mind_field_generator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is for generating the tuning curves of the neurons in the visual system for the Mind Field show.  Use at your own risk, MIT License. C. Eliasmith."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def print_layer(layer):\n",
    "    for i, row in enumerate(layer):\n",
    "        print(i+1)\n",
    "        print(row.reshape(5,5))    \n",
    "\n",
    "###########################\n",
    "#one person per pixel in a 5x5 array - Retina\n",
    "###########################\n",
    "retina = np.diag(np.ones(25))\n",
    "#print_layer(retina)\n",
    "\n",
    "###########################\n",
    "#horizontal line segments - V1\n",
    "###########################\n",
    "v1_hor_ind = np.array([[0,1,2],[1,2,3],[2,3,4]])\n",
    "v1_hor = []\n",
    "\n",
    "for i in range(5):\n",
    "    for j in range(3):\n",
    "        tmp = np.zeros(25)\n",
    "        tmp[v1_hor_ind[j]] = 1\n",
    "        v1_hor.append(tmp)\n",
    "    v1_hor_ind += 5\n",
    "#print_layer(v1_hor)\n",
    "    \n",
    "###########################\n",
    "#verticle line segments - V1\n",
    "###########################\n",
    "v1_vert_ind = np.array([[0,5,10],[5,10,15],[10,15,20]])\n",
    "v1_vert = []\n",
    "\n",
    "for i in range(5):\n",
    "    for j in range(3):\n",
    "        tmp = np.zeros(25)\n",
    "        tmp[v1_vert_ind[j]] = 1\n",
    "        v1_vert.append(tmp)\n",
    "    v1_vert_ind += 1\n",
    "#print_layer(v1_vert)\n",
    "\n",
    "v1 = np.concatenate((v1_hor, v1_vert))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "###########################\n",
    "#full height straight lines - V2\n",
    "###########################\n",
    "v1_bar_ind = np.array(range(0,25,5))\n",
    "#print(v1_bar_ind)\n",
    "\n",
    "v1_bar = []\n",
    "\n",
    "for i in range(5):\n",
    "    tmp = np.zeros(25)\n",
    "    tmp[v1_bar_ind] = 1\n",
    "    v1_bar.append(tmp)\n",
    "    v1_bar_ind += 1\n",
    "#print_layer(v1_bar)\n",
    "\n",
    "###########################\n",
    "#corners - V2\n",
    "###########################\n",
    "v2_top_r_ind = [0,1,2,7,12]\n",
    "v2_top_l_ind = [0,1,2,5,10]\n",
    "v2_bot_r_ind = [2,7,10,11,12]\n",
    "v2_bot_l_ind = [0,5,10,11,12]\n",
    "\n",
    "def gen_corners(inds):\n",
    "    matrices = []\n",
    "    inds = np.array(inds)\n",
    "    for i in range(2):\n",
    "        for j in range(3):\n",
    "            tmp = np.zeros(25)\n",
    "            tmp[inds] = 1\n",
    "            matrices.append(tmp)\n",
    "            inds += 1\n",
    "        inds += 7\n",
    "    return matrices\n",
    "\n",
    "v2_top_r = gen_corners(v2_top_r_ind)\n",
    "v2_top_l = gen_corners(v2_top_l_ind)\n",
    "v2_bot_r = gen_corners(v2_bot_r_ind)\n",
    "v2_bot_l = gen_corners(v2_bot_l_ind)\n",
    "\n",
    "#print_layer(v2_top_r)\n",
    "#print_layer(v2_top_l)\n",
    "#print_layer(v2_bot_r)\n",
    "#print_layer(v2_bot_l)\n",
    "v2 = np.concatenate((v2_top_r,v2_top_l,v2_bot_r,v2_bot_l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "###########################\n",
    "#define number templates - V4\n",
    "###########################\n",
    "\n",
    "one_c = np.zeros(25)\n",
