oscillation_synchronization/Diesmann_1999_synfire_chains.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _(Diesmann, et, al., 1999)_ Synfire Chains\n",
    "\n",
    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/examples/blob/main/oscillation_synchronization/Diesmann_1999_synfire_chains.ipynb)\n",
    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/examples/blob/main/oscillation_synchronization/Diesmann_1999_synfire_chains.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "Implementation of the paper:\n",
    "\n",
    "- Diesmann, Markus, Marc-Oliver Gewaltig, and Ad Aertsen. \"Stable propagation of synchronous spiking in cortical neural networks.\" Nature 402.6761 (1999): 529-533.\n",
    "\n",
    "Author: [Chaoming Wang](mailto:chao.brain@qq.com)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T03:57:10.673040700Z",
     "start_time": "2023-07-22T03:57:10.012474800Z"
    }
   },
   "outputs": [],
   "source": [
    "import brainpy as bp\n",
    "import brainpy.math as bm\n",
    "\n",
    "bp.math.set_platform('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T03:59:35.279583600Z",
     "start_time": "2023-07-22T03:59:35.261647900Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.4.3'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bp.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T03:57:10.679313300Z",
     "start_time": "2023-07-22T03:57:10.675439200Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "duration = 100.  # ms\n",
    "\n",
    "# Neuron model parameters\n",
    "Vr = -70.  # mV\n",
    "Vt = -55.  # mV\n",
    "tau_m = 10.  # ms\n",
    "tau_ref = 1.  # ms\n",
    "tau_psp = 0.325  # ms\n",
    "weight = 4.86  # mV\n",
    "noise = 39.24  # mV\n",
    "\n",
    "# Neuron groups\n",
    "n_groups = 10\n",
    "group_size = 100\n",
    "spike_sigma = 1.\n",
    "\n",
    "# Synapse parameter\n",
    "delay = 5.0  # ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T03:57:10.699542700Z",
     "start_time": "2023-07-22T03:57:10.683658500Z"
    }
   },
   "outputs": [],
   "source": [
    "# neuron model\n",
    "# ------------\n",
    "\n",
    "\n",
    "class Groups(bp.dyn.NeuDyn):\n",
    "  def __init__(self, size, **kwargs):\n",
    "    super(Groups, self).__init__(size, **kwargs)\n",
    "\n",
    "    self.V = bm.Variable(Vr + bm.random.random(self.num) * (Vt - Vr))\n",
    "    self.x = bm.Variable(bm.zeros(self.num))\n",
    "    self.y = bm.Variable(bm.zeros(self.num))\n",
    "    self.spike = bm.Variable(bm.zeros(self.num, dtype=bool))\n",
    "    self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool))\n",
    "    self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7)\n",
    "\n",
    "    # integral functions\n",
    "    self.int_V = bp.odeint(lambda V, t, x: (-(V - Vr) + x) / tau_m)\n",
    "    self.int_x = bp.odeint(lambda x, t, y: (-x + y) / tau_psp)\n",
    "    self.int_y = bp.sdeint(f=lambda y, t: -y / tau_psp + 25.27,\n",
    "                           g=lambda y, t: noise)\n",
    "\n",
    "  def update(self):\n",
    "    tdi = bp.share.get_shargs()\n",
    "    self.x[:] = self.int_x(self.x, tdi.t, self.y, tdi.dt)\n",
    "    self.y[:] = self.int_y(self.y, tdi.t, tdi.dt)\n",
    "    in_ref = (tdi.t - self.t_last_spike) < tau_ref\n",
    "    V = self.int_V(self.V, tdi.t, self.x, tdi.dt)\n",
