excitatory-and-inhibitory-neuron-groups.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 26417,
     "status": "ok",
     "timestamp": 1607194994806,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "FxX108GrUz1v",
    "outputId": "70c5b36d-ae19-4ae8-d0c7-1f8de9521ee0"
   },
   "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 5.5MB/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/2f/88/0769eecd7520210e2555d984793e9aa121fdc7b5fd22e9602282726a7818/sympy-1.7-py3-none-any.whl (5.9MB)\n",
      "\u001b[K     |████████████████████████████████| 5.9MB 19.4MB/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=1257517 sha256=871d6f20b1fc44ee71131ec81812fa1ca34d6970c6e62cb22ebe36e5b80b8951\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-1.7\n"
     ]
    }
   ],
   "source": [
    "pip install brian2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 24881,
     "status": "ok",
     "timestamp": 1607194996229,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "wM0ED7O2U2ba"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from brian2 import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LwIgEVtckK5C"
   },
   "source": [
    "#1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w71UGSGeBl2g"
   },
   "source": [
    "##1.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WtnR_ZSbEWTE"
   },
   "source": [
    "![Screenshot from 2020-12-01 10-20-30.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QArhN4k9EyQu"
   },
   "source": [
    "در لحظه رخ دادن اسپایک \n",
    "\n",
    "V\n",
    "\n",
    "را به ولتاژ نشتی بازنشانی می کنیم.\n",
    "\n",
    "همچنین\n",
    "\n",
    "w\n",
    "\n",
    "را به اندازه \n",
    "\n",
    "b\n",
    "\n",
    "افزایش می دهیم"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DwsHrlNBkKeV"
   },
   "outputs": [],
   "source": [
    "C=281*pF #membrane capacity.\n",
    "g_L=30*nS#leak conductance\n",
    "E_L=-70.6*mV#resting potential\n",
    "V_T=-50.4*mV#spike threshold\n",
    "delta_T=2*mV#determines smoothness of dV exponential part\n",
    "tau_w=144*ms#adaptaion variable (w) time constant.w itself helps v to go back to its resting voltage where-ever it is.this variable helps \n",
    "            #modelling different classes of neurons.\n",
    "a=4*nS#determines level of adaptaion when V is in subthreshold state\n",
    "b=0.0805*nA #helps modelling some characteristics related to spikes in certain neuron classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 276
    },
    "executionInfo": {
     "elapsed": 2926,
     "status": "ok",
     "timestamp": 1606846982130,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "KUjag3n9T1BL",
    "outputId": "803a66ad-14fb-4d06-ef16-2e00a353ba89"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_scope()\n",
    "eqs='''\n",
    "dV/dt=(-g_L*(V-E_L)+g_L*delta_T*exp((V-V_T)/delta_T)-w+I)/C : volt\n",
    "dw/dt=(a*(V-E_L)-w)/tau_w : amp\n",
    "I=(floor((sign(t/ms-50)+1)/2)-floor((sign(t/ms-250)+1)/2))*mA : amp\n",
    "'''\n",
    "G=NeuronGroup(1,eqs,threshold='V>(V_T+5*delta_T)',reset='V=E_L;w=w+b',method='exponential_euler')\n",
    "G.V=E_L\n",
    "G.w=0*mA\n",
    "M = StateMonitor(G,('V','I','w'),record=0)\n",
    "run(300*ms)\n",
    "subplot(131)\n",
    "plot(M.t/ms,M.V[0]/mV)\n",
    "ylabel('V')\n",
    "subplot(132)\n",
    "plot(M.t/ms,M.I[0]/mA)\n",
    "ylabel('I')\n",
    "subplot(133)\n",
    "plot(M.t/ms,M.w[0]/mA)\n",
    "ylabel('w')\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w33WtdZThtyn"
   },
   "source": [
    "##1.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1129,
     "status": "ok",
     "timestamp": 1606848086421,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "RnvuDPDwh2eK",
    "outputId": "09f7a281-ac7b-4299-d56f-70b81df44a84"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 50.1  50.2  50.3 ... 299.7 299.8 299.9]\n"
     ]
    }
   ],
   "source": [
    "start_scope()\n",
    "eqs='''\n",
    "dV/dt=(-g_L*(V-E_L)+g_L*delta_T*exp((V-V_T)/delta_T)-w+I)/C : volt\n",
    "dw/dt=(a*(V-E_L)-w)/tau_w : amp\n",
    "I=(floor((sign(t/ms-50)+1)/2)-floor((sign(t/ms-250)+1)/2))*mA : amp\n",
    "'''\n",
    "G=NeuronGroup(1,eqs,threshold='V>(V_T+5*delta_T)',reset='V=20*mV;w=w+b',method='exponential_euler')\n",
    "G.V=E_L\n",
    "G.w=0*mA\n",
    "S=SpikeMonitor(G)\n",
    "run(300*ms)\n",
    "print(S.t/ms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3V9_2SWBpWKC"
   },
   "source": [