    "one_c[np.array(range(0,25,5))+2] = 1\n",
    "one_c = one_c.reshape(5,5)\n",
    "two_c = [[ 0.,  1.,  1.,  1.,  0.],\n",
    "         [ 0.,  0.,  0.,  1.,  0.],\n",
    "         [ 0.,  1.,  1.,  1.,  0.],\n",
    "         [ 0.,  1.,  0.,  0.,  0.],\n",
    "         [ 0.,  1.,  1.,  1.,  0.]]\n",
    "\n",
    "three_c = [[ 0.,  1.,  1.,  1.,  0.],\n",
    "           [ 0.,  0.,  0.,  1.,  0.],\n",
    "           [ 0.,  0.,  1.,  1.,  0.],\n",
    "           [ 0.,  0.,  0.,  1.,  0.],\n",
    "           [ 0.,  1.,  1.,  1.,  0.]]\n",
    "\n",
    "four_c = [[ 0.,  1.,  0.,  1.,  0.],\n",
    "          [ 0.,  1.,  0.,  1.,  0.],\n",
    "          [ 0.,  1.,  1.,  1.,  0.],\n",
    "          [ 0.,  0.,  0.,  1.,  0.],\n",
    "          [ 0.,  0.,  0.,  1.,  0.]]\n",
    "\n",
    "five_c = [[ 0.,  1.,  1.,  1.,  0.],\n",
    "          [ 0.,  1.,  0.,  0.,  0.],\n",
    "          [ 0.,  1.,  1.,  1.,  0.],\n",
    "          [ 0.,  0.,  0.,  1.,  0.],\n",
    "          [ 0.,  1.,  1.,  1.,  0.]]\n",
    "\n",
    "six_c = [[ 0.,  1.,  0.,  0.,  0.],\n",
    "         [ 0.,  1.,  0.,  0.,  0.],\n",
    "         [ 0.,  1.,  1.,  1.,  0.],\n",
    "         [ 0.,  1.,  0.,  1.,  0.],\n",
    "         [ 0.,  1.,  1.,  1.,  0.]]\n",
    "\n",
    "seven_c = [[ 0.,  1.,  1.,  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.]]\n",
    "\n",
    "eight_c = [[ 0.,  1.,  1.,  1.,  0.],\n",
    "           [ 0.,  1.,  0.,  1.,  0.],\n",
    "           [ 0.,  1.,  1.,  1.,  0.],\n",
    "           [ 0.,  1.,  0.,  1.,  0.],\n",
    "           [ 0.,  1.,  1.,  1.,  0.]]\n",
    "\n",
    "nine_c = [[ 0.,  1.,  1.,  1.,  0.],\n",
    "          [ 0.,  1.,  0.,  1.,  0.],\n",
    "          [ 0.,  1.,  1.,  1.,  0.],\n",
    "          [ 0.,  0.,  0.,  1.,  0.],\n",
    "          [ 0.,  0.,  0.,  1.,  0.]]\n",
    "\n",
    "zero_c = [[ 0.,  1.,  1.,  1.,  0.],\n",
    "          [ 0.,  1.,  0.,  1.,  0.],\n",
    "          [ 0.,  1.,  0.,  1.,  0.],\n",
    "          [ 0.,  1.,  0.,  1.,  0.],\n",
    "          [ 0.,  1.,  1.,  1.,  0.]]\n",
    "\n",
    "one_ll = np.array(np.roll(one_c.reshape(1,25), -2))\n",
    "one_l = np.array(np.roll(one_c.reshape(1,25), -1))\n",
    "one_r = np.array(np.roll(one_c.reshape(1,25), 1))\n",
    "one_rr = np.array(np.roll(one_c.reshape(1,25), 2))\n",
    "one_c = one_c.reshape(1,25)\n",
    "one = np.concatenate((one_ll, one_l, one_c, one_r, one_rr))\n",
    "\n",
    "def gen_num_mat(num):\n",
    "    return np.squeeze(\n",
    "            np.array(\n",
    "                [np.roll(num.reshape(1,25), -1), num.reshape(1,25), np.roll(num.reshape(1,25), 1)]\n",
    "            )\n",
    "           )\n",
    "\n",
    "two = gen_num_mat(np.array(two_c))\n",
    "three = gen_num_mat(np.array(three_c))\n",
    "four = gen_num_mat(np.array(four_c))\n",
    "five = gen_num_mat(np.array(five_c))\n",
    "six = gen_num_mat(np.array(six_c))\n",
    "seven = gen_num_mat(np.array(seven_c))\n",
    "eight = gen_num_mat(np.array(eight_c))\n",
    "nine = gen_num_mat(np.array(nine_c))\n",
    "zero = gen_num_mat(np.array(zero_c))\n",
    "\n",
    "v4 = np.concatenate((one, two, three, four, five, six, seven, eight, nine, zero))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "###########################\n",
