Fig_1 & Fig_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sympy import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average of efficacy\n",
    "W_EI=0.44\n",
    "W_IE=0.66\n",
    "W_II=0.54\n",
    "W_EE=0.37\n",
    "W_EI2=0.49\n",
    "W_IE2=0.65\n",
    "W_II2=0.53"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2964\n",
      "0.22749999999999998\n",
      "0.2384\n"
     ]
    }
   ],
   "source": [
    "mu_EI=W_EI\n",
    "mu_IE=W_IE\n",
    "mu_II=W_II\n",
    "sigma_EI2=W_EI2-W_EI**2\n",
    "sigma_IE2=W_IE2-W_IE2**2\n",
    "sigma_II2=W_II2-W_II**2\n",
    "sigma_EI=np.sqrt(sigma_EI2)\n",
    "sigma_IE=np.sqrt(sigma_IE2)\n",
    "sigma_II=np.sqrt(sigma_II2)\n",
    "print(str(sigma_EI2)+'\\n'+str(sigma_IE2)+'\\n'+str(sigma_II2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CellID</th>\n",
       "      <th>DendriteID</th>\n",
       "      <th>SpineID</th>\n",
       "      <th>Global_SpineID</th>\n",
       "      <th>Starting Imaging Session</th>\n",
       "      <th>Ending Imaging Session</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Lambda1</th>\n",
       "      <th>Lambda2</th>\n",
       "      <th>Shape</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Center X</th>\n",
       "      <th>Center Y</th>\n",
       "      <th>Nearest Point X</th>\n",
       "      <th>Nearest Point Y</th>\n",
       "      <th>Z-Dimension Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>10.6930</td>\n",
       "      <td>7.6533</td>\n",
       "      <td>4.5668</td>\n",
       "      <td>0.252576</td>\n",
       "      <td>11.156</td>\n",
       "      <td>114.38</td>\n",
       "      <td>176.15</td>\n",
       "      <td>114.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.1684</td>\n",
       "      <td>5.0923</td>\n",
       "      <td>3.8244</td>\n",
       "      <td>0.142194</td>\n",
       "      <td>11.822</td>\n",
       "      <td>174.25</td>\n",
       "      <td>140.18</td>\n",
       "      <td>174.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>14.6730</td>\n",
       "      <td>9.0718</td>\n",
       "      <td>6.6570</td>\n",
       "      <td>0.153527</td>\n",
       "      <td>10.853</td>\n",
       "      <td>198.24</td>\n",
       "      <td>146.15</td>\n",
       "      <td>198.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>42.8730</td>\n",
       "      <td>36.3070</td>\n",
       "      <td>7.9729</td>\n",
       "      <td>0.639886</td>\n",
       "      <td>15.923</td>\n",
       "      <td>209.63</td>\n",
       "      <td>148.30</td>\n",
       "      <td>207.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.5290</td>\n",
       "      <td>30.3960</td>\n",
       "      <td>5.9931</td>\n",
       "      <td>0.670610</td>\n",
       "      <td>12.720</td>\n",
       "      <td>389.14</td>\n",
       "      <td>271.00</td>\n",
       "      <td>397.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CellID  DendriteID  SpineID  Global_SpineID  Starting Imaging Session  \\\n",
       "0       1           1        1               1                         1   \n",
       "1       1           1        2               2                         1   \n",
       "2       1           1        3               3                         1   \n",
       "3       1           1        4               4                         1   \n",
       "4       1           1        5               5                         1   \n",
       "\n",
       "   Ending Imaging Session   Volume  Lambda1  Lambda2     Shape  Distance  \\\n",
       "0                       6  10.6930   7.6533   4.5668  0.252576    11.156   \n",
       "1                       1   5.1684   5.0923   3.8244  0.142194    11.822   \n",
       "2                       6  14.6730   9.0718   6.6570  0.153527    10.853   \n",
       "3                       5  42.8730  36.3070   7.9729  0.639886    15.923   \n",
       "4                       1  19.5290  30.3960   5.9931  0.670610    12.720   \n",
       "\n",
       "   Center X  Center Y  Nearest Point X  Nearest Point Y  Z-Dimension Location  \n",
       "0    114.38    176.15            114.0            165.0                     0  \n",
       "1    174.25    140.18            174.0            152.0                     0  \n",
       "2    198.24    146.15            198.0            157.0                     0  \n",