    "##1.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 2066,
     "status": "ok",
     "timestamp": 1606849211102,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "SL4gr0RdnCot",
    "outputId": "0d2c83f4-13e3-4643-9151-f13ced0699b3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_scope()\n",
    "eqs='''\n",
    "dV/dt=(-g_L*(V-E_L)+g_L*delta_T*exp((V-V_T)/delta_T)-w+I)/C : volt\n",
    "dw/dt=(a*(V-E_L)-w)/tau_w : amp\n",
    "I=((floor((sign(t/ms-10)+1)/2)-floor((sign(t/ms-200)+1)/2))*0.5+floor((sign(t/ms-500)+1)/2)-floor((sign(t/ms-1000)+1)/2))*mA : amp\n",
    "'''\n",
    "G=NeuronGroup(1,eqs,threshold='V>(V_T+5*delta_T)',reset='V=E_L;w=w+b',method='exponential_euler')\n",
    "G.V=E_L\n",
    "G.w=0*mA\n",
    "M = StateMonitor(G,('V','I','w'),record=0)\n",
    "run(1000*ms)\n",
    "subplot(131)\n",
    "plot(M.t/ms,M.V[0]/mV)\n",
    "ylabel('V')\n",
    "subplot(132)\n",
    "plot(M.t/ms,M.I[0]/mA)\n",
    "ylabel('I')\n",
    "subplot(133)\n",
    "plot(M.t/ms,M.w[0]/mA)\n",
    "ylabel('w')\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WayMElQ9rT_I"
   },
   "source": [
    "##1.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zW10tDwWuh7F"
   },
   "source": [
    "Answer:Due to subthreshold adaptation induced by w "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "executionInfo": {
     "elapsed": 4149,
     "status": "ok",
     "timestamp": 1606850007558,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "VlY4NwxnrVN9",
    "outputId": "65331495-885c-44fe-958f-31944009c4e4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "############\n",
    "tau_w=72*ms#\n",
    "b=161*pA   # change parameters. \n",
    "###########\n",
    "\n",
    "start_scope()\n",
    "eqs='''\n",
    "dV/dt=(-g_L*(V-E_L)+g_L*delta_T*exp((V-V_T)/delta_T)-w+I)/C : volt\n",
    "dw/dt=(a*(V-E_L)-w)/tau_w : amp\n",
    "I=((floor((sign(t/ms-10)+1)/2)-floor((sign(t/ms-200)+1)/2))*0.5+floor((sign(t/ms-500)+1)/2)-floor((sign(t/ms-1000)+1)/2))*mA : amp\n",
    "'''\n",
    "G=NeuronGroup(1,eqs,threshold='V>(V_T+5*delta_T)',reset='V=V_T+3*mV;w=w+b',method='exponential_euler')\n",
    "G.V=E_L\n",
    "G.w=0*mA\n",
    "M = StateMonitor(G,('V','I','w'),record=0)\n",
    "run(1000*ms)\n",
    "subplot(131)\n",
    "plot(M.t/ms,M.V[0]/mV)\n",
    "ylabel('V')\n",
    "subplot(132)\n",
    "plot(M.t/ms,M.I[0]/mA)\n",
    "ylabel('I')\n",
    "subplot(133)\n",
    "plot(M.t/ms,M.w[0]/mA)\n",
    "ylabel('w')\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aBwE80W7vGKB"
   },
   "source": [
    "##1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 3902,
     "status": "ok",
     "timestamp": 1606850299341,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "o4BzavcuvFrT",
    "outputId": "b7d670cc-35af-47a5-fc6e-105964960485"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "C=281*pF\n",
    "g_L=30*nS\n",
    "E_L=-70.6*mV\n",
    "V_T=-50.4*mV+3*mV\n",
    "delta_T=2*mV\n",
    "tau_w=144*ms\n",
    "a=4*nS\n",
    "b=0.0805*nA\n",
    "start_scope()\n",
    "eqs='''\n",
    "dV/dt=(-g_L*(V-E_L)+g_L*delta_T*exp((V-V_T)/delta_T)-w+I)/C : volt\n",
    "dw/dt=(a*(V-E_L)-w)/tau_w : amp\n",
    "I=((floor((sign(t/ms-10)+1)/2)-floor((sign(t/ms-200)+1)/2))*0.5+floor((sign(t/ms-500)+1)/2)-floor((sign(t/ms-1000)+1)/2))*mA : amp\n",
    "'''\n",
    "G=NeuronGroup(1,eqs,threshold='V>(V_T+5*delta_T)',reset='V=V_T;w=w+b',method='exponential_euler')\n",
    "G.V=E_L\n",
    "G.w=0*mA\n",
    "M = StateMonitor(G,('V','I','w'),record=0)\n",
    "run(1000*ms)\n",
    "subplot(131)\n",
    "plot(M.t/ms,M.V[0]/mV)\n",
    "ylabel('V')\n",
    "subplot(132)\n",
    "plot(M.t/ms,M.I[0]/mA)\n",
    "ylabel('I')\n",
    "subplot(133)\n",
    "plot(M.t/ms,M.w[0]/mA)\n",
    "ylabel('w')\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KUxTQsHc3m4I"
   },
   "source": [
    "#2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7RMyKxIx-8zH"
   },
   "outputs": [],
   "source": [
    "dt=0.01\n",
    "Duration=20000\n",
    "Cm=1\n",
    "gNa=120\n",
    "gK=36\n",
    "gl=0.3\n",
    "ENa=55\n",
    "EK=-72\n",
    "El=-49.4\n",
    "vr=-60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "y3-TiBUt3oAE"
   },
   "outputs": [],
   "source": [
    "t=0\n",
    "Iext=0\n",
    "n=0\n",
    "m=0\n",
    "h=0\n",
    "v=vr\n",
    "t_vec=[]\n",
    "v_vec=[]\n",
    "n_vec=[]\n",
    "m_vec=[]\n",
    "h_vec=[]\n",
    "tau_n_vec=[]\n",
    "n_inf_vec=[]\n",
    "tau_m_vec=[]\n",
