neuron-group-simulation-with-DE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 25900,
     "status": "ok",
     "timestamp": 1604922499102,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "XA0b--qFfKpN",
    "outputId": "4ac7b084-0376-48dc-bd0a-9951b06a2d7b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting brian2\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9a/c7/565012c479953d041d5e828938515d3c402d0c3f4942180f1c44cb0f472e/Brian2-2.4.2.tar.gz (1.2MB)\n",
      "\u001b[K     |████████████████████████████████| 1.2MB 3.4MB/s \n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from brian2) (1.18.5)\n",
      "Requirement already satisfied: cython>=0.29 in /usr/local/lib/python3.6/dist-packages (from brian2) (0.29.21)\n",
      "Collecting sympy>=1.2\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e0/1f/8cbbf698e853019ac3dc5d60ca8f6be4ace4542b2f05f7b62949617fc98e/sympy-1.6.2-py3-none-any.whl (5.8MB)\n",
      "\u001b[K     |████████████████████████████████| 5.8MB 19.5MB/s \n",
      "\u001b[?25hRequirement already satisfied: pyparsing in /usr/local/lib/python3.6/dist-packages (from brian2) (2.4.7)\n",
      "Requirement already satisfied: jinja2>=2.7 in /usr/local/lib/python3.6/dist-packages (from brian2) (2.11.2)\n",
      "Requirement already satisfied: setuptools>=24.2 in /usr/local/lib/python3.6/dist-packages (from brian2) (50.3.2)\n",
      "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.6/dist-packages (from sympy>=1.2->brian2) (1.1.0)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2>=2.7->brian2) (1.1.1)\n",
      "Building wheels for collected packages: brian2\n",
      "  Building wheel for brian2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for brian2: filename=Brian2-2.4.2-cp36-cp36m-linux_x86_64.whl size=1257527 sha256=3bf6ec3330da4442a9769608cd2915b1af0f9fd991d27ef1588f4a652a453a4b\n",
      "  Stored in directory: /root/.cache/pip/wheels/5d/4b/cf/ef50e71df5479d67f981cbb17b1d842381019d27ba144d57de\n",
      "Successfully built brian2\n",
      "Installing collected packages: sympy, brian2\n",
      "  Found existing installation: sympy 1.1.1\n",
      "    Uninstalling sympy-1.1.1:\n",
      "      Successfully uninstalled sympy-1.1.1\n",
      "Successfully installed brian2-2.4.2 sympy-1.6.2\n"
     ]
    }
   ],
   "source": [
    "pip install brian2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 24919,
     "status": "ok",
     "timestamp": 1604922499104,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "yc4mkHHX2pUa"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 23848,
     "status": "ok",
     "timestamp": 1604922499105,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "THNQfjr1fbV4"
   },
   "outputs": [],
   "source": [
    "from brian2 import *\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "executionInfo": {
     "elapsed": 27771,
     "status": "ok",
     "timestamp": 1603596698369,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "CnOzYnkeoo2P",
    "outputId": "ebf9f277-8314-4656-e423-d97c53d37efe"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1.1\n",
    "start_scope()\n",
    "eqs='''\n",
    "a=exprel(x) : 1\n",