    "    V = bm.where(in_ref, self.V, V)\n",
    "    self.spike.value = V >= Vt\n",
    "    self.t_last_spike.value = bm.where(self.spike, tdi.t, self.t_last_spike)\n",
    "    self.V.value = bm.where(self.spike, Vr, V)\n",
    "    self.refractory.value = bm.logical_or(in_ref, self.spike)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T03:57:10.717168700Z",
     "start_time": "2023-07-22T03:57:10.699542700Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\codes\\projects\\brainpy\\brainpy\\_src\\deprecations.py:86: DeprecationWarning: brainpy.TwoEndConn is deprecated. Use brainpy.synapses.TwoEndConn instead.\n",
      "  _deprecate(message)\n"
     ]
    }
   ],
   "source": [
    "# synaptic  model\n",
    "# ---------------\n",
    "\n",
    "class SynBetweenGroups(bp.TwoEndConn):\n",
    "  def __init__(self, group, ext_group, **kwargs):\n",
    "    super(SynBetweenGroups, self).__init__(group, group, **kwargs)\n",
    "\n",
    "    self.group = group\n",
    "    self.ext = ext_group\n",
    "\n",
    "    # variables\n",
    "    self.delay_step = int(delay/bm.get_dt())\n",
    "    self.g = bm.LengthDelay(bm.zeros(self.group.num), self.delay_step)\n",
    "\n",
    "  def update(self):\n",
    "    # synapse model between external and group 1\n",
    "    g = bm.zeros(self.group.num)\n",
    "    g[:group_size] = weight * self.ext.spike.sum()\n",
    "    # feed-forward connection\n",
    "    for i in range(1, n_groups):\n",
    "      s1 = (i - 1) * group_size\n",
    "      s2 = i * group_size\n",
    "      s3 = (i + 1) * group_size\n",
    "      g[s2: s3] = weight * self.group.spike[s1: s2].sum()\n",
    "    # delay push\n",
    "    self.g.update(g)\n",
    "    # delay pull\n",
    "    self.group.y += self.g(self.delay_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T03:57:10.751984600Z",
     "start_time": "2023-07-22T03:57:10.717168700Z"
    }
   },
   "outputs": [],
   "source": [
    "# network running\n",
    "# ---------------\n",
    "\n",
    "def run_network(spike_num=48):\n",
    "  bm.random.seed(123)\n",
    "  times = bm.random.randn(spike_num) * spike_sigma + 20\n",
    "  ext_group = bp.neurons.SpikeTimeGroup(spike_num, times=times, indices=bm.arange(spike_num))\n",
    "  group = Groups(size=n_groups * group_size)\n",
    "  syn_conn = SynBetweenGroups(group, ext_group)\n",
    "  net = bp.Network(ext_group=ext_group, syn_conn=syn_conn, group=group)\n",
    "\n",
    "  # simulation\n",
    "  runner = bp.DSRunner(net, monitors=['group.spike'])\n",
    "  runner.run(duration)\n",
    "\n",
    "  # visualization\n",
    "  bp.visualize.raster_plot(runner.mon.ts, runner.mon['group.spike'],\n",
    "                           xlim=(0, duration), show=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T03:57:11.941024200Z",
     "start_time": "2023-07-22T03:57:10.736317100Z"
    }
   },
   "outputs": [
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      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_network(spike_num=51)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When external spike num is 44, the synchronous excitation disperses and eventually dies out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T03:57:12.873764900Z",