    "m_inf_vec=[]\n",
    "tau_h_vec=[]\n",
    "h_inf_vec=[]\n",
    "while t<=Duration:\n",
    "  if t<5000:\n",
    "    Iext=2\n",
    "  else:\n",
    "    Iext=0\n",
    "  t_vec+=[t]\n",
    "  dv=(gl*(El-v)-gNa*m**3*h*(v-ENa)-gK*n**4*(v-EK)+Iext)/Cm*dt\n",
    "  v_vec+=[v]\n",
    "  ############\n",
    "  u=vr-v\n",
    "  alpha_n=(.1*u+1)/(exp(1+.1*u)-1)/10\n",
    "  beta_n=.125*exp(u/80)\n",
    "  tau_n=1/(alpha_n+beta_n)\n",
    "  tau_n_vec+=[tau_n]\n",
    "  n_inf=alpha_n/(alpha_n+beta_n)\n",
    "  n_inf_vec+=[n_inf]\n",
    "  #\n",
    "  alpha_m=(u+25)/(exp(2.5+.1*u)-1)/10\n",
    "  beta_m=4*exp(u/18)\n",
    "  tau_m=1/(alpha_m+beta_m)\n",
    "  tau_m_vec+=[tau_m]\n",
    "  m_inf=alpha_m/(alpha_m+beta_m)\n",
    "  m_inf_vec+=[m_inf]\n",
    "  #\n",
    "  alpha_h=.07*exp(u/20)\n",
    "  beta_h=1/(1+exp(3+.1*u))\n",
    "  tau_h=1/(alpha_h+beta_h)\n",
    "  tau_h_vec+=[tau_h]\n",
    "  h_inf=alpha_h/(alpha_h+beta_h)\n",
    "  h_inf_vec+=[h_inf]\n",
    "  ############\n",
    "  dn=(-n+n_inf)/tau_n*dt\n",
    "  n_vec+=[n]\n",
    "  dm=(-m+m_inf)/tau_m*dt\n",
    "  m_vec+=[m]\n",
    "  dh=(-h+h_inf)/tau_h*dt\n",
    "  h_vec+=[h]\n",
    "  ######\n",
    "  v+=dv\n",
    "  n+=dn\n",
    "  m+=dm\n",
    "  h+=dh\n",
    "  t+=dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zqlFuKcfP6r3"
   },
   "source": [
    "##2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 541
    },
    "executionInfo": {
     "elapsed": 7258,
     "status": "ok",
     "timestamp": 1606906838078,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "Mr8QjVF_DPCo",
    "outputId": "8da80404-d4bf-4718-a908-aa642b5916f4"
   },
   "outputs": [
    {
     "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"
    },
    {
     "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": [
    "plot(t_vec,tau_n_vec)\n",
    "plot(t_vec,tau_m_vec)\n",
    "plot(t_vec,tau_h_vec)\n",
    "xlabel('t')\n",
    "legend(['tau_n','tau_m','tau_h'],loc='upper right')\n",
    "show()\n",
    "plot(t_vec,n_inf_vec)\n",
    "plot(t_vec,m_inf_vec)\n",
    "plot(t_vec,h_inf_vec)\n",
    "xlabel('t')\n",
    "legend(['n_inf','m_inf','h_inf'],loc='upper right')\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B3Zk8hFsGuzV"
   },
   "source": [
    "##2.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 2058,
     "status": "ok",
     "timestamp": 1606906845502,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "2e2_9gMAGtbR",
    "outputId": "4ddd2381-97be-4e5c-e920-d7e1a94d60da"
   },
   "outputs": [
    {
     "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": [
    "plot(t_vec,v_vec)\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DmCFun_hHB92"
   },
   "source": [
    "##2.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "executionInfo": {
     "elapsed": 85173,
     "status": "ok",
     "timestamp": 1606907618432,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "4eUnxhxzHDoB",
    "outputId": "84bad7f1-eb3f-4d69-a33f-cf12ee5cd8e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first spike at Iext=0.0\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": [
    "t=0\n",
    "Iext=0\n",
    "n=0\n",
    "m=0\n",
    "h=0\n",
    "v=vr\n",
    "v_vec=[]\n",
    "t_vec=[]\n",
    "counter=0\n",
    "spike1=False\n",
    "while t<=Duration:\n",
    "  t_vec+=[t]\n",
    "  if counter>=500:\n",
    "    Iext+=0.00\n",
    "    counter=0\n",
    "  else:\n",
    "    counter+=1\n",
    "  dv=(gl*(El-v)-gNa*m**3*h*(v-ENa)-gK*n**4*(v-EK)+Iext)/Cm*dt\n",
    "  v_vec+=[v]\n",
    "  ############\n",
    "  u=vr-v\n",
    "  alpha_n=(.1*u+1)/(exp(1+.1*u)-1)/10\n",
    "  beta_n=.125*exp(u/80)\n",
    "  tau_n=1/(alpha_n+beta_n)\n",
    "  n_inf=alpha_n/(alpha_n+beta_n)\n",
    "  #\n",
    "  alpha_m=(u+25)/(exp(2.5+.1*u)-1)/10\n",
    "  beta_m=4*exp(u/18)\n",
    "  tau_m=1/(alpha_m+beta_m)\n",
    "  m_inf=alpha_m/(alpha_m+beta_m)\n",
    "  #\n",
    "  alpha_h=.07*exp(u/20)\n",
    "  beta_h=1/(1+exp(3+.1*u))\n",
    "  tau_h=1/(alpha_h+beta_h)\n",
    "  h_inf=alpha_h/(alpha_h+beta_h)\n",
    "  ############\n",
    "  dn=(-n+n_inf)/tau_n*dt\n",
    "  dm=(-m+m_inf)/tau_m*dt\n",
    "  dh=(-h+h_inf)/tau_h*dt\n",
    "  ######\n",
    "  v+=dv\n",
    "  if v>20 and not spike1:\n",
    "    print('first spike at Iext={}'.format(Iext))\n",
    "    spike1=True\n",
    "  n+=dn\n",
    "  m+=dm\n",
    "  h+=dh\n",
    "  t+=dt\n",