    "b=(exp(x)-1)/x : 1\n",
    "x : 1\n",
    "'''\n",
    "g=NeuronGroup(1000,eqs)\n",
    "g.x=np.linspace(-(1e-8),1e-8,1000)\n",
    "plot(g.x,g.a,'C0',label='exprel')\n",
    "plot(g.x,g.b,'C1--',label='(exp-1)/x')\n",
    "xlabel('x')\n",
    "legend()\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 740
    },
    "executionInfo": {
     "elapsed": 13887,
     "status": "ok",
     "timestamp": 1604922513023,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "4Srv8bx6feV-",
    "outputId": "8b6a5d97-600b-4d7e-b46c-2c9a3cc84549"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      " [py.warnings]\n",
      "WARNING    /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      " [py.warnings]\n",
      "WARNING    /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      " [py.warnings]\n",
      "WARNING    /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      " [py.warnings]\n",
      "WARNING    /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      " [py.warnings]\n",
      "WARNING    /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      " [py.warnings]\n",
      "WARNING    /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      " [py.warnings]\n",
      "WARNING    /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      " [py.warnings]\n",
      "WARNING    /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      " [py.warnings]\n",
      "WARNING    /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      " [py.warnings]\n",
      "WARNING    /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      " [py.warnings]\n",
      "WARNING    /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      " [py.warnings]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fab8f339898>"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1.2\n",
    "start_scope()\n",
    "C_m=200*pF\n",
    "E_l=-65*mV\n",
    "E_k=-90*mV\n",
    "E_Na=50*mV\n",
    "g_Na=20000/nohm\n",
    "g_K=6000/nohm\n",
    "g_l=10/nohm\n",
    "VT=-63*mV\n",
    "eqs='''\n",
    "dv/dt=(g_l*(E_l-v)-g_Na*m**3*h*(v-E_Na)-g_K*n**4*(v-E_k)+I_ext)/C_m : volt\n",
    "dn/dt=alpha_n*(1-n)-beta_n*n : 1\n",
    "dm/dt=alpha_m*(1-m)-beta_m*m : 1\n",
    "dh/dt=alpha_h*(1-h)-beta_h*h : 1\n",
    "alpha_n=0.16/exprel((15*mV-v+VT)/5/mV)/ms : hertz\n",
    "beta_n=0.5*exp((10*mV-v+VT)/40./mV)/ms : hertz\n",
    "alpha_m=1.28/exprel((13*mV-v+VT)/4./mV)/ms : hertz\n",
    "beta_m=1.4/exprel((v-40*mV-VT)/5/mV)/ms : hertz\n",
    "alpha_h=0.128*exp((17*mV-v+VT)/18./mV)/ms : hertz\n",
    "beta_h=4/(exp((40*mV-v+VT)/5/mV)+1)/ms : hertz\n",
    "'''\n",
    "params_names=['v','n','m','h']\n",
    "I_exts=['100pA','1nA','50nA','100nA']\n",
    "figure(figsize=(14,4))\n",
    "for I_idx,I_ext in enumerate([100*pA,1*nA,50*nA,100*nA]):\n",
    "  G=NeuronGroup(1,eqs,method='exponential_euler')\n",
    "  G.v=E_l\n",
    "  S=StateMonitor(G,('v','n','m','h'),(0,0,0,0))\n",
    "  run(100*ms)\n",
    "  for param_idx,param in enumerate([S.v,S.n,S.m,S.h]):\n",
    "    subplot(1,len(I_exts),param_idx+1)\n",
    "    plot(S.t/ms, param[0],label=I_exts[I_idx])\n",
    "    xlabel('Time (ms)')\n",
    "    title(params_names[param_idx])\n",