       "3    209.63    148.30            207.0            164.0                     0  \n",
       "4    389.14    271.00            397.0            261.0                    -1  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting E->E connectivity from the spine imaging data\n",
    "c_EE=0.2\n",
    "path=\"../../../documents/Practical Biomedical Modelling/Semester 2/Global_Spines_info.csv\"\n",
    "spines_info=pd.read_csv(path)\n",
    "spines_info.drop('Unnamed: 0',axis=1,inplace=True)\n",
    "spines_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00756288522510971\n"
     ]
    }
   ],
   "source": [
    "spines_IS1=spines_info.loc[spines_info['Starting Imaging Session']==1]\n",
    "spines_IS1.head(100)\n",
    "S=spines_IS1['Volume'].mean()\n",
    "g=W_EE/S\n",
    "print(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connectivity matrix 8*8\n",
    "# EI\n",
    "# I*\n",
    "def W_Construction():\n",
    "    c_EE=0.51\n",
    "    W=np.zeros((8,8))\n",
    "    c=np.zeros((8,8))\n",
    "    for i in range(6):\n",
    "        for j in range(6,8):\n",
    "            if(random.uniform(0,1)<=0.5):\n",
    "                c[i,j]=1\n",
    "                W[i,j]=np.random.lognormal(mu_EI,sigma_EI)\n",
    "    for i in range(6,8):\n",
    "        for j in range(6):\n",
    "            if(random.uniform(0,1)<=0.5):\n",
    "                c[i,j]=1\n",
    "                W[i,j]=np.random.lognormal(mu_IE,sigma_IE)\n",
    "    for i in range(6,8):\n",
    "        for j in range(6,8):\n",
    "            if(random.uniform(0,1)<=0.5):\n",
    "                c[i,j]=1\n",
    "                W[i,j]=np.random.lognormal(mu_II,sigma_II)\n",
    "    for i in range(6):\n",
    "        for j in range(6):\n",
    "            if(random.uniform(0,1)<=c_EE):\n",
    "                index=random.randint(1,3688)\n",
    "                W[i,j]=spines_info['Volume'].loc[spines_info['Global_SpineID']==index]*g\n",
    "            else:\n",
    "                W[i,j]=0\n",
    "            c[i,j]=c_EE\n",
    "    return W,c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "W=np.zeros((6,8,8))\n",
    "c=np.zeros((6,8,8))\n",
    "plt.figure(figsize=(24,4))\n",
    "for i in range(6):\n",
    "    W[i],c[i]=W_Construction()\n",
    "    plt.subplot(1,6,i+1)\n",
    "    sns.heatmap(W[i],vmin=0,vmax=1.6,cmap='jet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recurrent input of neuron i\n",
    "h=np.zeros((8,5000))\n",
    "for i in range(8):\n",
    "    for j in range(6):\n",
    "        tsteps=0\n",
    "        while(True):\n",
    "            tstep=random.randint(10,100)\n",
    "            tsteps=tsteps+tstep\n",
    "            if(tsteps<5000):\n",
    "                h[i,tsteps]=h[i,tsteps]+c[0,i,j]*W[0,i,j]\n",
    "            else:\n",
    "                break\n",
    "    for j in range(6,8):\n",
    "        tsteps=0\n",
    "        while(True):\n",
    "            tstep=random.randint(10,100)\n",
    "            tsteps=tsteps+tstep\n",
    "            if(tsteps<5000):\n",
    "                h[i,tsteps]=h[i,tsteps]-c[0,i,j]*W[0,i,j]\n",
    "            else:\n",
    "                break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a229dd1d0>]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Potential of neuron\n",
    "v=np.zeros((8,5000))\n",
    "theta=33\n",
    "tau_m=10\n",
    "H_E=77.6\n",
    "H_I=57.8\n",
    "v_R=24.75\n",
    "for i in range(8):\n",
    "    v[i,0]=v_R\n",
    "t=range(4999)\n",
    "for dt in t:\n",
    "    for i in range(6):\n",
    "        if v[i,dt]==float('inf'):\n",
    "            v[i,dt+1]=v_R\n",
    "        else:\n",
    "            v[i,dt+1]=v[i,dt]+0.001*(-v[i,dt]/tau_m+h[i,dt]+H_E)\n",
    "            if v[i,dt+1]>=theta:\n",
    "                v[i,dt+1]=float('inf')\n",
    "    for i in range(6,8):\n",
    "        if v[i,dt]==float('inf'):\n",
    "            v[i,dt+1]=v_R\n",
    "        else:\n",
    "            v[i,dt+1]=v[i,dt]+0.001*(-v[i,dt]/tau_m+h[i,dt]+H_I)\n",
    "            if v[i,dt+1]>=theta:\n",
    "                v[i,dt+1]=float('inf')\n",
    "plt.figure()\n",
    "plt.plot(range(5000),v[1])\n",
    "plt.figure()\n",
    "plt.plot(range(5000),v[6],'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 1. 2. 3. 4. 5.]\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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