzZo5lTMrTm+PHjCevW4/HIjz32mHz69OmEttn6ZL6WIYmm\nO21NljPbJ1rvN0aWZVnpYHU4HDh27Bg2btwIl8uFDRs2oKKiQumyslZbWxtcLhfcbjd8Ph9qamqm\nPaF5tj6Zr2XudF6vF42NjXC5XHjzzTdht9tRUVGBioqKhKk3mewTLfebKkKNEELmi+JjaoQQMp8o\n1AghWYVCjRCSVSjUCCFZhUKNEJJVKNQIIVlF0UsPETIX+/fvj894f+2112557a/5cujQIbz77rvw\ner04evTogr8euT0UakRzXC5XRsIs5vnnnwdw47pzRN0o1LLc2rVrYbVaUV5ejtzcXAiCgM7OTrhc\nLuzYsQOCIMDtdqO/vx8OhwPV1dU4cOBA/Peffvpp1NTUaGY2OSEUalksdnrLzVs1TU1NaGhowPe/\n//2E5Zubm3HixImk59DCdekJiaFQy2Jerxe1tbUp76ZVV1fD4XAktHV0dCxEaYQsGDr6mcU8Hg82\nbNiQ1u9o4XpZhMyEQi2Leb3etEOKQo1oHe1+ZrGb77yezu94vV7s2bMHDocDL7/8MqqqquByufDc\nc8/B5XLh5Zdfji/n8Xhw4sQJPP/887Db7WhubobNZotf3ih29HAql8uFN954Axs3boTH44HX60VN\nTU3a9c5XTZ2dnfB6vbBarfH7Xn788cfT1k7UjUKNTMtqteLo0aO477774m12uz3e5nA4UFVVlbBl\nt3//flRVVSXcrerpp59Gc3NzQpvD4cALL7yAo0ePxtsOHTqEQ4cOpR0i81GTy+WCw+FICFWXy4Xm\n5ua0aiHqQLufZEbT7Y7G7j419bHy8nJ0dnYmLb9+/fqkI6ovvPACamtrE9rq6urwyiuvzPkuRrdT\nU2dn57TPR9NYtIlCjczJzQcgYkdYy8vLE9oFQYDP54v/f2yraLrft1qtSdfGz0RNFRUVaGhoQFNT\nU8L0lZvvj0q0gXY/yZzEbqt3s9mmj8SmjDgcjqTpIzt37rzl8y5kTXa7HS+//DIaGhrQ0NAAu92O\nHTt20HiaRlGoEUVMdxBjLgc25ktVVRWqqqrgcDhw+vRpNDU14cyZM3j11VcVq4nMDe1+koyK7SKq\n6SyF5ubm+Fjehg0bUF1djY6OjqS71hNtoFAjGRUbgG9ra0t6LDbelmler3fagwUVFRVJdyon6keh\nRmY03ZZKbB5Yqsvf7MCBA2hubk7aWmtra0v7DIj5qunw4cNJbW63myYjaxD34osvvqh0ESQzDh06\nhFdeeQXHjx9HKBTC22+/jRMnTsBmsyX98bpcLrzwwgvo6emBw+GAwWCAzWaLt124cAGhUAhbt25F\nW1sb/uZv/gZDQ0NwOBwoLi6G3W7HoUOH8Prrr+PChQs4f/48HnroofjzfP3rX8cPfvADXLhwAUND\nQ+ju7sZTTz2V0vv40Y9+hJ07d8JgMCTUOdeavF4vnnrqKbS1taG/vx/d3d1499138c1vfjPh4MPx\n48fxyCOPwGAwzGu/kPlF9/0kmvP000/j5ZdfzvjFGp999ll873vfo4tEqhztfhJCsgqFGiEkq1Co\nEUKyCoUaISSr0BkFRHPsdjv27NkDIDN3k4rdvYom4moDHf0khGQV2v0khGQVCjVCSFahUCOEZBUK\nNUJIVqFQI4RkFQo1QkhW+f+gvqUHqcTTmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da4760208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scale = res[1].gm1[0]/5\n",
    "\n",
    "for i in range(3):\n",
    "    plt.plot(res[i].gm1/scale)\n",
    "plt.title('Mean gap junction coupling')\n",
    "plt.xlabel('Time [ms]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}