    "legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "executionInfo": {
     "elapsed": 15763,
     "status": "ok",
     "timestamp": 1604922578320,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "6Cnot4623An8",
    "outputId": "92831e72-d716-4cbe-ac6d-40fe24965d6b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    \"I_ext\" is an internal variable of group \"neurongroup_2\", but also exists in the run namespace with the value 100. * namp. The internal variable will be used. [brian2.groups.group.Group.resolve.resolution_conflict]\n"
     ]
    },
    {
     "data": {
      "image/png": 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+0MxbePbD4g+t1qwDs//A7B8Wj3NuX83iw671c5B1/cD2pUB/Necdmr+T2T0sHufcnjmw/UvAXas460ZgR7d9Oou3Zk5brXm7uVcCD9P9YvBE7zmLf9AVOpm/yGKrPwhc3a1dx+J3qAA/APwzsA/4NPCyVZz1J1j8juV/WLxq2bPKz+3Hga8C93UfO1dx1r8C9nQ5P3Gkv3hXQ96h2ZkVwZjn9k+7c/vZ7ty+chVnDYu33e4HPg9sXu1fB8C1wPXTeD//FxOS1Ljj9RmBJGlMFoEkNc4ikKTGWQSS1DiLQJIaZxFIUuMsAklq3P8Bg0fT9WuEa0gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1.3\n",
    "start_scope()\n",
    "eqs='''\n",
    "dv/dt=(g_l*(E_l-v)-g_Na*m**3*h*(v-E_Na)-g_K*n**4*(v-E_k)+I_ext)/C_m : volt\n",
    "dn/dt=alpha_n*(1-n)-beta_n*n : 1\n",
    "dm/dt=alpha_m*(1-m)-beta_m*m : 1\n",
    "dh/dt=alpha_h*(1-h)-beta_h*h : 1\n",
    "alpha_n=0.16/exprel((15*mV-v+VT)/5/mV)/ms : hertz\n",
    "beta_n=0.5*exp((10*mV-v+VT)/40./mV)/ms : hertz\n",
    "alpha_m=1.28/exprel((13*mV-v+VT)/4./mV)/ms : hertz\n",
    "beta_m=1.4/exprel((v-40*mV-VT)/5/mV)/ms : hertz\n",
    "alpha_h=0.128*exp((17*mV-v+VT)/18./mV)/ms : hertz\n",
    "beta_h=4/(exp((40*mV-v+VT)/5/mV)+1)/ms : hertz\n",
    "I_ext : amp\n",
    "'''\n",
    "G=NeuronGroup(100,eqs,threshold='v>-40*mV',refractory=5*ms,method='exponential_euler')\n",
    "G.I_ext='i*0.7*nA/99.'\n",
    "G.v=E_l\n",
    "M = SpikeMonitor(G)\n",
    "run(2000*ms)\n",
    "plot(G.I_ext/nA,M.count/2000/ms)\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "executionInfo": {
     "elapsed": 5835,
     "status": "ok",
     "timestamp": 1603603280616,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "mz0y_gE36AgA",
    "outputId": "b180b497-7c54-4400-91b6-55243d1c651d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    \"I_ext\" is an internal variable of group \"neurongroup_1\", but also exists in the run namespace with the value 1. * namp. The internal variable will be used. [brian2.groups.group.Group.resolve.resolution_conflict]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1.4\n",
    "start_scope()\n",
    "eqs='''\n",
    "dv/dt=(g_l*(E_l-v)-g_Na*m**3*h*(v-E_Na)-g_K*n**4*(v-E_k)+I_ext)/C_m : volt\n",
    "dn/dt=alpha_n*(1-n)-beta_n*n : 1\n",
    "dm/dt=alpha_m*(1-m)-beta_m*m : 1\n",
    "dh/dt=alpha_h*(1-h)-beta_h*h : 1\n",
    "alpha_n=0.16/exprel((15*mV-v+VT)/5/mV)/ms : hertz\n",
    "beta_n=0.5*exp((10*mV-v+VT)/40./mV)/ms : hertz\n",
    "alpha_m=1.28/exprel((13*mV-v+VT)/4./mV)/ms : hertz\n",
    "beta_m=1.4/exprel((v-40*mV-VT)/5/mV)/ms : hertz\n",
    "alpha_h=0.128*exp((17*mV-v+VT)/18./mV)/ms : hertz\n",
    "beta_h=4/(exp((40*mV-v+VT)/5/mV)+1)/ms : hertz\n",
    "I_ext : amp\n",
    "'''\n",
