mind_field_sim-179.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "source": [
    "# http://appliedbrainresearch.com\n",
    "# Copyright 2018 C. Eliasmith\n",
    "# MIT License"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define the cortical model \n",
    "model={}\n",
    "model[26]=[1,2,3]\n",
    "model[27]=[2,3,4]\n",
    "model[28]=[3,4,5]\n",
    "model[29]=[6,7,8]\n",
    "model[30]=[7,8,9]\n",
    "model[31]=[8,9,10]\n",
    "model[32]=[11,12,13]\n",
    "model[33]=[12,13,14]\n",
    "model[34]=[13,14,15]\n",
    "model[35]=[16,17,18]\n",
    "model[36]=[17,18,19]\n",
    "model[37]=[18,19,20]\n",
    "model[38]=[21,22,23]\n",
    "model[39]=[22,23,24]\n",
    "model[40]=[23,24,25]\n",
    "model[41]=[1,6,11]\n",
    "model[42]=[6,11,16]\n",
    "model[43]=[11,16,21]\n",
    "model[44]=[2,7,12]\n",
    "model[45]=[7,12,17]\n",
    "model[46]=[12,17,22]\n",
    "model[47]=[3,8,13]\n",
    "model[48]=[8,13,18]\n",
    "model[49]=[13,18,23]\n",
    "model[50]=[4,9,14]\n",
    "model[51]=[9,14,19]\n",
    "model[52]=[14,19,24]\n",
    "model[53]=[5,10,15]\n",
    "model[54]=[10,15,20]\n",
    "model[55]=[15,20,25]\n",
    "model[57]=[6,12,18]\n",
    "model[60]=[7,13,19]\n",
    "model[63]=[8,14,20]\n",
    "model[65]=[15,19,23]\n",
    "model[66]=[10,14,18]\n",
    "model[67]=[14,18,22]\n",
    "model[68]=[5,9,13]\n",
    "model[69]=[9,13,17]\n",
    "model[70]=[13,17,21]\n",
    "model[71]=[4,8,12]\n",
    "model[72]=[8,12,16]\n",
    "model[73]=[3,7,11]\n",
    "model[74]=[8,12,18]\n",
    "model[75]=[9,13,19]\n",
    "model[76]=[10,14,20]\n",
    "model[77]=[26,41]\n",
    "model[78]=[26,47]\n",
    "model[79]=[32,41]\n",
    "model[80]=[32,47]\n",
    "model[81]=[32,43]\n",
    "model[82]=[32,49]\n",
    "model[83]=[38,43]\n",
    "model[84]=[38,49]\n",
    "model[85]=[27,44]\n",
    "model[86]=[27,50]\n",
    "model[87]=[33,44]\n",
    "model[88]=[33,50]\n",
    "model[89]=[33,46]\n",
    "model[90]=[33,52]\n",
    "model[91]=[39,46]\n",
    "model[92]=[39,52]\n",
    "model[93]=[47,28]\n",
    "model[94]=[53,28]\n",
    "model[95]=[34,47]\n",
    "model[96]=[34,53]\n",
    "model[97]=[34,49]\n",
    "model[98]=[34,55]\n",
    "model[99]=[40,49]\n",
    "model[100]=[40,55]\n",
    "model[111]=[41,42]\n",
    "model[112]=[42,43]\n",
    "model[113]=[44,45]\n",
    "model[114]=[45,46]\n",
    "model[115]=[47,48]\n",
    "model[116]=[48,49]\n",
    "model[117]=[50,51]\n",
    "model[118]=[51,52]\n",
    "model[119]=[53,54]\n",
    "model[120]=[54,55]\n",
    "model[129]=[26,57]\n",
    "model[130]=[26,72]\n",
    "model[131]=[38,57]\n",
    "model[132]=[38,72]\n",
    "model[133]=[27,60]\n",
    "model[134]=[27,69]\n",
    "model[135]=[39,60]\n",
    "model[136]=[39,69]\n",
    "model[137]=[28,63]\n",
    "model[138]=[28,66]\n",
    "model[139]=[40,63]\n",
    "model[140]=[40,66]\n",
    "model[144]=[29,70]\n",
    "model[145]=[30,67]\n",
    "model[146]=[31,65]\n",
    "model[147]=[32,73]\n",
    "model[148]=[33,71]\n",
    "model[149]=[34,68]\n",
    "model[153]=[47,74]\n",
    "model[154]=[50,75]\n",
    "model[155]=[53,76]\n",
    "model[171]=[78,81,83]\n",