    "plot(t_vec,v_vec)\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JJNu_82kQCv7"
   },
   "source": [
    "##2.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "executionInfo": {
     "elapsed": 89332,
     "status": "ok",
     "timestamp": 1606908418060,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "Pdb8riqqKiXQ",
    "outputId": "1ccd9854-e6c9-4b8a-f278-e38841752ed6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first spike at t=5.469999999999928\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": [
    "t=0\n",
    "Iext=0\n",
    "n=0\n",
    "m=0\n",
    "h=0\n",
    "v=vr\n",
    "v_vec=[]\n",
    "t_vec=[]\n",
    "counter=0\n",
    "spike1=False\n",
    "while t<=Duration:\n",
    "  t_vec+=[t]\n",
    "  dv=(gl*(El-v)-gNa*m**3*h*(v-ENa)-gK*n**4*(v-EK)+Iext)/Cm*dt\n",
    "  v_vec+=[v]\n",
    "  ############\n",
    "  u=vr-v\n",
    "  alpha_n=(.1*u+1)/(exp(1+.1*u)-1)/10\n",
    "  beta_n=.125*exp(u/80)\n",
    "  tau_n=1/(alpha_n+beta_n)\n",
    "  n_inf=alpha_n/(alpha_n+beta_n)\n",
    "  #\n",
    "  alpha_m=(u+25)/(exp(2.5+.1*u)-1)/10\n",
    "  beta_m=4*exp(u/18)\n",
    "  tau_m=1/(alpha_m+beta_m)\n",
    "  m_inf=alpha_m/(alpha_m+beta_m)\n",
    "  #\n",
    "  alpha_h=.07*exp(u/20)\n",
    "  beta_h=1/(1+exp(3+.1*u))\n",
    "  tau_h=1/(alpha_h+beta_h)\n",
    "  h_inf=alpha_h/(alpha_h+beta_h)\n",
    "  ############\n",
    "  dn=(-n+n_inf)/tau_n*dt\n",
    "  dm=(-m+m_inf)/tau_m*dt\n",
    "  dh=(-h+h_inf)/tau_h*dt\n",
    "  ######\n",
    "  v+=dv\n",
    "  if v>20 and not spike1:\n",
    "    print('first spike at t={}'.format(t))\n",
    "    spike1=True\n",
    "  n+=dn\n",
    "  m+=dm\n",
    "  h+=dh\n",
    "  t+=dt\n",
    "plot(t_vec,v_vec)\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hvCklsM3M5lS"
   },
   "source": [
    "##2.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Gm4IxQ7TOU8F"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lbaA14v2PfQ1"
   },
   "source": [
    "##2.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 2793,
     "status": "ok",
     "timestamp": 1607194419910,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "LmcEalDVPhVG"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jeZZn-ukPhwg"
   },
   "source": [
    "#3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 1674,
     "status": "ok",
     "timestamp": 1607195957977,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "H6ZIpkwKS1R-"
   },
   "outputs": [],
   "source": [
    "Tm=20*ms\n",
    "El=-70*mV\n",
    "IeRm=25*mV\n",
    "Vt=-54*mV\n",
    "rm=10*Mohm\n",
    "taue=10*ms\n",
    "Delay=5*ms\n",
    "eqs_inh='''\n",
    "dv/dt= (IeRm -(v-El)-gs*(v-0*mV)*rm)/Tm : volt\n",
    "dgs/dt = (-1*gs)/(taue) : siemens\n",
    "'''\n",
    "eqs_exc='''\n",
    "dv/dt = (IeRm-(v-El)-gs*(v+80*mV)*rm)/Tm : volt\n",
    "dgs/dt = (-1*gs)/(taue) : siemens\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I2D7O2FES1qk"
   },
   "source": [
    "##3.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "executionInfo": {
     "elapsed": 42340,
     "status": "ok",
     "timestamp": 1607197751544,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "qsRwVWsTQJDP"
   },
   "outputs": [],
   "source": [
    "start_scope()\n",
    "G_E=NeuronGroup(1000,eqs_exc,threshold='v > Vt',method='exponential_euler')\n",
    "G_I=NeuronGroup(1000,eqs_inh,threshold='v > Vt',method='exponential_euler')\n",
    "G_E.v=El\n",
    "G_I.v=El\n",
    "S_exc2inh=Synapses(G_E,G_I,on_pre='gs_post+=5*nsiemens',delay=Delay)\n",
    "S_exc2inh.connect(p=0.5)\n",
    "S_exc2exc=Synapses(G_E,G_E,on_pre='gs_post+=5*nsiemens',delay=Delay)\n",
    "S_exc2exc.connect(p=0.5)\n",
    "S_inh2exc=Synapses(G_I,G_E,on_pre='gs_post+=5*nsiemens',delay=Delay)\n",
    "S_inh2exc.connect(p=0.5)\n",
    "S_inh2inh=Synapses(G_I,G_I,on_pre='gs_post+=5*nsiemens',delay=Delay)\n",
    "S_inh2inh.connect(p=0.5)\n",
    "M_E=StateMonitor(G_E,'v',record=True)\n",
    "M_I=StateMonitor(G_I,'v',record=True)\n",
    "run(1000*ms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sv7_-zwDeWT2"
   },
   "source": [
    "نورون های مهاری می خواهند به سمت  ولتاژ استراحت بروند \n",
    "اما در اثر فعالیت های نورون های همسایه (چه مهاری و چه تحریکی) و افزایش نمایی رسانایی  به سمت ولتاژ صفر می روند\n",
    "نورون های تحریکی هم به همین صورت اما به سمت ولتاژ منفی هشتاد می روند.\n",
    "بخاطر نقصان معادلات اثر نورون های مهاری بر مهاری بصورت تحریکی و اثر نورون های تحریکی بر تحریکی بصورت مهاری ظاهر می شود.\n",