     "start_time": "2023-07-22T03:57:11.941024200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "37317a4cebd7454185c03df265c774bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SCnvYsl/5ae4Z29hSxwgWPxzTTk9/QEYqx2zJHDiNzERu2XI1yNW9c4LSdtjAS+2X02XlmE6BI+GZD5GRSo8Xr2KCUjfwJvbP3PFS++WlsrqFjJTP2JI5cJoXr2K44reTl7IDTvJj22Fr3Xqp/fJSWf2K6Q9SxPQH/sdt0Hay+bZqdAx1i1xg+gOL2Xo1hcz48YrfD8rLy1VUVKT9+/e7dqxxrDuDbAr8gIxUilqKaHv16qU9e/aoqKhIu3fvdrmEgH+5nblw+/sBZI6MFHyDq3pkyu3MhdvfD8BeZKRS1FJEy5ia9HBVDwDINTJSFmNMTXq4qgcA/wpqrwMZqRRx1x4AAK2ztdeBjBQApCioV8SADYLa60AgBUdxYkMuuT2pKvs7giyow10IpOAot09sCBa3r4izsb8TjAHeQiAFR7l9YkOw5PKKuKWAJxv7e7rBGIEX4C4Gm6eIweYAjuXUwNp0p1WxdYAvYAsGmyMruGpFqthXUuNUtjXdrBpZX8BdZKRS5PWMVLavWpmM1L/IcADwEzJSyIpsX7UyiNy/yHAgSMjAoqPISKXI6xmpbCMjBcAPyMD6Hxkpn/L6VVBQ5wsB4C9kYNFRBFIuoWsMANzHRWFuOZ1EcCNJQSDlEq6CAABB43QSwY0kBYGUS7gK8n73ppf4YVv74Te4hW0HWzidRHAjScFg8xQFfbC5E4PLGeSZO37Y1n74DW5h2yHIGGwOKziRLqV7M3f8sK398BvcwrYDnENGKkU2ZaTcmHqA6Q4AAF5ERgrNtJUdcmosBGO6Usd4FAAIDgIpD2orTc+0Cu6jDnKLwBVIH8dN9hBIeVBb2SHGQriPOsgtAlcQFKSP4yZ7GCOVIpvGSAH4BOP3wF2J6QvSceP0+ZtAKkUEUgBgpyAFBUgfgZQlCKQAAPAe7toDxBgIAICdCKRyhECgYxgYCcALaOuDh0AqRwgEOoY74QC4Id3AiLY+eAikciRIgYATV2RMCArADekGRkFq6/ExBpuniMHmqeNWZAAdZcudeLaUA5njrj1LEEiljoYHQEdxQYZs4a49eA7dcIC32DhAmi4yeAUZqRSRkQLgV2R/4GdkpAAAjiL7k102ZvjgHAIpIAU0jP5APbaM7vjsYgqE3HPz2M5q196BAwfUtWvXbH2cVejaCza6PvyhqR7D4bAWLFhA4ABHcMNN7rXVRlvXtTd27Fi9//77zZa/9NJLOuuss7JRJsA6dH34Q3l5ucLhsBoaGsgWwDFk+HLPzTY67UCqR48eGjp0qKqrqyVJjY2Nmj17tkaPHq2JEydmvYCADWgY/aGsrEwLFiwgKAZ8xs02Ou1A6qmnntLcuXP19a9/Xdddd51GjRqln/3sZ3r66af1wx/+0IkyAoxtQdYQFCPI7UmQf7tjTIbKy8tNKBQy+fn55q9//WumH+MZ8XjcSDLxeNztogRSLBYzkkwsFnO7KECSyspKE4vFTGVlZZvLYA8vtycd3be8/Nsz5fT5O+1Aas+ePebKK680kUjELFy40Fx//fWmW7du5ic/+YkT5bMGgZS7ODHBVi2dmIJ4svISL7cnbe1bqfwuL//2TFkXSJWUlJjzzjvPvPfee4ll1dXVpqioyFxyySVZLZxNCKQAtISMFHKprX2LAL5lTp+/057+4N5779XMmTOVl5c8vOr999/XV77yFS1fvryjvY1WYvoDoGO4JRxwFsdYy6yb/uAHP/hBIog6dOhQYnm/fv0cCaJ27NihG264Qb169VLXrl111llnae3atYn3jTGaPXu2SkpKVFhYqLFjx+r1119P+oz6+npNnTpVvXv3Vrdu3TRx4sQWp3AA4BwmKURbGATdcdxI4Y60A6nGxkbde++9Oumkk3TCCSfovffek/RxgPXzn/88q4Xbu3evzjvvPOXn5+uZZ57RG2+8oQceeECf+tSnEuvcf//9mj9/vhYsWKBXXnlF0WhUF110kfbv359YZ/r06Vq2bJmqq6u1atUqffjhh5owYYIaGhqyWl4ArWMuLrSFQBuelW5f4N13321OPvlks3jxYlNYWGjeffddY4wxS5YsMZ///Oez2u945513mlGjRrX6fmNjo4lGo6aioiKx7NChQyYSiZiqqipjjDH79u0z+fn5prq6OrHOjh07TF5ennn22Wdb/exDhw6ZeDyeeG3fvp0xUgDgEMaVwSlOj5FKOyP1i1/8QgsXLtT111+vcDicWD506FD94x//yGKI9/GcVSNHjtRVV12lPn36aPjw4XrkkUcS72/evFm1tbUaP358YllBQYHGjBmj1atXS5LWrl2rI0eOJK1TUlKiIUOGJNZpybx58xSJRBKv/v37Z/W3uY00OoKGfd5udEvBq9IOpHbs2KFTTjml2fLGxkYdOXIkK4Vq8t5776myslKDBw/WH//4R5WVlelb3/qWfvGLX0iSamtrJUnFxcVJf1dcXJx4r7a2Vp07d1bPnj1bXaclM2bMUDweT7y2b9+ezZ/mOtLoCBr2eQBOSDuQOuOMM/Tiiy82W/7rX/9aw4cPz0qhmjQ2Nuqzn/2s5s6dq+HDh+u///u/dcstt6iysjJpvVAolPRvY0yzZcdrb52CggL16NEj6WWrTK60Ga/iX2ReWsY+D8AJaQdSs2bN0pQpU3TfffepsbFRS5cu1S233KK5c+fqrrvuymrh+vbtq9NPPz1p2WmnnaZt27ZJkqLRqCQ1yyzt2rUrkaWKRqM6fPiw9u7d2+o6XpfJlTZpdP+yOfPiZpDHPg/ACWkHUpdddpmWLFmiP/zhDwqFQrrrrrv05ptv6ne/+50uuuiirBbuvPPO01tvvZW07O2331YsFpMkDRo0SNFoNGnahcOHD2vlypUqLS2VJI0YMUL5+flJ63zwwQfauHFjYh2v40obx7J5f7A5yAOAjDgyhD1LXn75ZdOpUyczZ84c884775jHH3/cdO3a1SxevDixTkVFhYlEImbp0qWmpqbGTJo0yfTt29fU1dUl1ikrKzP9+vUzK1asMOvWrTMXXHCBGTZsmDl69GjKZfH6zObcEQO3HLvvsR8CyDXrHhGTa7/73e/MkCFDTEFBgTn11FPNwoULk95vbGw0s2bNMtFo1BQUFJjRo0ebmpqapHUOHjxopkyZYoqKikxhYaGZMGGC2bZtW1rl8HogxaMD4BYv7nu5CvgILGETv+6PVgRSn/rUp0zPnj1TevmVG4FUNndqTgxIVbbr0Iv7RK6CPy8GmUgN+709rAikHn300cTrgQceMD179jTXXnut+fGPf2x+/OMfm2uvvdb07NnTzJ8/35FC2sCNQMqLO7UXy4xk1KE3Lzy8eOL2My