    "model[172]=[86,89,91]\n",
    "model[173]=[94,97,99]\n",
    "model[174]=[130,132]\n",
    "model[175]=[134,136]\n",
    "model[176]=[138,140]\n",
    "model[181]=[78,82,84]\n",
    "model[182]=[86,90,92]\n",
    "model[183]=[94,98,100]\n",
    "model[184]=[78,84,153]\n",
    "model[185]=[86,92,154]\n",
    "model[186]=[94,100,155]\n",
    "model[190]=[79,116]\n",
    "model[191]=[87,118]\n",
    "model[192]=[95,120]\n",
    "model[196]=[77,82,84]\n",
    "model[197]=[85,90,92]\n",
    "model[198]=[93,98,100]\n",
    "model[199]=[129,131]\n",
    "model[200]=[133,135]\n",
    "model[201]=[137,140]\n",
    "model[203]=[79,83,84]\n",
    "model[204]=[87,91,92]\n",
    "model[205]=[95,99,100]\n",
    "model[206]=[82,83,147]\n",
    "model[207]=[90,91,148]\n",
    "model[208]=[98,99,149]\n",
    "model[216]=[78,115]\n",
    "model[217]=[86,117]\n",
    "model[218]=[94,119]\n",
    "model[227]=[130,131]\n",
    "model[228]=[134,135]\n",
    "model[229]=[138,139]\n",
    "model[233]=[78,79,116]\n",
    "model[234]=[86,87,118]\n",
    "model[235]=[94,95,120]\n",
    "model[242]=[77,88,118]\n",
    "model[243]=[85,96,120]\n",
    "model[245]=[77,83,115]\n",
    "model[246]=[85,91,117]\n",
    "model[247]=[93,99,119]\n",
    "\n",
    "complex_cells = {}\n",
    "complex_cells[291]=[111,113,115,117,119]\n",
    "complex_cells[292]=[112,114,116,118,120]\n",
    "complex_cells[293]=[130,134,138,144,145,146]\n",
    "\n",
    "IT = {}\n",
    "IT[251]=[245,246,247,248,249]\n",
    "IT[252]=[156,157,158,159,160,291,292]\n",
    "IT[253]=[171,172,173,174,175,176,179,288,289,290]\n",
    "IT[254]=[181,182,183,184,185,186,189,271,272,273]\n",
    "IT[255]=[190,191,192,193,194,195,265,266,267,268,269,270,285,286,287]\n",
    "IT[256]=[196,197,198,199,200,201]\n",
    "IT[257]=[203,204,205,206,207,208]\n",
    "IT[258]=[216,217,218,274,275,276,293]\n",
    "IT[259]=[227,228,229,230,231,277,278,279,280,281]\n",
    "IT[260]=[233,234,235,242,243,261,262,263]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of neurons is: 179\n",
      "Neurons in retina: 25\n",
      "Neurons in V1: 45\n",
      "Neurons in V2: 55\n",
      "Neurons in V4: 44\n",
      "Neurons in IT: 10\n"
     ]
    }
   ],
   "source": [
    "print(\"The total number of neurons is:\", len(model)+len(IT)+len(complex_cells)+25)\n",
    "print(\"Neurons in retina:\", 25)\n",
    "cnt = 0\n",
    "for el in model.keys():\n",
    "    if el>=26 and el<=76:\n",
    "        cnt+=1\n",
    "print(\"Neurons in V1:\", cnt)\n",
    "cnt = 0\n",
    "for el in model.keys():\n",
    "    if el>=77 and el<=155:\n",
    "        cnt+=1\n",
    "print(\"Neurons in V2:\", cnt)\n",
    "cnt = 0\n",
    "for el in model.keys():\n",
    "    if el>=156 and el<=293:\n",
    "        cnt+=1\n",
    "print(\"Neurons in V4:\", cnt+len(complex_cells))\n",
    "print(\"Neurons in IT:\", len(IT))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change this to run different inputs\n",
    "image = [0,0,0,1,0,\n",
    "         0,0,0,1,0,\n",
    "         0,0,0,1,0,\n",
    "         0,0,0,1,0,\n",
    "         0,0,0,1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active retinal neurons: [4, 9, 14, 19, 24]\n",