    "G=NeuronGroup(1,eqs,method='exponential_euler')\n",
    "G.I_ext=1*nA\n",
    "G.v=E_l\n",
    "G.run_regularly('I_ext += 1*nA',dt=1*ms)\n",
    "M = StateMonitor(G,('v'),record=0)\n",
    "run(100*ms)\n",
    "plot(M.t/ms,M.v[0]/mV)\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "executionInfo": {
     "elapsed": 1988,
     "status": "ok",
     "timestamp": 1603606023061,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "-fs4iq5DHgiV",
    "outputId": "5d4ecb31-a57d-4489-9445-8032fe34f58d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1.5\n",
    "start_scope()\n",
    "eqs='''\n",
    "dv/dt=(g_l*(E_l-v)-g_Na*m**3*h*(v-E_Na)-g_K*n**4*(v-E_k)+I_ext)/C_m : volt\n",
    "dn/dt=alpha_n*(1-n)-beta_n*n : 1\n",
    "dm/dt=alpha_m*(1-m)-beta_m*m : 1\n",
    "dh/dt=alpha_h*(1-h)-beta_h*h : 1\n",
    "alpha_n=0.16/exprel((15*mV-v+VT)/5/mV)/ms : hertz\n",
    "beta_n=0.5*exp((10*mV-v+VT)/40./mV)/ms : hertz\n",
    "alpha_m=1.28/exprel((13*mV-v+VT)/4./mV)/ms : hertz\n",
    "beta_m=1.4/exprel((v-40*mV-VT)/5/mV)/ms : hertz\n",
    "alpha_h=0.128*exp((17*mV-v+VT)/18./mV)/ms : hertz\n",
    "beta_h=4/(exp((40*mV-v+VT)/5/mV)+1)/ms : hertz\n",
    "I_ext : amp\n",
    "'''\n",
    "G=NeuronGroup(4,eqs,method='exponential_euler')\n",
    "G.v=E_l\n",
    "G.I_ext[0]=100*pA\n",
    "G.I_ext[1]=1*nA\n",
    "G.I_ext[2]=50*nA\n",
    "G.I_ext[3]=100*nA\n",
    "M=StateMonitor(G,('v','n'),record=True)\n",
    "run(50*ms)\n",
    "figure(figsize=(20,4))\n",
    "for idx,I in enumerate(['100pA','1nA','50nA','100nA']):\n",
    "  subplot(1,4,idx+1)\n",
    "  xlabel('v')\n",
    "  ylabel('n')\n",
    "  title(I)\n",
    "  plot(M.v[idx],M.n[idx])\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 242
    },
    "executionInfo": {
     "elapsed": 17808,
     "status": "ok",
     "timestamp": 1604137733298,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "u_3hglcznc93",
    "outputId": "6f0d8d6c-690c-4124-c2de-8dd70563151c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1.6\n",
    "start_scope()\n",
    "from draw_utils import phaseplane_animation\n",
    "I_ext=200*pA\n",
    "C_m=200*pF\n",
    "E_l=-65*mV\n",
    "E_k=-90*mV\n",
    "E_Na=50*mV\n",
    "g_Na=20000/nohm\n",
    "g_K=6000/nohm\n",
    "g_l=10/nohm\n",
    "VT=-63*mV\n",
    "eqs='''\n",
    "dv/dt=(g_l*(E_l-v)-g_Na*m**3*h*(v-E_Na)-g_K*n**4*(v-E_k)+I_ext)/C_m : volt\n",
    "dn/dt=alpha_n*(1-n)-beta_n*n : 1\n",
    "dm/dt=alpha_m*(1-m)-beta_m*m : 1\n",
    "dh/dt=alpha_h*(1-h)-beta_h*h : 1\n",
    "alpha_n=0.16/exprel((15*mV-v+VT)/5/mV)/ms : hertz\n",
    "beta_n=0.5*exp((10*mV-v+VT)/40./mV)/ms : hertz\n",
    "alpha_m=1.28/exprel((13*mV-v+VT)/4./mV)/ms : hertz\n",
    "beta_m=1.4/exprel((v-40*mV-VT)/5/mV)/ms : hertz\n",
    "alpha_h=0.128*exp((17*mV-v+VT)/18./mV)/ms : hertz\n",
    "beta_h=4/(exp((40*mV-v+VT)/5/mV)+1)/ms : hertz\n",
    "'''\n",
    "G=NeuronGroup(1,eqs,method='exponential_euler')\n",
    "G.v=E_l\n",
    "M=StateMonitor(G,('v','n'),record=0)\n",
    "run(100*ms)\n",
    "phaseplane_animation(M.t/ms,M.v[0]/mV,M.n[0])\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 567
    },
    "executionInfo": {
     "elapsed": 2620,
     "status": "ok",