    "#Number classification - IT\n",
    "###########################\n",
    "# Just get inputs from V4 and map to digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "66\n",
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      "67\n",
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      "68\n",
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      "69\n",
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      "70\n",
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      "71\n",
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      "72\n",
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      "74\n",
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      "75\n",
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      "76\n",
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      "77\n",
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      "78\n",
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      "79\n",
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      " [0. 0. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
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      "80\n",
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      " [1. 0. 0. 0. 0.]\n",
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      "81\n",
      "[[0. 1. 0. 0. 0.]\n",
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      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
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      "82\n",
      "[[0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
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      "83\n",
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      "84\n",
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      " [0. 0. 0. 0. 1.]\n",
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      "85\n",
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      " [0. 0. 1. 0. 0.]\n",
      " [1. 1. 1. 0. 0.]\n",
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      "86\n",
      "[[0. 1. 1. 1. 0.]\n",
      " [0. 0. 0. 1. 0.]\n",
      " [0. 1. 1. 1. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
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      "87\n",
      "[[0. 0. 1. 1. 1.]\n",
      " [0. 0. 0. 0. 1.]\n",
      " [0. 0. 1. 1. 1.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 1. 1.]]\n",
      "88\n",
      "[[1. 1. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 1. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
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      "89\n",
      "[[0. 1. 1. 1. 0.]\n",
      " [0. 0. 0. 1. 0.]\n",
      " [0. 0. 1. 1. 0.]\n",
      " [0. 0. 0. 1. 0.]\n",
      " [0. 1. 1. 1. 0.]]\n",
      "90\n",
      "[[0. 0. 1. 1. 1.]\n",
      " [0. 0. 0. 0. 1.]\n",
      " [0. 0. 0. 1. 1.]\n",
      " [0. 0. 0. 0. 1.]\n",
      " [0. 0. 1. 1. 1.]]\n",
      "91\n",
      "[[1. 0. 1. 0. 0.]\n",
      " [1. 0. 1. 0. 0.]\n",
      " [1. 1. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]]\n",
      "92\n",
      "[[0. 1. 0. 1. 0.]\n",
      " [0. 1. 0. 1. 0.]\n",
      " [0. 1. 1. 1. 0.]\n",
      " [0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 1. 0.]]\n",
      "93\n",