    "بالا رفتن اولیه ولتاژ در هر دو گروه بخاطر اثر جریان خارجی است"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "executionInfo": {
     "elapsed": 1509,
     "status": "ok",
     "timestamp": 1607197756447,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "1yZdZcUJV_ZH",
    "outputId": "a28897e3-e31f-4589-b425-ac4803e47bfa"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MpRYR9SBWR8/d3a919+PcfbC75+DKtv2kmoNIcDp1SpRVZ82axejRo/nc5z7HCSecwCWXXELyhcP33HMPQ4cOZfDgwSxatAiAhx56aI8rpKurq6msrKRfv3489dRTjcc955xzqKur47777uPHP/4xQ4YM4fnnn6euro7TTjuNiooKxowZw7vvJmb7T5w4kauvvpoRI0Zw8803U1ZWxq7JOA0NDRx//PG05OScTKnnPWAeMDb6ussGoPmLhbcFqjmIxOsPt8A/X2/ZY/YcDGdNyXr3V155hQULFtCrVy8+8YlPMHv2bD75ycSCEd27d+cf//gH9957L3fddRcPPPBAyvfX1dUxd+5c3n77bU499VRqa2sbt5WWlnL11VfTqVMnbrrpJgDOPfdcJkyYwIQJE5g2bRrXX389Tz75JAD19fW8+OKLFBQU0LlzZ6ZPn84NN9xAdXU1J554Ij169GjOT2YP++w5uPur7v4QiXtI/zLp8Tt3/7DFImj11HMQCdnw4cMpLi6mXbt2DBkyZI+1jc4//3wATj755IxrHrVr146ysjKOPfbYxh7Gvrz00ktcfPHFAHzxi1/khRdeaNz2+c9/noKCAgC+9KUv8fDDDwOJ+0O01Gqsu+yz52Bmr7P7vtEp2929okUjaZXUcxCJ1X78hZ8rHTp0aHxeUFDQuPpq8ra925Pt/fmZ7vM0W8lLdffp04ePfexjPPfcc8ydO5fp06cf8HHTyTRb6Rzg3AwPERFpwuOPP05DQwNvv/0277zzDv37999j++GHH86GDRsaX59yyik88khiEezp06fvc5luSKzUeumll+7Ro2gpmYaVlu16kFiBdXD02By1HfzcVZAWkWYpKSlh+PDhnHXWWdx3330UFRXtsf3cc89lxowZjQXpe+65hwcffJCKigp+9atf8dOf/nSfxx47diwbN25s8SElyG7J7guBO4FZJAbgRwFfd/fUybsxydmS3X/6PzC3Cr75QcsfW0TS0pLd2aupqeHGG2/k+eefb3Lf/V2yO5uJsrcBw9x9ZXSwHkA10GqSQ26p5yAirc+UKVOYOnVqi9cadsnmCul2uxJDZE2W33cQUEFaRFqnW265hWXLljVOq21p2fQc/mBmzwC/jV5/gcSVzGFQzUFEApRNclhJ4v4NQ6LXVe4+I3chtSK6CE4kFu7erCmfsqemasvpZDM8dBhwCzAcWAq0vhWickq/oCL5VFRUxJo1aw7oA01SuTtr1qxJmSXVlCZ7Du7+XeC7ZlZBYkjpr2ZWv68luUVEmqO4uJj6+voWXScodEVFRRQXF+/X9+zPsn4rgX+SKEgftV/v0papayuSV4WFhfTt2zfuMILX5LCSmV1jZrOAZ4FuwJVhLJ2Bag4iEqxseg59gBvcfX6ug2md1HMQkfBkU3O4NR+BtE7qOYhImGK5mM3MvmNmK8xsfvQ4O2nbrWZWa2aLzewzccS3B9UcRCRAub3PXGY/dve7khvMbCAwHhgE9AKqzayfu++MI0DVHEQkVK1tGYxxwCPuvtXdl5K4l/TweENSz0FEwhNncrjOzF4zs2lm1jVq6w0sT9qnPmpLYWaTzKzGzGpyNx9aPQcRCVPOkoOZVZvZG2ke44CpwHEkluR4H/jh/h7f3avcvdLdK1vyvqkp1HEQkQDlrOaQ7RXUZvYL4Kno5QoSU2d3KY7a4qGag4gEKq7ZSkcnvTwPeCN6PhMYb2YdzKwvUAbMzXd8uziOq+sgIgGKa7bSD8xsCIlB/TrgKgB3X2BmjwELgR3AtbHNVAJeXvI+A7bsoHNcAYiIxCSW5ODuX8ywbTIwOY/hpI+joYE+a1/k7w1laIVBEQlNa5vK2mq8/fqL9LY1/LEh5pm0IiIxUHLYh1XzZtLgxl92Dml6ZxGRg4ySwz50e+8vvOrHsa1Dt7hDERHJOyWHNFb/czn9drzFsztP0mVwIhIkJYc06mr+AMDqnqN0q0IRCZKSQxo7l85mox9Kx2PUcxCRMCk5pPGxD+fx9qHlFBS010XSIhIkJYe9rF25gtKG5Ww6egRmhqvvICIBUnLYS90rzwHQdcBoDC2vJCJhUnLYy7alL7HN29O34hNgWrRbRMKk5LCXTh8uYFlhXzoUdcSUHUQkUEoOSbyhgZKtS1h7xABAt48WkXApOSR5r24xR/ARHL17yQwVpEUkREoOSf65aA4AR5YlFttTQVpEQqXkkGTb8n+wzQsoOeFkIDGspNwgIiGKLTmY2VfNbJGZLTCzHyS132pmtWa22Mw+k8+YDvvwTZa3L6FDUcdELJiWzxCRIMVysx8zOxUYB5zo7lvN7KiofSAwHhgE9AKqzaxfvu4Gd9SWOuqPODEpTvUcRCRMcfUcvgJMcfetAO6+MmofBzzi7lvdfSlQC+TlbjsfbVhHT1ax/ciyxjbVHEQkVHElh37AKDN72cz+ambDovbewPKk/eqjtpx7r/Y1ADocPWh3o+ayikigcjasZGbVQM80m26L3vdIYCQwDHjMzI7dz+NPAiYBlJSUNC9YYN2y1wHoVjp493tEX90dU6IQkYDkLDm4++n72mZmXwF+54lq71wzawC6AyuAPkm7Fkdt6Y5fBVQBVFZWNnvwZ8fKRWzzAnr1HZAU5673UidCRMIS17DSk8CpAGbWDzgEWA3MBMabWQcz6wuUAXPzEVDRulreK+hF4SEdGtsMZQQRCVMss5WAacA0M3sD2AZMiHoRC8zsMWAhsAO4Nl8zlbpvrmNVx+MpTbNNNWkRCU0sycHdtwGX7mPbZGByPuPZsX0bPRs+oL7LGXu07x5WclAvQkQCoiukgZUrllJoOyk4su8e7Y0F6fyHJCISKyUHYG39YgA69jxuj/bkgrSISEiUHIBNH7wNwJG9++3Rvmv6qlZmFZHQKDkAO9csZbsXcFTvvmm3q+cgIqFRcgAO2fAuH7TrQfvCQ/Zo17UNIhIqJQfg8M0r+PCQXintu65zUM9BREKj5AD02PE+mzr1SWlvLEir5iAigQk+OWxYv5aubMA7H5OyTaNKIhKq4JPD6hWJmUqF3VKTwy4aVhKR0ASfHDZ8sAyAw3qk6Tk0DiuJiIQl+OSwZW3i9hFdepambNtdkFZ6EJGwBJ8cdq5bQYMb3Xqm3hNCPQcRCVXwyaFg43ustc57LNW9N3UcRCQ0wSeHos0r+bB9j7TbTF0HEQlU8Mnh8G0r2XjIUWm37V6VVdlBRMISfHLo1rCabR3T3epaq7KKSLhiSQ5m9qiZzY8edWY2P2nbrWZWa2aLzewzuYzjow3rOIKPaDj86PRxRl+VG0QkNHHdCe4Lu56b2Q+B9dHzgcB4YBDQC6g2s365ulXomvfrOAwo7Fqci8OLiLRZsQ4rWaLieyHw26hpHPCIu29196VALTA8V+//r+gCuEO7pU5jjeIDdJ2DiIQn7prDKOADd18Sve4NLE/aXh+1pTCzSWZWY2Y1q1atOqA337phNTu8HZ0/ln7pDE1WEpFQ5WxYycyqgXSV3tvc/ffR84vY3WvYL+5eBVQBVFZWHtDn98lnf5mdZ0yg1z5u3NBYc1B2EJHA5Cw5uPvpmbabWXvgfODkpOYVQPLa2cVRW84UtM/wI9BtQkUkUHEOK50OLHL3+qS2mcB4M+tgZn2BMmBuLNGRtGS3coOIBCaW2UqR8ew1pOTuC8zsMWAhsAO4NlczlbKhmoOIhCq25ODuE/fRPhmYnN9o0tNtQkUkVHHPVmrVdJtQEQmVkkMGuk2oiIRKySELGlYSkdAoOWSggrSIhErJIQPdJlREQqXkkImW7BaRQCk5ZKCCtIiESskhg92rssYciIhInik5ZKDbhIpIqJQcMtBtQkUkVEoOGWgqq4iESskhA1NJWkQCpeSQBV3nICKhUXLIQMNKIhIqJYcsqOMgIqFRcsjAbPdkVhGRkMSSHMxsiJnNMbP5ZlZjZsOjdjOzu82s1sxeM7OhccTXGGf0VT0HEQlNXD2HHwDfdfchwLei1wBnkbhvdBkwCZgaT3gJqjmISKjiuk2oA0dEzzsD70XPxwEPe2J60Bwz62JmR7v7+3EE2b5dIjuM+9lsju5clGi0RI9i95CTSNul