     "timestamp": 1604143348089,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "7nGrC452D6eM",
    "outputId": "7d32cbd2-2f5b-4ca8-b9d9-5ad5fc68c37e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2.1\n",
    "E_l=-60*mV\n",
    "E_e=0*mV\n",
    "E_i=-80*mV\n",
    "g_l=9.99*nS\n",
    "C_m=198*pF\n",
    "tau_e=5*ms\n",
    "tau_i=10*ms\n",
    "I_ex=150*pA\n",
    "eqs='''\n",
    "dv/dt=(g_l*(E_l-v)+g_e*(E_e-v)+g_i*(E_i-v)+I_ex)/C_m : volt\n",
    "dg_e/dt=-g_e/tau_e : siemens\n",
    "dg_i/dt=-g_i/tau_i : siemens\n",
    "'''\n",
    "figure(figsize=(9,9))\n",
    "for i,tspan in enumerate([100*ms,200*ms,300*ms]):\n",
    "  start_scope()\n",
    "  G=NeuronGroup(1,eqs,method='exponential_euler')\n",
    "  G.v=0\n",
    "  G.g_e=0\n",
    "  G.g_i=0\n",
    "  M=StateMonitor(G,('v','g_e','g_i'),record=True)\n",
    "  run(tspan)\n",
    "  subplot(3,3,3*i+1)\n",
    "  plot(M.t/ms,M.v[0]/mV)\n",
    "  ylabel(str(tspan))\n",
    "  if i==0:\n",
    "    title('v')\n",
    "  elif i==2:\n",
    "    xlabel('time')\n",
    "  subplot(3,3,3*i+2)\n",
    "  plot(M.t/ms,M.g_e[0]/nS)\n",
    "  if i==0:\n",
    "    title('g_e')\n",
    "  elif i==2:\n",
    "    xlabel('time')\n",
    "  subplot(3,3,3*i+3)\n",
    "  plot(M.t/ms,M.g_i[0]/nS)\n",
    "  if i==0:\n",
    "    title('g_i')\n",
    "  elif i==2:\n",
    "    xlabel('time')\n",
    "show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "executionInfo": {
     "elapsed": 11141,
     "status": "ok",
     "timestamp": 1604145151404,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "JayIL-MtW_Yu",
    "outputId": "2e12398e-b81d-4435-c256-5b7c947f8bd9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    \"I_ex\" is an internal variable of group \"neurongroup\", but also exists in the run namespace with the value 150. * pamp. The internal variable will be used. [brian2.groups.group.Group.resolve.resolution_conflict]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2.2\n",
    "start_scope()\n",
    "E_l=-60*mV\n",
    "E_e=0*mV\n",
    "E_i=-80*mV\n",
    "g_l=9.99*nS\n",
    "C_m=198*pF\n",
    "tau_e=5*ms\n",
    "tau_i=10*ms\n",
    "eqs='''\n",
    "dv/dt=(g_l*(E_l-v)+g_e*(E_e-v)+g_i*(E_i-v)+I_ex)/C_m : volt\n",
    "dg_e/dt=-g_e/tau_e : siemens\n",
    "dg_i/dt=-g_i/tau_i : siemens\n",
    "I_ex : amp\n",
    "'''\n",
    "G=NeuronGroup(20,eqs,method='exponential_euler',threshold='v>-50*mV',refractory='5*ms')\n",
    "G.v=E_l\n",
    "G.I_ex=np.linspace(0,1,20)*nA\n",
    "S=SpikeMonitor(G)\n",
    "run(200*ms)\n",
    "plot(G.I_ex/nA,S.count/200/ms)\n",
    "xlabel('I_ex')\n",
    "ylabel('freq')\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KnlEQw6LgPOf"
   },
   "outputs": [],
   "source": [
    "#3\n",
    "start_scope()\n",
    "eqs='''\n",
    "dv/dt=v-v**3-w+I : hertz\n",
    "dw/dt=0.08*(v+0.7-0.8*w) : hertz\n",
    "I : hertz\n",
    "'''\n",
    "G=NeuronGroup(5,eqs,method='euler')\n",
    "G.I=np.array([0.,0.3,0.4,0.8,1.5])*hertz\n",
    "G.v=-2*hertz\n",
    "G.w=-0.5*hertz\n",
    "M=StateMonitor(G,('v','w','I'),record=True)\n",
    "run(200*ms)\n"
   ]
  }
 ],
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