      "[[0. 0. 1. 0. 1.]\n",
      " [0. 0. 1. 0. 1.]\n",
      " [0. 0. 1. 1. 1.]\n",
      " [0. 0. 0. 0. 1.]\n",
      " [0. 0. 0. 0. 1.]]\n",
      "94\n",
      "[[1. 1. 1. 0. 0.]\n",
      " [1. 0. 0. 0. 0.]\n",
      " [1. 1. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [1. 1. 1. 0. 0.]]\n",
      "95\n",
      "[[0. 1. 1. 1. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 1. 1. 0.]\n",
      " [0. 0. 0. 1. 0.]\n",
      " [0. 1. 1. 1. 0.]]\n",
      "96\n",
      "[[0. 0. 1. 1. 1.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 1. 1.]\n",
      " [0. 0. 0. 0. 1.]\n",
      " [0. 0. 1. 1. 1.]]\n",
      "97\n",
      "[[1. 0. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0.]\n",
      " [1. 1. 1. 0. 0.]\n",
      " [1. 0. 1. 0. 0.]\n",
      " [1. 1. 1. 0. 0.]]\n",
      "98\n",
      "[[0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 1. 1. 0.]\n",
      " [0. 1. 0. 1. 0.]\n",
      " [0. 1. 1. 1. 0.]]\n",
      "99\n",
      "[[0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 1. 1.]\n",
      " [0. 0. 1. 0. 1.]\n",
      " [0. 0. 1. 1. 1.]]\n",
      "100\n",
      "[[1. 1. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]]\n",
      "101\n",
      "[[0. 1. 1. 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.]]\n",
      "102\n",
      "[[0. 0. 1. 1. 1.]\n",
      " [0. 0. 0. 0. 1.]\n",
      " [0. 0. 0. 0. 1.]\n",
      " [0. 0. 0. 0. 1.]\n",
      " [0. 0. 0. 0. 1.]]\n",
      "103\n",
      "[[1. 1. 1. 0. 0.]\n",
      " [1. 0. 1. 0. 0.]\n",
      " [1. 1. 1. 0. 0.]\n",
      " [1. 0. 1. 0. 0.]\n",
      " [1. 1. 1. 0. 0.]]\n",
      "104\n",
      "[[0. 1. 1. 1. 0.]\n",
      " [0. 1. 0. 1. 0.]\n",
      " [0. 1. 1. 1. 0.]\n",
      " [0. 1. 0. 1. 0.]\n",
      " [0. 1. 1. 1. 0.]]\n",
      "105\n",
      "[[0. 0. 1. 1. 1.]\n",
      " [0. 0. 1. 0. 1.]\n",
      " [0. 0. 1. 1. 1.]\n",
      " [0. 0. 1. 0. 1.]\n",
      " [0. 0. 1. 1. 1.]]\n",
      "106\n",
      "[[1. 1. 1. 0. 0.]\n",
      " [1. 0. 1. 0. 0.]\n",
      " [1. 1. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]]\n",
      "107\n",
      "[[0. 1. 1. 1. 0.]\n",
      " [0. 1. 0. 1. 0.]\n",
      " [0. 1. 1. 1. 0.]\n",
      " [0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 1. 0.]]\n",
      "108\n",
      "[[0. 0. 1. 1. 1.]\n",
      " [0. 0. 1. 0. 1.]\n",
      " [0. 0. 1. 1. 1.]\n",
      " [0. 0. 0. 0. 1.]\n",
      " [0. 0. 0. 0. 1.]]\n",
      "109\n",
      "[[1. 1. 1. 0. 0.]\n",
      " [1. 0. 1. 0. 0.]\n",
      " [1. 0. 1. 0. 0.]\n",
      " [1. 0. 1. 0. 0.]\n",
      " [1. 1. 1. 0. 0.]]\n",
      "110\n",
      "[[0. 1. 1. 1. 0.]\n",
      " [0. 1. 0. 1. 0.]\n",
      " [0. 1. 0. 1. 0.]\n",
      " [0. 1. 0. 1. 0.]\n",
      " [0. 1. 1. 1. 0.]]\n",
      "111\n",
      "[[0. 0. 1. 1. 1.]\n",
      " [0. 0. 1. 0. 1.]\n",
      " [0. 0. 1. 0. 1.]\n",
      " [0. 0. 1. 0. 1.]\n",
      " [0. 0. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "###########################\n",
    "#Print the entire set of feature detectors\n",
    "###########################\n",
    "\n",
    "all_features = np.concatenate((retina, v1, v2, v4))\n",
    "\n",
    "print_layer(all_features)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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