      "Processing standard cortical neurons...\n",
      "Processing complex neurons...\n",
      "Processing IT neurons...\n",
      "Potentially active IT neurons: [252, 252]\n",
      "Processing inhibition...\n",
      "Active cortical neurons: [50, 51, 52, 117, 118, 252, 291, 292]\n",
      "The predicted number is: 1\n"
     ]
    }
   ],
   "source": [
    "active_neurons = []\n",
    "\n",
    "for i in range(len(image)):\n",
    "    if image[i] == 1:\n",
    "        active_neurons.append(i+1)\n",
    "active_retinal_neurons = active_neurons.copy()\n",
    "\n",
    "print(\"Active retinal neurons:\", active_neurons)\n",
    "\n",
    "#Process standard cortical neurons\n",
    "print(\"Processing standard cortical neurons...\")\n",
    "for i in model.keys():\n",
    "    #print(\"Processing neuron:\", i)\n",
    "    should_fire = True\n",
    "    for j in model[i]:\n",
    "        if j not in active_neurons:\n",
    "            should_fire = False\n",
    "            break\n",
    "    if should_fire:\n",
    "        active_neurons.append(i)\n",
    "\n",
    "#Process complex neurons\n",
    "print(\"Processing complex neurons...\")\n",
    "for i in complex_cells.keys():\n",
    "    should_fire = False\n",
    "    for j in complex_cells[i]:\n",
    "        if j in active_neurons:\n",
    "            active_neurons.append(i)\n",
    "\n",
    "#Process IT neurons\n",
    "active_IT_neurons = []\n",
    "print(\"Processing IT neurons...\")\n",
    "for i in IT.keys():\n",
    "    should_fire = False\n",
    "    for j in IT[i]:\n",
    "        if j in active_neurons:\n",
    "            active_IT_neurons.append(i)\n",
    "print(\"Potentially active IT neurons:\", active_IT_neurons)\n",
    "print(\"Processing inhibition...\")\n",
    "active_IT_neurons = list(set(active_IT_neurons))\n",
    "if len(active_IT_neurons) == 1:\n",
    "    active_neurons.append(active_IT_neurons[0])\n",
    "elif 259 in active_IT_neurons:\n",
    "    active_neurons.append(259)\n",
    "elif 260 in active_IT_neurons:\n",
    "    active_neurons.append(260)\n",
    "elif 251 in active_IT_neurons:\n",
    "    active_neurons.append(251)    \n",
    "elif 253 in active_IT_neurons:\n",
    "    active_neurons.append(253)\n",
    "elif 254 in active_IT_neurons:\n",
    "    active_neurons.append(254)\n",
    "elif 258 in active_IT_neurons:\n",
    "    active_neurons.append(258)\n",
    "elif 257 in active_IT_neurons:\n",
    "    active_neurons.append(257)\n",
    "elif 255 in active_IT_neurons:\n",
    "    active_neurons.append(255)\n",
    "\n",
    "\n",
    "active_cortical_neurons = list(set(active_neurons)-set(active_retinal_neurons))\n",
    "active_cortical_neurons.sort()\n",
    "print(\"Active cortical neurons:\", active_cortical_neurons)\n",
    "predicted_number = active_neurons[-1]-251\n",
    "if predicted_number not in range(10):\n",
    "    predicted_number = \"Not a number\"\n",
    "print(\"The predicted number is:\", predicted_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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