8eR37dh6wIpI515ZVXmocffrjZ8ocffthcfvnl2SiTlbyekcoVL5Y5V1raNjZuLxvLhPZ58cTtZxxH9rAukOrWrZt55513mi1/++23Tbdu3bJSKBu1VhEcLEhVSyc6v578OC5yj23uLrZ/+9zaRtYFUgMGDDD3339/s+X333+/GTBgQFYKZaPWKsKvJ0Jkn1cyUtnQdFyEw2Hf/TagJZwL2ufWNrIukFq0aJHJy8szl1xyibn33nvNvffeay699FITDofNokWLHCiiHchIAamrrKw04XCYE4tH0a6lj23WPr9mpELGGJPu3FMvvfSSHnroIb355psyxuj000/Xt771LZ1zzjkZzGTlDXV1dYpEIorH41Y/LgawRVVVlSoqKlReXs5s4h4zcOBAbd26VbFYTFu2bHG7OECHOH3+Tntmc0k655xz9Pjjj2vdunVav369Hn/8cV8HUe3h2WZAc0F6JIvf2gCbZ8cHbJNRRqqxsVGbNm3Srl271NjYmPTe6NGjs1Y4m7QV0XL1BgSbm20AmT+gbdZlpP72t7/plFNO0WmnnabRo0dr7Nixidf555+f9QJ6AVdvgP+yMulwsw3g+YWAu9LOSJ111ln6j//4D919993q27evQqFQ0vuRSCSrBbQFY6SAtpGZdQcZKaBtTp+/0w6kunXrpldffVWnnHJK1gtjMwIpoG2c0AHYyLquvXPOOUebNm3KekEAeFtHBpcHuVsQgLelHUhNnTpVd9xxhx599FGtXbtWr732WtIryDgZAJlhnA/aQtsKm6XdtZeX1zz2CoVCMsYoFAqpoaEha4WzSSqpQcaIIKg62q1HtyDaQtuKjrCua2/z5s3NXu+9917iv0HG3XsIqo5mlII051Q2BSVTQ9sKm2U0j1QQMdgcaB0ZJXeQqQHaZ8Vde0899ZS++MUvKj8/X0899VSb606cODFrhbMJgRSCiiDJXtQN0D4rAqm8vDzV1taqT58+LY6RSnxYwMdIAX6UadaDkzyOxf4At1gxRqqxsVF9+vRJ/H9rL78GUUCQZTo+hTvxkgVlPFNr2B/gVxk9tBhAcGQ6EJwBwsmCHkiwP8CvGGyeIrr2AHQEXVuAO6zo2gPgH0HvYnILUzwA/kQgBQRM0LuYAHBBlU0EUkDAMFYFHcEJ2H6p1BEXVNmT0RipxsZGbdq0Sbt27VJjY2PSe6NHj85a4WzCGCl4DWNy4AQmAbVfKnUUpPbB8fO3SdOaNWvMoEGDTF5engmFQkmvvLy8dD/OM+LxuJFk4vG420VBiiorK00sFjOVlZVuF8UVsVjMSDKxWMztosBHgn5ceYGX68iJsjt9/k47kBo2bJi56qqrzBtvvGH27t1r9u3bl/TyKwIp7wl6IOHlxhSfaKkeqVs08du+4ES7bV0g1bVrV/POO+84URarEUh5TyoNjN8aIRuxjTumpRNL0C8S8Am/7QuByEidf/755plnnnGiLFYjkPInvzVCLXE7kAnCNj5Wtrc3GSm0hX2hfdYFUkuXLjWnn366WbRokfn73/9uXn311aSXXxFI+dPxjZAfGyW3Axk/btO2uL29ASSzLpA6foB50yBzBpvDRumexP14EqSLM7fYlnahPmBdILVly5Y2X35FIOVN6QZGQW10/RhAelFQ978m6f7+VNZn34Z1gVRQEUh5k20nJtvK08TWcgVN0E/66fz+yspKEw6H212ffRtWBlKbNm0yU6ZMMRdeeKEZN26cmTp1qtm0aVO2y2YVAilkQ9BPlGhb0E/66fz+pmMpHA57YnsFvW7d5PT5O+1HxPzxj3/U6aefrpdffllDhw7VkCFD9NJLL+mMM87Q8uXL0/04IFB4PIs9bHzUSdAfbJzO7286lhYsWOCJ7cUjWXws3cjrrLPOMnfeeWez5XfeeacZPnx4VqI7G5GRgg24qs2etrKDbGe0JtN9g33KPU6fv9N+1l6XLl1UU1OjwYMHJy1/++23NXToUB06dCh7UZ5FeNYebMBzzrKnrWeN2bqdg/R8NFvZum+gdU6fv9Pu2jvxxBO1YcOGZss3bNigPn36ZKNMgFVs6gLyategTduwSVvdSLZuZy93D9m4D2TC1n0D7kk7I3XPPffoRz/6kcrLy1VaWqpQKKRVq1bpvvvu0x133KHvf//7TpXVVWSkgosr0I5jG2aHlzNS7ANwi+Pn73T7AhsbG838+fPNSSedlJiQ86STTjIPPvigaWxszHbXozVa6mOlzzsYqOeOYxuCfQBusWqM1NGjR/X444/r4osvVjQa1f79+yVJ3bt3z36EZ5mWIlqusAAAsJtVY6Q6deqkW2+9VfX19ZI+DqCCEES1hr5yALnilzFGgN+kPdj8nHPO0fr1650oi+cEac4XGnHAXV4eaA74WdqB1De/+U3dcccdWrBggdasWaPXXnst6QV/ohH3HoLf9Ni+vdLJgNv+WwA/Sfuuvby85rFXKBSSMUahUEgNDQ1ZK5xNgn7XnpfvFgoqxvClx0/by0+/pT20TWiPVWOkJGnz5s3NXu+9917iv/CnIHVj+gVj+NLjp+117G/xe3aKbDnclnZGKqiCnpEC4AynMyp+z06RkUJ7nD5/px1I/eIXv2jz/RtvvLFDBbIVgdTHaLSA1KR6rDgd6HDMIuisC6R69uyZ9O8jR47owIED6ty5s7p27ao9e/ZktYC2IJD6mN+vboFsSfVYIdBBKthPMmfdGKm9e/cmvT788EO99dZbGjVqlH75y19mvYCwi5/GkQBOSvVYYfwhUsFYMHtlbYzU3//+d91www36xz/+kY2Ps04uM1JceQAAjsV5IXPWZaRaEw6HtXPnzmx9XKBx5QGv8fudYYDbyFzaK+1A6qmnnkp6Pfnkk6qqqtLkyZN13nnnOVHGwKH7DF5jW/BPYAcgVzo8IWcoFNKJJ56oCy64QA888ID69u2b1QLagsHmQOts63bgpggATay7ay+oCKQA77AtsAPgHmvHSB0+fFhvvfWWjh49ms3yeB5dCvajjvzPqfEk2dp32AcBHzFp+uijj8xXvvIVEw6HTTgcNu+++64xxpipU6eaefPmpftxaZk7d66RZKZNm5ZY1tjYaGbNmmX69u1runTpYsaMGWM2btyY9HeHDh0yU6ZMMb169TJdu3Y1l112mdm+fXta3x2Px40kE4/H21wvFosZSSYWi6X1+cgd6giZyta+wz4I5E6q5+9MpZ2RmjFjhl577TU9//zz6tKlS2L5uHHjtGTJkuxEdy145ZVXtHDhQg0dOjRp+f3336/58+drwYIFeuWVVxSNRnXRRRdp//79iXWmT5+uZcuWqbq6WqtWrdKHH36oCRMmOPKAZQaK2y/IdUQmpGOyte8EeR9MF/ssrJdu5DVgwACzZs0aY4wxJ5xwQiIj9c4775ju3btnN8z7//bv328GDx5sli9fbsaMGZPISDU2NppoNGoqKioS6x46dMhEIhFTVVVljDFm3759Jj8/31RXVyfW2bFjh8nLyzPPPvtsymVwOqIFcoFMCLymo/tsZWWlicViprKyMrsFg2dYl5H617/+pT59+jRb/tFHHykUCnU4sGvJbbfdpksvvVTjxo1LWr5582bV1tZq/PjxiWUFBQUaM2aMVq9eLUlau3atjhw5krROSUmJhgwZklinJfX19aqrq0t6AV5HJgReU1paqnA4rNLS0oz+3rapOeA/aQdSn/vc5/T0008n/t0UPD3yyCM699xzs1ey/6+6ulrr1q3TvHnzmr1XW1srSSouLk5aXlxcnHivtrZWnTt3bvaMwGPXacm8efMUiUQSr/79+3f0pwQa6Xk7MKkfvGb16tVqaGho88K3LVw8OIM2/RNpB1Lz5s3TzJkzdeutt+ro0aP68Y9/rIsuukiPPvqo5syZk9XCbd++XdOmTdPixYuTxmMd7/hMmDGm3exYe+vMmDFD8Xg88dq+fXt6hUcSrgrRETY02jaUIYg6Gghx8eAM2vRPpB1IlZaW6q9//asOHDigT3/60/rTn/6k4uJirVmzRiNGjMhq4dauXatdu3ZpxIgR6tSpkzp16qSVK1fqoYceUqdOnRKZqOMzS7t27Uq8F41GdfjwYe3du7fVdVpSUFCgHj16JL2QOa4K0RE2NNo2lMHPWgtUCYTsRJt+DEdGXmVJXV2dqampSXqNHDnS3HDDDaampiYx2Py+++5L/E19fX2Lg82XLFmSWGfnzp0MNgc8xIYBwzaUwSbZ3h7cCAGnOH3+tjqQasmxd+0ZY0xFRYWJRCJm6dKlpqamxkyaNMn07dvX1NXVJdYpKysz/fr1MytWrDDr1q0zF1xwgRk2bJg5evRoyt9LIAX4EwFSZrId+FAPcIrT5+9OqWau8vLy2h13FAqFcj7T+Xe/+10dPHhQ3/zmN7V3716dc845+tOf/qTu3bsn1vnRj36kTp066eqrr9bBgwd14YUX6tFHH1U4HM5pWQHY59guO7qPUldeXp54DE82lJWVsf3hSSk/a+/JJ59s9b3Vq1fr4YcfljFGBw8ezFrhbMKz9nKLZ6UhV9jXgOb8dFw4fv7uSDrrzTffNFdccYUJh8PmxhtvNFu3bs1KmsxGdO3lFuMlgs0v3TxO/A6/bBvYzU9tsHUTckrSzp07dcstt2jo0KE6evSoNmzYoMcee0wDBgzIZoyHAPPTHSHcNp++XNwhl4t6ceJ3cPcgciFXbbAv2sd0oq59+/aZ7373u6awsNCce+655oUXXnAkurMRGSlkyk9XdrmSi6xLLuqFjJR3sF3dkYvj0Jq79u677z5TVFRkTj/9dPPb3/7WkcLYjEAKmaKBthNBDo7FBY87cnHMOH3+TnmweV5engoLCzVu3Lg273ZbunRph7NkNmKwOWAPJwbCZuMzBw4cqK1btyoWi2nLli1ZKRdyw0+Dq22X623t9Pk75UDq5ptvTumhxIsWLepwoWzkdEVwEAOpaylg6egxlI0giOMYaF+uLzisvmsvSJxODZJWBlLXUndAR48hr3TLeaWcQGtyvQ9b07UXdGSkALsF5Rii+zBYgrJfO8marr2gY4wUYJegnmCC+ruDisC545w+f2c0jxRwPF/MBQJPCep8SmVlZdqyZQtBVED4aU49vyKQQlYE9aQG97R0giGgh98QONuPQApZYeNVEydVf2vpBENADziHNrVljJFKEWOkvIexBcHD+CHAOV5tUxkjBWSotLRU4XBYpaWlbhcFOUI3COAcG3sebEAg5WGkWdu2evVqNTQ0aPXq1W4XBQA8jwuVlhFIeRjjQdrG1ZPdcnUhwAUH4F82HN+MkUqRjWOkGA8CL8vVeAuvjusA0L5Ujm/GSKFVpFkh2XFFlolcZQzJTPqDV/fzXAriNrLh+CYjlSIbM1KA5M+MC9lWHM+P+3m2sY1aRkYKQJtsuCLLNrfG/wXxit4r/LifZxvbyB1kpFJERgrIjaqqKs2cOVOSNGfOnJxmpLiiB/yHjBSAQJk5c6b27NkjSW0GUU5kj7ii9zYyinADgRRwDBpi7zi++y8bdccNHN7GlDBwA4EUcAwaYvfNmTNHsVhMc+bMSVp+fKB0fPaIugMZRbjCICXxeNxIMvF43O2iwEGVlZUmFouZysrKNpch92KxmJFkYrFYi++nUk/UJRA8Tp+/GWyeIgabBxcDkO1w7JQIktqcHqG16ROoSyB4GGwOuIzugo+5PX7s2PFL7XXjtfY+dQkg2wikgHbYOADZjaAm3TFITpaxvYCotfdtrEsA3kbXXoro2oNNUu2iyuYM4el+Ft1oAGxA1x6AZlLtosrmnWytZXNayzzRjQYgCAikAA9KtYsqW8FMW910rQVrdKPljtvj15CM+ggYR+4F9CGmP8g9blW3R1tTD7RVT9RhbrQ3NQRyi/qwi9PnbzJSsBYTLNqjrcxWW5kn6tBZTZmP0tJSulEtQrd2sBBIWYrUMI2RTTLtpqMOndUUqK5evZpuVIukc7zQ1meHq9vRkTyXD6WSGsxmNwapYXRUpvvj8X9H95y9qJtPeHVb0NZnR1vb0emuPQKpFKVSEdk8ILzaKMAeme6Px/+dLQ09xwTaYst+mi726+xoazsSSFki1xmpbLK1XE4K4m8+XjYyUpMmTTKhUMh07drV9W3p1ROlF3nx+PFimZEbBFKW8PJde0E8AQXxNzshHA4bSSYcDrtdFE6UHZTO9uP4CYagHFPctYcOC+KA3yD+ZidcffXVCofDuvrqq5u9l+vBncxL1THp3EGZzvHDYGnv4q7aLHEkPPMhL2ekgHSlcqVK1sJbnMo+sB94Fxmp7CAjBQRIqtmDVK5Uyfp5i1MZPfYD7yLLmx08tDhFPLQYNujoQ4gzedixpKw9+BgAco2HFgNI6OiYhlSzB2VlZSovL1dFRYVmzpzJOApIYjwUnOXV/YtAClbx6oGUKx3tRkknld8UtElyteuGfcIeDE6Gkzy7fzky8sqHGGyeGwxctYctA1Fb2ydsKV+QsM3hJKf2L6fP34yRShFjpHKjo2OA4D+t7ROpjvcCEGyMkUKgcBeJndzsXmttn+BuMQA2ICOVIjJSCLK2sj9kEQHYjIwUANe1lf3x7ABRDwjSQPsg/Vb4CxmpFJGR8j8yK5lhuzknSOPAgvRbkVtkpADl5mqVzEpmGNfmnCCNAwvSb4W/EEjBE3IR5NCQN9daAEs3TG4EKUgN0m+Fv9C1lyK69txF95E7WutuyVU3DPUOoKPo2gPE1apbWsvS5Sp752Z3K1k3AKkgkIL1OKG5p7