3+K27wvD+nDFqGNb/LhxJYcbgGfM7C4SvZdTovbewPKk/eqjtpTkYGaTSPQuKCkpyUmQo8p6cM3o41ixbjM7GxL9B2/8j0jbpmVhDg7dO3XIyXFzlhzMrBromWbTbcAY4EZ3/39mdiHwX8Dp+3N8d68CqgAqKytz8lt+WIf23HzmCbk4tIhIq5az5ODu+/ywN7OHgX+LXj4OPBA9XwH0Sdq1OGoTEZE8iqsg/R7wqej5acCS6PlM4LJo1tJIYH1c9QYRkZDFVXO4EvipmbUHthDVDoCngbOBWmATcHk84YmIhC2W5ODuLwAnp2l34Nr8RyQiIsl0hbSIiKRQchARkRRKDiIikkLJQUREUtjBcCMbM1sFLDvAb+8OrG7BcNoCnXMYdM5haM45H+PuPdJtOCiSQ3OYWY27V8YdRz7pnMOgcw5Drs5Zw0oiIpJCyUFERFIoOUSL9wVG5xwGnXMYcnLOwdccREQklXoOIiKSQslBRERSBJMczOxMM1tsZrVmdkua7R3M7NFo+8tmVpr/KFtWFuf872a20MxeM7NnzeyYOOJsSU2dc9J+F5iZm1mbn/aYzTmb2YXRv/UCM/tNvmNsaVn8bpeY2V/M7JXo9/vsOOJsKWY2zcxWmtkb+9huZnZ39PN4zcyGNvtN3f2gfwAFwNvAscAhwKvAwL32uQa4L3o+Hng07rjzcM6nAh2j518J4Zyj/Q4H/gbMASrjjjsP/85lwCtA1+j1UXHHnYdzrgK+Ej0fCNTFHXczz/l/AUOBN/ax/WzgDyRuCz4SeLm57xlKz2E4UOvu77j7NuARYNxe+4wDfhk9fwIYY2Zt+f7rTZ6zu//F3TdFL+eQuPNeW5bNvzPA7cD/JXEvkbYum3O+Evi5u38I4O4r8xxjS8vmnB04InremcQNxtosd/8bsDbDLuOAhz1hDtDFzI5uznuGkhx6A8uTXtdHbWn3cfcdwHqgW16iy41szjnZl0n85dGWNXnOUXe7j7v/Tz4Dy6Fs/p37Af3MbLaZzTGzM/MWXW5kc87fAS41s3oSNxH7an5Ci83+/v/epLjuBCetiJldClSy+9atByUzawf8CJgYcyj51p7E0NJoEr3Dv5nZYHdfF2tUuXUR8JC7/9DMPg78yszK3b0h7sDailB6DiuAPkmvi6O2tPtEty/tDKzJS3S5kc05Y2anA7cBY919a55iy5WmzvlwoByYZWZ1JMZmZ7bxonQ2/871wEx33+7uS4G3SCSLtiqbc/4y8BiAu78EFJFYoO5gldX/7/sjlOTwd6DMzPqa2SEkCs4z99pnJjAhev454DmPKj1tVJPnbGYnAfeTSAxtfRwamjhnd1/v7t3dvdTdS0nUWca6e0084baIbH63nyTRa8DMupMYZnonn0G2sGzO+V1gDICZDSCRHFblNcr8mglcFs1aGgmsd/f3m3PAIIaV3H2HmV0HPENipsM0d19gZv8J1Lj7TOC/SHQ9a0kUfsbHF3HzZXnOdwKdgMej2vu77j42tqCbKctzPqhkec7PAGeY2UJgJ/B1d2+zveIsz/lrwC/M7EYSxemJbfmPPTP7LYkE3z2qo3wbKARw9/tI1FXOBmqBTcDlzX7PNvzzEhGRHAllWElERPaDkoOIiKRQchARkRRKDiIikkLJQUREUig5iBwAM+tiZtdEz3uZ2RNxxyTSkjSVVeQAREu6P+Xu5TGHIpITQVwEJ5IDU4DjzGw+sAQY4O7lZjYR+CxwGIklKu4isaz0F4GtwNnuvtbMjgN+DvQgcdHSle6+KP+nIZKehpVEDswtwNvuPgT4+l7byoHzgWHAZGCTu58EvARcFu1TBXzV3U8GbgLuzUvUIllSz0Gk5f3F3TcAG8xsPfDfUfvrQIWZdQJOYfeyJQAd8h+myL4pOYi0vOTVbRuSXjeQ+H+uHbAu6nWItEoaVhI5MBtILAG+39z9X8BSM/s8NN7/98SWDE6kuZQcRA5AtKrp7OiG73cewCEuAb5sZq8CC0h/O1OR2Ggqq4iIpFDPQUREUoRt9BUAAAAoSURBVCg5iIhICiUHERFJoeQgIiIplBxERCSFkoOIiKRQchARkRT/H7Hc+kQ6yPaOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(M_E.t,np.mean(M_E.v/mV,axis=0))\n",
    "plot(M_I.t,np.mean(M_I.v/mV,axis=0))\n",
    "xlabel('time')\n",
    "ylabel('voltage')\n",
    "legend(['excitatory','inhibitory'])\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WIu7zfk6XQFv"
   },
   "source": [
    "##3.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UgpoxkWWfbZ8"
   },
   "source": [
    "الف)چون اثر مهاری بر مهاری از بین رفته در حالت پایدار در ولتاژ ۰ نمی ایستد\n",
    "\n",
    "ب)فرقی با بخش یک نمی کند\n",
    "\n",
    "ج)فرقی با بخش یک نمی کند.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 803
    },
    "executionInfo": {
     "elapsed": 136486,
     "status": "ok",
     "timestamp": 1607198125643,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -210
    },
    "id": "uSjkN8-5XRYq",
    "outputId": "94d7ced6-bfd1-4193-efc3-dd5e7a2e2bd4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gMkDfvn0bEWkSmeyQBjioF7TqqJqDiOScjCUHdz8l2X4zuwI4CxjrXtN+sxrok3BYYVhW1/mnAdMASktLM9z+k6HkYAYHq1NaRHJPVKOVTge+D4xz98T5I2YDE8yslZn1A/oD86KIMZDhnAPhHEuLM9+/ISLSAFH1OfwS6AD8j5ktMLOpAO6+GHgSWAL8Gbje3XdHFGPmm5UgGM66bSN8uSZz1xARaaCMNSsl4+5HJNl3J3BnFsOJVs00GkugYw70vYuIkBujlXJYFpp6Dg6Hs366KPPXEhFJkZJDMtloVmrTCTr2gU+UHEQkdyg5JJXh6TOqHVysmoOI5BQlh1RksuYAQdPSZx/Azm2ZvY6ISIqUHJLJ1vDSnsXgu7Xwj4jkDCWHpLLVrDQ0eP90cWavIyKSIiWHZLLRIQ3hwj9t1O8gIjlDySElGU4OefnBNBqf/C2z1xERSZGSQ1JZnNLi4CFBs5Km0RCRHKDkkEy2mpUg6HfYuiFY/EdEJGJKDkllqUMaEp6UVqe0iERPySEVWak5hMlB/Q4ikgOUHJLJZvt/m07Qsa+Sg4jkBCWHpLLYrATQ6yhY8052riUikoSSQzLZ7JAG6DUcPl8OWzZk53oiIvVQcsglvY4O3j9eEG0cIhJ7US0TeoeZLQxXgfuLmfUKy83M7jez8nD/8Cji2yPbzUphclj9dnauJyJSj6hqDve4e4m7DwOeAX4Ylp9BsG50f2AyMCWi+ALZblZq0wm6HK5+BxGJXCTJwd2/TNhsx54/0ccDj3pgLtDJzA7JeoC1ZCk5APQerpqDiEQusj4HM7vTzFYBl7Cn5tAbWJVwWGVYVtfPTzazMjMrW7duXWaDzaZew2HTGtj0SdSRiEiMZSw5mNkcM1tUx2s8gLvf7u59gJnADQ09v7tPc/dSdy/t3r17usOvvkjwnq1mJQhqDqDag4hEqkWmTuzup6R46EzgWeBHwGqgT8K+wrAsIlnukAboWQJ5LaDyLTjyzOxdV0QkQVSjlfonbI4HqpdAmw1cFo5aGg1sdPfoZ6LLZs2hZVs45ChY+Ub2rikiso+M1Rz2424zGwhUASuAa8PyZ4EzgXJgC3BlNOGFopo++9Ax8OZDwZrSBa2jiUFEYi2S5ODu59dT7sD1WQ4niYiSQ98x8PoDsHo+FB0bTQwiEmt6QjqZKDqkAfqODt5Xvp7d64qIhJQcUpLl5NC2C/QYDCuUHEQkGkoOSUW4ZGffv4NV82D3ruhiEJHYUnJIJqpmJYB+X4Mdm2F1WfavLSKxt9/kYGZtzexfzOzhcLu/mZ2V+dByQQTPOVQ77ESwfCifk/1ri0jspVJzmAFsB/4u3F4N/CRjEUmgTScoHKnkICKRSCU5HO7uPwV2Arj7FiL5UzoCUTYrARxxSjBD6+ZmNHeUiDQJqSSHHWbWhrCNxcwOJ6hJxECEzUoAR4wN3j96KZrri0hspZIcfgT8GehjZjOBF4DvZzSqXBNVzeGQYdCuOyz9UzTXF5HY2u8T0u7+P2b2NjCa4E/o77j7ZxmPLBdENX1Gtbw8GHQ2vPs47NgSzLskIpIFqYxWGg4cCnwMrAH6mtnhZhbVvExZFHGzEsDgc2DnFvjgL9HFICKxk8oX/IPAcGAhwbdkMbAY6Ghm33L35vutFXWHNMChxwZNS0uehiHnRBeHiMRKKn0Oa4Cjw4V1RgBHAx8BXwd+msngckeEySG/RdC0tOzPsO3L/R8vIpIGqSSHAe6+uHrD3ZcAR7r7R5kLK1dE3OdQbdhE2LUV/vaHqCMRkZhIJTksNrMpZnZC+HoQWGJmrQiffWi2cqFZCYKlQw8eCvMfiTYOEYmNVJLDFQSL79wUvj4Ky3YCJ2UqsNyQAx3SECSnEZfDJwu1trSIZMV+k4O7b3X3n7n7ueHrXnff4u5V7r65MRc3s380MzezbuG2mdn9ZlZuZgvDkVLRi7rmAFByIbTsECwCJCKSYakMZe1vZk+Z2RIz+6j61dgLm1kf4FRgZULxGUD/8DUZmNLY6zRKjnQ5ANC6I4yaBItnwWflUUcjIs1cqhPvTQF2ETQjPQr8Lg3Xvo/gSevEr+DxwKMemAt0MrND0nCtA5QjzUrVRl8HLVrBKz+LOhIRaeZSSQ5t3P0FwNx9hbv/K/D3jbmomY0HVrv7u/vs6g2sStiuDMvqOsdkMyszs7J16zI0MV2udEhXa98DRk6Cdx8LJuQTEcmQVJLDdjPLAz4wsxvM7Fyg/f5+yMzmmNmiOl7jgR8AP2xM4O4+LXz2orR79+6NOVXTcsL3oW1XeO7W6Kf3EJFmK5Xk8B2gLXAjMAKYCFy2vx9y91PcvXjfF8Fop37Au2ZWARQCb5tZT4K1IvoknKYwLItIjjUrQdD3cMqPYNVcmDct6mhEpJlKJTkUuftmd6909yvd/Xyg74Fe0N3/5u493L3I3YsImo6Gu/snwGzgsnDU0mhgo7t/fKDXarRca1aqdvSl0P9U+Mu/wKdLoo5GRJqhVJLDbSmWpcOzBDWLcuBh4LoMXadhci05mMH4XwW1iMe+CZvXRh2RiDQz9U68Z2ZnAGcCvc3s/oRdBxGMXEqLsPZQ/dmB69N17sbL4Tb99j3g4sdhxt/DzAvg0lnQtkvUUYlIM5Gs5rAGmA9sC9+rX7OB0zIfWg7I9Q7f3iPgwkdh7Xsw/XT4YuX+f0ZEJAX11hzCYabvmtnv3D1tNYWmxcmpzui6DDgVLv0veOwimHIcnH0fDDkv95rCRKRJqbfmYGZ/M7OFBCOJFu77ymKM0WoKX7JFx8E1f4VuR8BTV8FvzoZVb0UdlYg0YckW+zkra1HkqlxvVkrUpR9c9ReYPwNeugv+4xToNRyGXQwDz4SOdT5LKCJSp2TNSiuqP5vZwcDIcHOeu8dkeEwTaFZKlN8CRl0NR00I1p2e9zA8e0vw6jYQCkuh19HQ9XDo3A869gl+RkRkH/v9ZjCzC4F7gJcJvikfMLPvuftTGY4teu5No1lpX606BEli1NWw7n1Y9idYORfefx4WzNxznOVBmy7BE9dtu0KbztCyLbRoDQVt9rznt4S8fLD88D0v/Jy357PlBfvqS6Z1/h4bcuyBHC8SA90HQs+haT9tKn823g6MrK4tmFl3YA7Q/JMD0KRqDnXpPiB4QZDsvlwDny+HDcvhixWwZX342hBs79wCO7cGr13bgpeI5K5jb4osOeTt04y0ntQenmsGmlCfQyrMgr6Hjr2DTuxUuMPuHVC1G3w3eFX4uSrhc0J53Sep+7wNUe/xaTi3SFPWpnNGTptKcnjOzJ4HHgu3v0nwJHPz11SbldLJLJgmXERiJZXksJZg/YZh4fY0d5+VuZBySRPrkBYRSZNUmofaAbcCo4DlwOsZjUhERCKXyhrSP3b3IQRzHh0C/K+Zzcl4ZLlAzUoiElMN6VheC3xC0CHdIzPh5Bo1K4lIPO03OZjZdWb2MvAC0BW42t1LMh1YzlDNQURiKJUO6T7ATe6+INPB5BwNiRSRmNpvcnD3TC3s00So5iAi8RPJw2xm9q9mttrMFoSvMxP23WZm5Wa2zMyiXTdCHdIiElNRzrp2n7vfm1hgZoOBCcAQoBcwx8wGuHt9j95mgZKDiMRPrk2DMR543N23u/tygrWkR0UXjvocRCSeokwON4QLB003s+rJQXoDqxKOqQzLajGzyWZWZmZl69aty0yEalYSkZjKWHIwszlmtqiO13hgCnA4wZQcHwM/a+j53X2au5e6e2n37t3THH3NVVCzkojEUcb6HNz9lFSOM7OHgWfCzdUEQ2erFYZl0VFuEJEYimq00iEJm+cCi8LPs4EJZtbKzPoB/YF52Y6vmnuVeh1EJJaiGq30UzMbRtBuUwFcA+Dui83sSWAJsAu4PsqRSm8sW82gbVVkZrZ0EZHcFUlycPdLk+y7E7gzi+HUqWr3boq+eIOyqgF8PepgRESyLNeGsuaMDxe+Ri/bwHO7R0YdiohI1ik51OOzd/5IlRsvVQ3b/8EiIs2MkkM9uq15iXf8CHa0VI+DiMSPkkMdPvtkJf13fcALu4/WaCURiSUlhzpUvPUcAF/0+hpVmrZbRGJIyaEOuyteY5O3oV3fYVrSQURiScmhDj0/f5uP2g4lv0WBmpVEJJaUHPax/tNKDq1axZZDRmMGrqqDiMSQksM+Vi54AYDOg04gz7RSqIjEk5LDPrYvf5PtXsBhJcdhmDqkRSSWlBz20WHDIlYU9KNlq9ZBs1LUAYmIREDJIYFXVdFnxwd83nEQAGamZiURiSUlhwRrKt7jILZghwRTZlQv5aBOaRGJGyWHBJ8snQtAl/7BstXVK4QqN4hI3Cg5JNixagE7PJ8+A4cDkBdmB+UGEYmbyJKDmX3bzJaa2WIz+2lC+W1mVm5my8zstGzG1O7zJaxqcSitWrcNYgnLNWJJROImksV+zOwkYDxwlLtvN7MeYflgYAIwBOgFzDGzAdlaDa7HtgoqD9ozRbealUQkrqKqOXwLuNvdtwO4+9qwfDzwuLtvd/flQDkwKhsBbf7yc3ryGbu6DKwps5pmJWUHEYmXqJLDAOB4M3vTzP7XzKqXW+sNrEo4rjIsq8XMJptZmZmVrVu3rtEBrSlfCEDLQwYlXCN4V81BROImY81KZjYH6FnHrtvD63YBRgMjgSfN7LCGnN/dpwHTAEpLSxv99b1x5SIAuvUbWlNW0yGt5CAiMZOx5ODup9S3z8y+BfyXBw8QzDOzKqAbsBrok3BoYViWcbs+XcoOz6dXv8F74gzf1SEtInETVbPS08BJAGY2AGgJfAbMBiaYWSsz6wf0B+ZlI6DWG8tZk9+bFgUta8pqmpWyEYCISA6JZLQSMB2YbmaLgB3A5WEtYrGZPQksAXYB12drpFL3rctZ264/RQlle5qVlB5EJF4iSQ7uvgOYWM++O4E7sxnPrp076Fn1Kas61v1YRZVyg4jEjJ6QBtauXk4LqyK/S7+9yk3tSiISU0oOwIbKZQC07Xn4XuV5NblB2UFE4kXJAdjy6YcAdC0csFf5ntFKWQ5IRCRiSg7A7vXL2en59Oi9d83B1CEtIjGl5AC03LSST/N6kN9i7/75PHU5iEhMKTkAHbauZkOrXrV3hDUHPQQnInGj5AB03/UxW9v1qVWeV7MUXHbjERGJWuyTw6aNG+jMJrzTobX2GdU1h2xHJSISrdgnh89WByOVCrr0rbXPNJRVRGIq9slh06crAGjXvXbNIU9TdotITMU+OWzbECwf0alnUa19e5qVlB1EJF5inxx2f7GaKje69qzdrIRqDiISU7FPDvmb17DBOlLQslWtfdWzsoqIxE3sk0PrrZ/yeYvude7TYj8iElexTw4H7VjL5pY96tynNaRFJK5inxy6VK1nR9u6lrpOWOwnmwGJiOSASJKDmT1hZgvCV4WZLUjYd5uZlZvZMjOre/WdNPlq0xccxFdUHdS7njiDdzUriUjcRLUS3DerP5vZz4CN4efBwARgCNALmGNmAzK1VOj6jytoBxR0qmNeJRJnZc3E1UVEclekzUoWfPteCDwWFo0HHnf37e6+HCgHRmXq+l+GD8C16VrHMFb2dEhrym4RiZuo+xyOBz519w/C7d7AqoT9lWFZLWY22czKzKxs3bp1B3Tx7Zs2sNPz6Xhw7aejg2sE70oNIhI3GWtWMrM5QF09vbe7+3+Hny9iT62hQdx9GjANoLS09IC+v0eceSVVp11G73qeZ8hTs5KIxFTGkoO7n5Jsv5m1AM4DRiQUrwYS584uDMsyJi8/v959es5BROIqymalU4Cl7l6ZUDYbmGBmrcysH9AfmBdJdOg5BxGJr0hGK4UmsE+TkrsvNrMngSXALuD6TI1USkXNaCX1OohIzESWHNz9inrK7wTuzG40ddszWinSMEREsi7q0Uo5Tc85iEhcKTkkkaeV4EQkppQcktgzfUa0cYiIZJuSQxJ7mpWUHUQkXpQcktjznEOkYYiIZJ2SQxJW8+S0soOIxIuSQxJ5eghORGJKySEJCxuW1KwkInGj5JDEnukzlB1EJF6UHJLQlN0iEldKDknsaVZSehCReFFySEKDlUQkrpQckqhZ7CfiOEREsk3JIYk902coPYhIvCg5JKHnHLiu4f4AAAatSURBVEQkrpQcklKHtIjEUyTJwcyGmdlcM1tgZmVmNiosNzO738zKzWyhmQ2PIr49cQbvSg0iEjdR1Rx+CvzY3YcBPwy3Ac4gWDe6PzAZmBJNeIE8ZQcRiamolgl14KDwc0dgTfh5PPCoB48kzzWzTmZ2iLt/HEWQBflBcrjykbfo3akNeXmQb0ZentXM2Joueyb5S9P50no2EclV3xzZh0nHH5b280aVHG4CnjezewlqL2PC8t7AqoTjKsOyWsnBzCYT1C7o27dvRoIc1PMgfnJOMe99/CXbd1Wxu8qDV7r7INJ+OlV1ROKiW/tWGTlvxpKDmc0Betax63ZgLHCzu/+nmV0I/AdwSkPO7+7TgGkApaWlGfk2zMszJo4+NBOnFhHJaRlLDu5e75e9mT0KfCfc/APw6/DzaqBPwqGFYZmIiGRRVB3Sa4ATws8nAx+En2cDl4WjlkYDG6PqbxARibOo+hyuBn5hZi2AbYR9B8CzwJlAObAFuDKa8ERE4i2S5ODurwIj6ih34PrsRyQiIon0hLSIiNSi5CAiIrUoOYiISC1KDiIiUot5M5hx1MzWASsO8Me7AZ+lMZymQPccD7rneGjMPR/q7t3r2tEskkNjmFmZu5dGHUc26Z7jQfccD5m6ZzUriYhILUoOIiJSi5JDOHlfzOie40H3HA8ZuefY9zmIiEhtqjmIiEgtSg4iIlJLbJKDmZ1uZsvMrNzMbq1jfyszeyLc/6aZFWU/yvRK4Z6/a2ZLzGyhmb1gZk1+ZaP93XPCceebmZtZkx/2mMo9m9mF4X/rxWb2+2zHmG4p/L/d18xeMrN3wv+/z4wiznQxs+lmttbMFtWz38zs/vD3sdDMhjf6ou7e7F9APvAhcBjQEngXGLzPMdcBU8PPE4Anoo47C/d8EtA2/PytONxzeFwH4K/AXKA06riz8N+5P/AO0Dnc7hF13Fm452nAt8LPg4GKqONu5D1/DRgOLKpn/5nAcwTLx48G3mzsNeNScxgFlLv7R+6+A3gcGL/PMeOB34SfnwLGmpllMcZ02+89u/tL7r4l3JxLsPJeU5bKf2eAO4D/T7CWSFOXyj1fDfzK3T8HcPe1WY4x3VK5ZwcOCj93JFhgrMly978CG5IcMh541ANzgU5mdkhjrhmX5NAbWJWwXRmW1XmMu+8CNgJdsxJdZqRyz4n+geAvj6Zsv/ccVrf7uPufshlYBqXy33kAMMDMXjOzuWZ2etaiy4xU7vlfgYlmVkmwiNi3sxNaZBr6732/oloJTnKImU0EStmzdGuzZGZ5wM+BKyIOJdtaEDQtnUhQO/yrmQ119y8ijSqzLgIecfefmdnfAb81s2J3r4o6sKYiLjWH1UCfhO3CsKzOY8LlSzsC67MSXWakcs+Y2SnA7cA4d9+epdgyZX/33AEoBl42swqCttnZTbxTOpX/zpXAbHff6e7LgfcJkkVTlco9/wPwJIC7vwG0JpigrrlK6d97Q8QlObwF9DezfmbWkqDDefY+x8wGLg8/fwN40cOeniZqv/dsZkcDDxEkhqbeDg37uWd33+ju3dy9yN2LCPpZxrl7WTThpkUq/28/TVBrwMy6ETQzfZTNINMslXteCYwFMLNBBMlhXVajzK7ZwGXhqKXRwEZ3/7gxJ4xFs5K77zKzG4DnCUY6THf3xWb2/4Ayd58N/AdB1bOcoONnQnQRN16K93wP0B74Q9j3vtLdx0UWdCOleM/NSor3/DxwqpktAXYD33P3JlsrTvGe/xF42MxuJuicvqIp/7FnZo8RJPhuYT/Kj4ACAHefStCvciZQDmwBrmz0NZvw70tERDIkLs1KIiLSAEoOIiJSi5KDiIjUouQgIiK1KDmIiEgtSg4iB8DMOpnZdeHnXmb2VNQxiaSThrKKHIBwSvdn3L044lBEMiIWD8GJZMDdwOFmtgD4ABjk7sVmdgVwDtCOYIqKewmmlb4U2A6c6e4bzOxw4FdAd4KHlq5296XZvw2RuqlZSeTA3Ap86O7DgO/ts68YOA8YCdwJbHH3o4E3gMvCY6YB33b3EcAtwINZiVokRao5iKTfS+6+CdhkZhuBP4blfwNKzKw9MIY905YAtMp+mCL1U3IQSb/E2W2rErarCP7N5QFfhLUOkZykZiWRA7OJYArwBnP3L4HlZnYB1Kz/e1Q6gxNpLCUHkQMQzmr6Wrjg+z0HcIpLgH8ws3eBxdS9nKlIZDSUVUREalHNQUREalFyEBGRWpQcRESkFiUHERGpRclBRERqUXIQEZFalBxERKSW/wPnXeRl8BLqOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "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"
    },
    {
     "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": [
    "for ps in [[0,0,0.5,0.5],[1,1,0.5,0.5],[1,1,1,1]]:\n",
    "  pii,pee,pei,pie=ps\n",
    "  start_scope()\n",
    "  G_E=NeuronGroup(1000,eqs_exc,threshold='v > Vt',method='exponential_euler')\n",
    "  G_I=NeuronGroup(1000,eqs_inh,threshold='v > Vt',method='exponential_euler')\n",
    "  G_E.v=El\n",
    "  G_I.v=El\n",
    "  S_exc2inh=Synapses(G_E,G_I,on_pre='gs_post+=5*nsiemens',delay=Delay)\n",
    "  S_exc2inh.connect(p=pei)\n",
    "  S_exc2exc=Synapses(G_E,G_E,on_pre='gs_post+=5*nsiemens',delay=Delay)\n",
    "  S_exc2exc.connect(p=pee)\n",
    "  S_inh2exc=Synapses(G_I,G_E,on_pre='gs_post+=5*nsiemens',delay=Delay)\n",
    "  S_inh2exc.connect(p=pie)\n",
    "  S_inh2inh=Synapses(G_I,G_I,on_pre='gs_post+=5*nsiemens',delay=Delay)\n",
    "  S_inh2inh.connect(p=pii)\n",
    "  M_E=StateMonitor(G_E,'v',record=True)\n",
    "  M_I=StateMonitor(G_I,'v',record=True)\n",
    "  run(1000*ms)\n",
    "  plot(M_E.t,np.mean(M_E.v/mV,axis=0))\n",
    "  plot(M_I.t,np.mean(M_I.v/mV,axis=0))\n",
    "  xlabel('time')\n",
    "  ylabel('voltage')\n",
    "  legend(['excitatory','inhibitory'])\n",
    "  show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V0znLYSwiDCv"
   },
   "source": [
    "##3.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Db9PnLoPiEtF"
   },
   "source": [
    "بله .می توان.\n",
    "کافی است جمله ی پیش برنده ولتاژ به سمت \n",
    "\n",
    "Vs \n",
    "\n",
    "(برای تحریکی ۰ و برای مهاری -۸۰)\n",
    "\n",
    "را با ضریب\n",
    "\n",
    "gs_prime\n",
    "\n",
    "به معادله نورون های تحریکی و مهاری اضافه کنیم \n",
    "تا اثر نورون های مهاری بر مهاری بصورت مهاری \n",
    "و نورون های تحریکی بر تحریکی بصورت تحریکی ظاهر شود\n"
   ]
  }
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