UANtuBbWt17GZ3K2PmAKSCQArW44SWe7kOXluqY7e79RgzByAVVgdS8+bN0+c+9zl1795dffr00RVXXKG33noraR1jjGbPnq2SkhIVFhZq7Nixev3115PWqa+v19SpU9W7d29169ZNEydO1Pvvv5/Ln4IO4ISWe7kOXluqY7cDaC91J5O1DQbqOX1VVVUaMmSIs1/iyBP8suTiiy82ixYtMhs3bjQbNmwwl156qRkwYID58MMPE+tUVFSY7t27m9/85jempqbGXHPNNaZv376mrq4usU5ZWZk56aSTzPLly826devM+eefb4YNG2aOHj2acll4aDGCxIaH09pQBq/gYd/BQD2nr2mbOXn+tjqQOt6uXbuMJLNy5UpjjDGNjY0mGo2aioqKxDqHDh0ykUjEVFVVGWOM2bdvn8nPzzfV1dWJdXbs2GHy8vLMs88+m/J3E0gB7SP4cQfbPRio5/RVVlaa/v37O3r+trpr73jxeFySVFRUJEnavHmzamtrNX78+MQ6BQUFGjNmjFavXi1JWrt2rY4cOZK0TklJiYYMGZJYpyX19fWqq6tLeiGY3EinezWF73Z3XFB5qRsSmTu2nr3aRuRaWVmZNm7c6Oh3eCaQMsbo9ttv16hRoxL9nbW1tZKk4uLipHWLi4sT79XW1qpz587q2bNnq+u0ZN68eYpEIolX//79s/lz4CHHBge5arwy+U4bGlbGswG5wUWLPTwTSE2ZMkWvvfaafvnLXzZ7LxQKJf3bGNNs2fHaW2fGjBmKx+OJ1/bt2zMruMNsOHn63bHBQa4ar7a+s7U6b+3Ot1zuH2RG7EC74H9ctFjEkQ7DLJsyZYrp16+fee+995KWv/vuu0aSWbduXdLyiRMnmhtvvNEYY8xzzz1nJJk9e/YkrTN06FBz1113pVwGW8dIMfgwt9wYo3D8dzbVeVFRUdLylsrG/hFM1DtyzebxW06fv60OpBobG81tt91mSkpKzNtvv93i+9Fo1Nx3332JZfX19S0ONl+yZElinZ07d/pmsLnNOy+c0VTnRUVF7Z4s2T+CiXpHrtkcvAc6kLr11ltNJBIxzz//vPnggw8SrwMHDiTWqaioMJFIxCxdutTU1NSYSZMmtTj9Qb9+/cyKFSvMunXrzAUXXMD0B/C8XJ4sW/ouTtYAmtjcHgQ6kNL/n/vh+NeiRYsS6zQ2NppZs2aZaDRqCgoKzOjRo01NTU3S5xw8eNBMmTLFFBUVmcLCQjNhwgSzbdu2tMpCIAXb5LLhaulq0+YrUADZZ3Ow1JZAB1I2IZCCW1prvHIZyJCR8hfqDpnw6sUTgZQlCKTglqaxUEVFRcaYj0+CRUVFpmvXrqaoqIiToQfYFrh49YRoMxvq2Oky2PAbMykLgZQlCKTglmMDqcrKShMOhxPd3E0nQpsaODRnW+DC/pJ9NtSxDWXIlXR+q9Pnb8/MI4XcYy4aO8yZM0exWEwXX3yxpkyZooaGBoVCIRUVFSXmkGFyPrvZNucP831lnw11bEMZcsWm3xoyxhi3C+EFdXV1ikQiisfj6tGjh9vFaVVVVZUqKipUXl7e4UZy4MCB2rp1q2KxmLZs2ZKdAiJjTfURDoe1YMGCpPrNZr0DgJ84ff4mkEqRVwKpbAY/nJztQn0AQPqcPn/Ttecz2Ux3kv63C/XhDXSJA8FCRipFXslIAccii5V7dIkDdiEjBVdxde1tDELPPZsGwaJjaP+QCjJSKQpqRoqra28jIwVkzsb2j2M6fWSk4CqurnMnlavfdK+QGVcFZM7G9s+LWWbfZ/YcmZ3Kh5iQE05LZYK5pnWKiorSmlCRCRizj20KN3hxv2urbcvF72Fmc0sQSMFpqTQoTY+HycvLS2sG4yDNeJwrbFO7eTHg8Ku26iIXxxEzmwM+15T2ltRuN1xZWZm6d++uxsZGhcPhlLscbOyisE263Q9sU7t5sQvMr9oaYuCH44hACnBZOg1+VVWV9u/fr6Kiomazm7eFsVLt89KJ1/djTrLADyfoIPBF2+RInsuH6NqDU1Lt0ovFYokHGKeaBqd7I3Xpbis3u/Zs6FZk34JXMEbKEgRS9vNzw57pIHMbTrh+5eb+ZsO+zr4FryCQsgSBlP383LBneuJsGpxeVFTkywAT7rEhmIO7vLIPOH3+ZkLOFAV1Qk4vYaK6ltk4qSAA7/NK28KEnECKfDFo0QFBG3TLQGwgN4LWtrSGjFSKyEjBizLJ0nk9s+eVq2QAuUFGCjnBVbw/ZXJLv5emAWgJV8kAcolACpK8f/JEyzIJKrweiNDFCyCXCKQgyfsnT7/qaKYwk6CCQATIDjL9wUAgFSBtHdScPO10fKaQhhnwDjL9wUAgFSAc1NmVi6Dm+EwhdQh4B5n+YCCQChAO6uzKRVBzbKbw2OfslZeXk50CLBekTH+Q2yOmP0gR0x/geE3TBJSWlmr16tUpTxeQ6fQCx9/Wz23+AGxhc3vE9AeApZquNlevXp1WZirTTNbxGcW2MoxBvjoEkHuB7vFw5MEzPsSz9tCadJ83lYvnU7X13EGvPB/LaWwHIBh4aLElOlIRNNjItbb2uWw+3NmmfTvdsvj5IddBYNO+B7sRSFmiIxVBg+19xzfaXm7Es1l2m/btdMvi5TqEXfse7EYgZQkyUsF2fKNdVFRkJJmioiJjTHDr2KbfbVNZ4DzqG6lyOpDirr0Ucdde27z+oNv2HPv7JOmb3/ymjDEqKirS7t27k+5YKS8v9/W2AAAvcfr8TSCVIgKpttl862u2Nf1WSZo0aZKeeOKJpECr6a68IGwLALAd0x/AE4J062t5ebnC4bAkafXq1ZKSJ94L0rZwElM4APACMlIpIiOFY/m9K9MGQcpyAnAOGSnAQkF69INbyOwB3heEzDKBlMuc3smCsBM7IZXtxrZ1FsEqvIY2oblAPGjdkXsBfcip2yednguFuVYy09p2mzRpkgmHw2bSpElsWyAHvDTNAW1CczbUn9PTH5CRyoKOXIWk232R7nfRPZKZpu1WWlqatL1/9atfqaGhQb/61a8c2bZc0QLJvJTRoL1tLhCZZUfCMx9qK6LN5VVILr7LhisIWxy/vY/NSBmT/W3FFS2QjPYoe4K6LZnZ3BJtVUQud063H3gbNO1t72xvq6A2dACcF9S2nUDKEulUhNdPhl4vfy6xrbyDukLQBfUYIJCyRDoVkc2oP6g7vm1seWgx+0Pmgno1DgQdgZQl3MpI0fjb4fh6OPbfTfXddCcf3a52IggFgolAyhJOV0RraPxzr6Vt3lZGqim4CYfDrt0IwH4SPNQ5kBoCKUsEaYxU0LWW9WkviMlFRqo1ZKqChzoHUkMgZQm3xkgh944PmJqmPMjPzzeSTFFRkcslbI7gPXiocyA1TgdSPLQ4Rek89JAH2vpLp06d1NDQkPh3UVGRdu/e7WKJAACp4qHFHhSImVwD5Oqrr1YoFFJ+fr6Kioo0Z86cNtdndnIACA4CKaAVTQHR6NGjNWDAAB05ckTxeLzdv/PSIy2AoOPCBx1FIAW04tiAqLy8XOFwWA0NDZoyZUqbjS7P2wK8gwsfdBSBFNCKYwOisrIyLViwIBFMtdXolpWVqby8XBUVFVzlApbjwgcdxWDzFDk9WA3eUFVVpZkzZ0qS5syZ0+o4uIEDB2rr1q2KxWLasmVLDksIADgWg80Bi5SVlal79+7as2dPIivV0hgLrnIBIBgIpIA0HR8ktTTGwpY7NxlIiyBq2u+vu+469n+H0LZ8gq69FNG1l4y5sj5h87agixFB1LTfN41pZP/PPi+1LXTtZdFPf/pTDRo0SF26dNGIESP04osvul0kz+JOl0/Ykn1qCV2MCKKm/X748OEKh8MqLS11u0i+Q9vyicAEUkuWLNH06dM1c+ZMrV+/Xv/5n/+pL37xi9q2bZvbRfMkDqJP2JzitjnIA5zStN//61//UkNDg1avXu12kXyHtuUTgQmk5s+fr6997Wv6+te/rtNOO00PPvig+vfvr8rKyhbXr6+vV11dXdILn/DTQdReINTe+2TnADtxwYdcCMQYqcOHD6tr16769a9/rS996UuJ5dOmTdOGDRu0cuXKZn8ze/Zs3X333c2WM0bKf9rr62/vfZvHSAFA0DFGKgv+/e9/q6GhQcXFxUnLi4uLVVtb2+LfzJgxQ/F4PPHavn17LooKF7R31dre+37KzgEA0tPJ7QLkUigUSvq3MabZsiYFBQUqKCjIRbHgsrKysjaDoPbeBwAEVyAyUr1791Y4HG6Wfdq1a1ezLBUAAECqAhFIde7cWSNGjNDy5cuTli9fvpzbYgEAQMYC07V3++23a/LkyRo5cqTOPfdcLVy4UNu2baPLBgAAZCwwgdQ111yj3bt365577tEHH3ygIUOG6A9/+INisZjbRQMAAB4ViOkPsoFHxAAA4D1MfwAAAGApAikAAIAMEUgBAABkiEAKAAAgQwRSAAAAGSKQAgAAyBCBFAAAQIYIpAAAADIUmJnNO6pp3tK6ujqXSwIAAFLVdN52av5xAqkU7d69W5LUv39/l0sCAADStXv3bkUikax/LoFUioqKiiRJ27Ztc6QikLq6ujr1799f27dv53E9LqMu7EFd2IX6sEc8HteAAQMS5/FsI5BKUV7ex8PJIpEIB4UlevToQV1YgrqwB3VhF+rDHk3n8ax/riOfCgAAEAAEUgAAABkikEpRQUGBZs2apYKCAreLEnjUhT2oC3tQF3ahPuzhdF2EjFP3AwIAAPgcGSkAAIAMEUgBAABkiEAKAAAgQwRSAAAAGSKQSsFPf/pTDRo0SF26dNGIESP04osvul0k35s3b54+97nPqXv37urTp4+uuOIKvfXWW0nrGGM0e/ZslZSUqLCwUGPHjtXrr7/uUomDY968eQqFQpo+fXpiGXWRWzt27NANN9ygXr16qWvXrjrrrLO0du3axPvUR24cPXpU3//+9zVo0CAVFhbq5JNP1j333KPGxsbEOtSFM1544QVddtllKikpUSgU0m9/+9uk91PZ7vX19Zo6dap69+6tbt26aeLEiXr//ffTL4xBm6qrq01+fr555JFHzBtvvGGmTZtmunXrZrZu3ep20Xzt4osvNosWLTIbN240GzZsMJdeeqkZMGCA+fDDDxPrVFRUmO7du5vf/OY3pqamxlxzzTWmb9++pq6uzsWS+9vLL79sBg4caIYOHWqmTZuWWE5d5M6ePXtMLBYzN998s3nppZfM5s2bzYoVK8ymTZsS61AfufE///M/plevXub3v/+92bx5s/n1r39tTjjhBPPggw8m1qEunPGHP/zBzJw50/zmN78xksyyZcuS3k9lu5eVlZmTTjrJLF++3Kxbt86cf/75ZtiwYebo0aNplYVAqh1nn322KSsrS1p26qmnmvLycpdKFEy7du0ykszKlSuNMcY0NjaaaDRqKioqEuscOnTIRCIRU1VV5VYxfW3//v1m8ODBZvny5WbMmDGJQIq6yK0777zTjBo1qtX3qY/cufTSS81Xv/rVpGVXXnmlueGGG4wx1EWuHB9IpbLd9+3bZ/Lz8011dXVinR07dpi8vDzz7LPPpvX9dO214fDhw1q7dq3Gjx+ftHz8+PFavXq1S6UKpng8LumTh0dv3rxZtbW1SXVTUFCgMWPGUDcOue2223TppZdq3LhxScupi9x66qmnNHLkSF111VXq06ePhg8frkceeSTxPvWRO6NGjdJzzz2nt99+W5L06quvatWqVbrkkkskURduSWW7r127VkeOHElap6SkREOGDEm7bnhocRv+/e9/q6GhQcXFxUnLi4uLVVtb61KpgscYo9tvv12jRo3SkCFDJCmx/Vuqm61bt+a8jH5XXV2tdevW6ZVXXmn2HnWRW++9954qKyt1++2363vf+55efvllfetb31JBQYFuvPFG6iOH7rzzTsXjcZ166qkKh8NqaGjQnDlzNGnSJEkcG25JZbvX1taqc+fO6tmzZ7N10j2/E0ilIBQKJf3bGNNsGZwzZcoUvfbaa1q1alWz96gb523fvl3Tpk3Tn/70J3Xp0qXV9aiL3GhsbNTIkSM1d+5cSdLw4cP1+uuvq7KyUjfeeGNiPerDeUuWLNHixYv1xBNP6IwzztCGDRs0ffp0lZSU6KabbkqsR124I5Ptnknd0LXXht69eyscDjeLTnft2tUs0oUzpk6dqqeeekp/+ctf1K9fv8TyaDQqSdRNDqxdu1a7du3SiBEj1KlTJ3Xq1EkrV67UQw89pE6dOiW2N3WRG3379tXpp5+etOy0007Ttm3bJHFs5NJ3vvMdlZeX69prr9WZZ56pyZMn69vf/rbmzZsnibpwSyrbPRqN6vDhw9q7d2+r66SKQKoNnTt31ogRI7R8+fKk5cuXL1dpaalLpQoGY4ymTJmipUuX6s9//rMGDRqU9P6gQYMUjUaT6ubw4cNauXIldZNlF154oWpqarRhw4bEa+TIkbr++uu1YcMGnXzyydRFDp133nnNpgJ5++23FYvFJHFs5NKBAweUl5d8Gg2Hw4npD6gLd6Sy3UeMGKH8/PykdT744ANt3Lgx/brJaIh8gDRNf/Dzn//cvPHGG2b69OmmW7duZsuWLW4XzdduvfVWE4lEzPPPP28++OCDxOvAgQOJdSoqKkwkEjFLly41NTU1ZtKkSdxWnCPH3rVnDHWRSy+//LLp1KmTmTNnjnnnnXfM448/brp27WoWL16cWIf6yI2bbrrJnHTSSYnpD5YuXWp69+5tvvvd7ybWoS6csX//frN+/Xqzfv16I8nMnz/frF+/PjE1USrbvayszPTr18+sWLHCrFu3zlxwwQVMf+CUn/zkJyYWi5nOnTubz372s4lb8OEcSS2+Fi1alFinsbHRzJo1y0SjUVNQUGBGjx5tampq3Ct0gBwfSFEXufW73/3ODBkyxBQUFJhTTz3VLFy4MOl96iM36urqzLRp08yAAQNMly5dzMknn2xmzpxp6uvrE+tQF874y1/+0uI54qabbjLGpLbdDx48aKZMmWKKiopMYWGhmTBhgtm2bVvaZQkZY0zG+TMAAIAAY4wUAABAhgikAAAAMkQgBQAAkCECKQAAgAwRSAEAAGSIQAoAACBDBFIAAAAZIpACAADIEIEUAKvNnj1bZ511lmvf/4Mf/EDf+MY3HPv8Xbt26cQTT9SOHTsc+w4AzmFmcwCuCYVCbb5/0003acGCBaqvr1evXr1yVKpP/POf/9TgwYP12muvaeDAgY59z+233666ujr97Gc/c+w7ADiDQAqAa2praxP/v2TJEt1111166623EssKCwsViUTcKJokae7cuVq5cqX++Mc/Ovo9NTU1Ovvss7Vz50717NnT0e8CkF107QFwTTQaTbwikYhCoVCzZcd37d1888264oorNHfuXBUXF+tTn/qU7r77bh09elTf+c53VFRUpH79+un//u//kr5rx44duuaaa9SzZ0/16tVLl19+ubZs2dJm+aqrqzVx4sSkZWPHjtXUqVM1ffp09ezZU8XFxVq4cKE++ugjfeUrX1H37t316U9/Ws8880zib/bu3avrr79eJ554ogoLCzV48GAtWrQo8f6ZZ56paDSqZcuWZb4xAbiCQAqA5/z5z3/Wzp079cILL2j+/PmaPXu2JkyYoJ49e+qll15SWVmZysrKtH37dknSgQMHdP755+uEE07QCy+8oFWrVumEE07QF77wBR0+fLjF79i7d682btyokSNHNnvvscceU+/evfXyyy9r6tSpuvXWW3XVVVeptLRU69at08UXX6zJkyfrwIEDkj4eZ/XGG2/omWee0ZtvvqnKykr17t076TPPPvtsvfjii1neUgCcRiAFwHOKior00EMP6TOf+Yy++tWv6jOf+YwOHDig733vexo8eLBmzJihzp07669//aukjzNLeXl5+tnPfqYzzzxTp512mhYtWqRt27bp+eefb/E7tm7dKmOMSkpKmr03bNgwff/73098V2FhoXr37q1bbrlFgwcP1l133aXdu3frtddekyRt27ZNw4cP18iRIzVw4ECNGzdOl112WdJnnnTSSe1myADYp5PbBQCAdJ1xxhnKy/vkOrC4uFhDhgxJ/DscDqtXr17atWuXJGnt2rXatGmTunfvnvQ5hw4d0rvvvtvidxw8eFCS1KVLl2bvDR06tNl3nXnmmUnlkZT4/ltvvVX/9V//pXXr1mn8+PG64oorVFpamvSZhYWFiQwWAO8gkALgOfn5+Un/DoVCLS5rbGyUJDU2NmrEiBF6/PHHm33WiSee2OJ3NHW97d27t9k67X1/092ITd//xS9+UVu3btXTTz+tFStW6MILL9Rtt92mH/7wh4m/2bNnT6tlAWAvuvYA+N5nP/tZvfPOO+rTp49OOeWUpFdrdwV++tOfVo8ePfTGG29kpQwnnniibr75Zi1evFgPPvigFi5cmPT+xo0bNXz48Kx8F4DcIZAC4HvXX3+9evfurcsvv1wvvviiNm/erJUrV2ratGl6//33W/ybvLw8jRs3TqtWrerw999111168skntWnTJr3++uv6/e9/r9NOOy3x/oEDB7R27VqNHz++w98FILcIpAD4XteuXfXCCy9owIABuvLKK3Xaaafpq1/9qg4ePKgePXq0+nff+MY3VF1dneiiy1Tnzp01Y8YMDR06VKNHj1Y4HFZ1dXXi/SeffFIDBgzQf/7nf3boewDkHhNyAkArjDH6/Oc/r+nTp2vSpEmOfc/ZZ5+t6dOn67rrrnPsOwA4g4wUALQiFApp4cKFOnr0qGPfsWvXLn35y192NFAD4BwyUgAAABkiIwUAAJAhAikAAIAMEUgBAABkiEAKAAAgQwRSAAAAGSKQAgAAyBCBFAAAQIYIpAAAADJEIAUAAJCh/wdeTo2IQBpDrgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_network(spike_num=44)"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "jupytext": {
   "formats": "ipynb,auto:percent"
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  "kernelspec": {
   "display_name": "brainpy",
   "language": "python",
   "name": "brainpy"
  },
  "language_info": {
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   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
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   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
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   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
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   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
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   "toc_position": {},
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  "varInspector": {
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
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     "varRefreshCmd": "print(var_dic_list())"
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     "delete_cmd_postfix": ") ",
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}